Restore naturally ordered Foundational Analysis case paths

foundational_analysis/cases/case10/case10_01.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_01.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/core/ISRUC_01.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.306056,
+                    "tolerance": 0.0153028
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "C4-F4"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_02.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_02.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/core/ISRUC_02.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.431466,
+                    "tolerance": 0.021573300000000004
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "C4-F4"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_03.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_03.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/core/ISRUC_03.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.304628,
+                    "tolerance": 0.0152314
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "C4-F4"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_04.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_04.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/core/ISRUC_04.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.291297,
+                    "tolerance": 0.014564849999999999
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "C3-F3"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_05.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "ISRUC_05.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "ISRUC"
+    },
+    "agent_input": {
+        "data_path": "data/core/ISRUC_05.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.354238,
+                    "tolerance": 0.0177119
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "C3-F3"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_06.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "BCIC2020-3_01.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "BCIC2020-3"
+    },
+    "agent_input": {
+        "data_path": "data/core/BCIC2020-3_01.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.466999,
+                    "tolerance": 0.02334995
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "P3-P5"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_07.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "BCIC2020-3_02.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "BCIC2020-3"
+    },
+    "agent_input": {
+        "data_path": "data/core/BCIC2020-3_02.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.334351,
+                    "tolerance": 0.01671755
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "FC4-FC6"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_08.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "BCIC2020-3_03.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "BCIC2020-3"
+    },
+    "agent_input": {
+        "data_path": "data/core/BCIC2020-3_03.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.446754,
+                    "tolerance": 0.022337700000000002
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "AF7-F5"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_09.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "BCIC2020-3_04.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "BCIC2020-3"
+    },
+    "agent_input": {
+        "data_path": "data/core/BCIC2020-3_04.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.514571,
+                    "tolerance": 0.025728550000000003
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F7-FT7"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_10.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "BCIC2020-3_05.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "BCIC2020-3"
+    },
+    "agent_input": {
+        "data_path": "data/core/BCIC2020-3_05.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.490313,
+                    "tolerance": 0.02451565
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "P1-P3"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_11.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "SEED-V-01.cnt",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "SEED-V"
+    },
+    "agent_input": {
+        "data_path": "data/core/SEED-V-01.cnt",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.352766,
+                    "tolerance": 0.017638300000000003
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "PO6-PO8"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_12.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "SEED-V-02.cnt",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "SEED-V"
+    },
+    "agent_input": {
+        "data_path": "data/core/SEED-V-02.cnt",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.347057,
+                    "tolerance": 0.01735285
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "PO3-PO5"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_13.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "SEED-V-03.cnt",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "SEED-V"
+    },
+    "agent_input": {
+        "data_path": "data/core/SEED-V-03.cnt",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.366734,
+                    "tolerance": 0.0183367
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "P5-PO5"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_14.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "SEED-V-04.cnt",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "SEED-V"
+    },
+    "agent_input": {
+        "data_path": "data/core/SEED-V-04.cnt",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.474302,
+                    "tolerance": 0.023715100000000003
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "O2-PO4"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_15.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "SEED-V-05.cnt",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "SEED-V"
+    },
+    "agent_input": {
+        "data_path": "data/core/SEED-V-05.cnt",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.42731,
+                    "tolerance": 0.021365500000000003
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "P4-P6"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_16.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "Mumtaz2016_01.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "Mumtaz2016"
+    },
+    "agent_input": {
+        "data_path": "data/core/Mumtaz2016_01.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.368373,
+                    "tolerance": 0.01841865
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "FP1-FP2"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_17.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "Mumtaz2016_02.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "Mumtaz2016"
+    },
+    "agent_input": {
+        "data_path": "data/core/Mumtaz2016_02.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.495031,
+                    "tolerance": 0.02475155
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F3-FP1"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_18.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "Mumtaz2016_03.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "Mumtaz2016"
+    },
+    "agent_input": {
+        "data_path": "data/core/Mumtaz2016_03.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.608099,
+                    "tolerance": 0.03040495
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F4-FZ"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_19.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "Mumtaz2016_04.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "Mumtaz2016"
+    },
+    "agent_input": {
+        "data_path": "data/core/Mumtaz2016_04.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.404867,
+                    "tolerance": 0.02024335
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "FP1-FZ"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_20.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "Mumtaz2016_05.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "Mumtaz2016"
+    },
+    "agent_input": {
+        "data_path": "data/core/Mumtaz2016_05.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.479867,
+                    "tolerance": 0.02399335
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F3-FP1"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_21.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "MentalArithmetic_01.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "MentalArithmetic"
+    },
+    "agent_input": {
+        "data_path": "data/core/MentalArithmetic_01.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.463025,
+                    "tolerance": 0.02315125
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F4-FZ"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_22.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "MentalArithmetic_02.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "MentalArithmetic"
+    },
+    "agent_input": {
+        "data_path": "data/core/MentalArithmetic_02.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.577661,
+                    "tolerance": 0.02888305
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F7-FP1"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_23.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "MentalArithmetic_03.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "MentalArithmetic"
+    },
+    "agent_input": {
+        "data_path": "data/core/MentalArithmetic_03.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.478912,
+                    "tolerance": 0.0239456
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "C4-CZ"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_24.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "MentalArithmetic_04.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "MentalArithmetic"
+    },
+    "agent_input": {
+        "data_path": "data/core/MentalArithmetic_04.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.551636,
+                    "tolerance": 0.027581800000000004
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F4-FZ"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case10/case10_25.json

@@ -0,0 +1,36 @@
+{
+    "meta_info": {
+        "case_id": "MentalArithmetic_05.edf",
+        "bench_subset": "NeuroBench-Core",
+        "difficult": 1.5,
+        "original_dataset": "MentalArithmetic"
+    },
+    "agent_input": {
+        "data_path": "data/core/MentalArithmetic_05.edf",
+        "instruction": "Please select EEG channels from the raw signal, apply an 8-13Hz alpha-band FIR (linear-phase) bandpass filter, and then use the first 30 seconds of the filtered signal to compute inter-channel correlations using the Pearson correlation coefficient. Then report: (1) the global synchronization value defined as the mean absolute correlation across all channel pairs, and (2) the single channel pair with the highest correlation coefficient."
+    },
+    "eval_config": {
+        "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) global synchronization value in alpha band\n2) highest-correlation channel pair\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"global_sync\" and \"top_pair\".\n5. \"global_sync\" must be a FLOAT.\n6. To extract \"top_pair\", first identify the two complete source channel labels. A referenced label such as F4-A1 is one complete channel label, and its hyphen must not be treated as the pair separator.\n7. For each source channel separately, uppercase it and remove one terminal reference suffix if present: -A1, -A2, -LE, -REF, -M1, -M2, or -AVG. Never output a reference electrode (A1/A2/LE/REF/M1/M2/AVG) as a pair member.\n8. Convert legacy aliases after removing the reference suffix: T3->T7, T4->T8, T5->P7, T6->P8.\n9. Sort the two normalized channel labels in ascending lexicographic order, then join them with exactly one hyphen using format \"CH1-CH2\" (uppercase, no spaces). If the report already uses a canonical pair such as F4-C4, normalize it to C4-F4. Each final label may contain only uppercase A-Z letters and digits; mixed-case forms such as Fp1 or Fp2 are forbidden and must become FP1 or FP2.\n10. Required examples: \"F4-A1 and C4-A1\" -> \"C4-F4\"; \"F4-C4\" -> \"C4-F4\"; \"T4-A1 and T3-A2\" -> \"T7-T8\"; \"Fp2 and Fp1\" -> \"FP1-FP2\".\n11. If the pair cannot be resolved into exactly two non-reference channels, set \"top_pair\" to null; do not guess. If global_sync is missing, set only global_sync to null.\n\n### OUTPUT TEMPLATE\n{\"global_sync\": <float|null>, \"top_pair\": <string|null>}",
+        "metrics": [
+            {
+                "metric_id": "alpha_global_sync_accuracy",
+                "type": "numeric_check",
+                "target_key": "global_sync",
+                "weight": 50,
+                "params": {
+                    "gt_value": 0.479741,
+                    "tolerance": 0.02398705
+                }
+            },
+            {
+                "metric_id": "highest_corr_pair_accuracy",
+                "type": "categorical_check",
+                "target_key": "top_pair",
+                "weight": 50,
+                "params": {
+                    "gt_value": "F8-FP2"
+                }
+            }
+        ]
+    }
+}

foundational_analysis/cases/case11/case11_01.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "ISRUC_01.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "ISRUC"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/ISRUC_01.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the 110-120 minute segment and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_02.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "ISRUC_02.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "ISRUC"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/ISRUC_02.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the 110-120 minute segment and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_03.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "ISRUC_03.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "ISRUC"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/ISRUC_03.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the 110-120 minute segment and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_04.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "ISRUC_04.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "ISRUC"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/ISRUC_04.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the 110-120 minute segment and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_05.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "ISRUC_05.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "ISRUC"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/ISRUC_05.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the 110-120 minute segment and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_06.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "BCIC2020-3_01.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "BCIC2020-3"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/BCIC2020-3_01.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "gamma"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_07.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "BCIC2020-3_02.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "BCIC2020-3"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/BCIC2020-3_02.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "gamma"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_08.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "BCIC2020-3_03.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "BCIC2020-3"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/BCIC2020-3_03.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "gamma"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_09.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "BCIC2020-3_04.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "BCIC2020-3"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/BCIC2020-3_04.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "gamma"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_10.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "BCIC2020-3_05.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "BCIC2020-3"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/BCIC2020-3_05.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "gamma"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_11.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "SEED-V-01.cnt",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "SEED-V"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/SEED-V-01.cnt",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_12.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "SEED-V-02.cnt",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "SEED-V"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/SEED-V-02.cnt",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_13.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "SEED-V-03.cnt",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "SEED-V"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/SEED-V-03.cnt",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_14.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "SEED-V-04.cnt",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "SEED-V"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/SEED-V-04.cnt",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "beta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_15.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "SEED-V-05.cnt",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "SEED-V"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/SEED-V-05.cnt",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_16.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "Mumtaz2016_01.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "Mumtaz2016"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/Mumtaz2016_01.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_17.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "Mumtaz2016_02.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "Mumtaz2016"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/Mumtaz2016_02.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_18.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "Mumtaz2016_03.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "Mumtaz2016"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/Mumtaz2016_03.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_19.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "Mumtaz2016_04.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "Mumtaz2016"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/Mumtaz2016_04.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_20.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "Mumtaz2016_05.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "Mumtaz2016"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/Mumtaz2016_05.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "theta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_21.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "MentalArithmetic_01.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "MentalArithmetic"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/MentalArithmetic_01.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_22.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "MentalArithmetic_02.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "MentalArithmetic"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/MentalArithmetic_02.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_23.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "MentalArithmetic_03.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "MentalArithmetic"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/MentalArithmetic_03.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_24.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "MentalArithmetic_04.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "MentalArithmetic"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/MentalArithmetic_04.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        }
+                                    ]
+                    }
+}

foundational_analysis/cases/case11/case11_25.json

@@ -0,0 +1,35 @@
+{
+    "meta_info":  {
+                      "case_id":  "MentalArithmetic_05.edf",
+                      "bench_subset":  "NeuroBench-Core",
+                      "difficult": 1.0,
+                      "original_dataset":  "MentalArithmetic"
+                  },
+    "agent_input":  {
+                        "data_path":  "data/core/MentalArithmetic_05.edf",
+                        "instruction":  "Please determine the dominant and secondary dominant EEG frequency bands in the first 60 seconds and return two discrete band names. Return only band names chosen from: alpha, beta, delta, gamma, theta."
+                    },
+    "eval_config":  {
+                        "parser_prompt":  "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent report:\n1) dominant EEG band\n2) secondary dominant EEG band\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"dominant_band\" and \"secondary_band\".\n5. Values must be lowercase strings and each must be one of: [alpha, beta, delta, gamma, theta].\n6. If one value is missing, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": \u003cstring|null\u003e, \"secondary_band\": \u003cstring|null\u003e}",
+                        "metrics":  [
+                                        {
+                                            "metric_id":  "dominant_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "dominant_band",
+                                            "weight":  70,
+                                            "params":  {
+                                                           "gt_value":  "delta"
+                                                       }
+                                        },
+                                        {
+                                            "metric_id":  "secondary_band_accuracy",
+                                            "type":  "categorical_check",
+                                            "target_key":  "secondary_band",
+                                            "weight":  30,
+                                            "params":  {
+                                                           "gt_value":  "alpha"
+                                                       }
+                                        }
+                                    ]
+                    }
+}