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S-CEReBrO

PyTorch
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scerebro
eeg
biosignal
foundation-model
self-supervised
masked-modeling
windowed-alternating-attention
neuroscience
Eval Results (legacy)
Model card Files
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@@ -38,34 +38,114 @@ model-index:
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  results:
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  - task:
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  type: time-series-classification
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- name: Seizure Detection
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  dataset:
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  type: CHB-MIT
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- name: CHB-MIT
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  metrics:
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  - type: roc_auc
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- value: 87.45
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- name: AUROC
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  - task:
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  type: time-series-classification
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- name: Motor Imagery Classification
 
 
 
 
 
 
 
 
 
 
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  dataset:
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  type: PhysioNet-MI
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- name: PhysioNet-MI
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  metrics:
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  - type: f1
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- value: 60.93
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  name: Weighted F1
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  - task:
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  type: time-series-classification
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- name: Abnormal Classification
 
 
 
 
 
 
 
 
 
 
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  dataset:
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  type: TUAB
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- name: TUAB
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  metrics:
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  - type: roc_auc
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- value: 89.30
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- name: AUROC
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  <div align="center">
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  <h1>S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring</h1>
 
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  results:
39
  - task:
40
  type: time-series-classification
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+ name: Seizure Detection
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  dataset:
43
  type: CHB-MIT
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+ name: CHB-MIT
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  metrics:
46
  - type: roc_auc
47
+ value: 87.45
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+ name: AUROC
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  - task:
50
  type: time-series-classification
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+ name: Seizure Detection
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+ dataset:
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+ type: Neonate
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+ name: Neonate
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+ metrics:
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+ - type: roc_auc
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+ value: 85.76
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+ name: AUROC
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+ - task:
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+ type: time-series-classification
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+ name: Motor Imagery Classification
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  dataset:
63
  type: PhysioNet-MI
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+ name: PhysioNet-MI
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  metrics:
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  - type: f1
67
+ value: 60.93
68
  name: Weighted F1
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  - task:
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  type: time-series-classification
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+ name: Motor Imagery Classification
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+ dataset:
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+ type: SHU-MI
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+ name: SHU-MI
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+ metrics:
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+ - type: roc_auc
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+ value: 66.48
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+ name: AUROC
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+ - task:
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+ type: time-series-classification
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+ name: Abnormal Classification
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  dataset:
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  type: TUAB
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+ name: TUAB
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  metrics:
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  - type: roc_auc
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+ value: 89.30
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+ name: AUROC
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+ - task:
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+ type: time-series-classification
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+ name: Mental Workload Classification
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+ dataset:
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+ type: STEW
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+ name: STEW
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+ metrics:
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+ - type: f1
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+ value: 52.90
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+ name: Weighted F1
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+ - task:
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+ type: time-series-regression
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+ name: Vigilance Estimation
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+ dataset:
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+ type: SEED-VIG
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+ name: SEED-VIG
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+ metrics:
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+ - type: nrmse
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+ value: 0.93
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+ name: NRMSE
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+ - task:
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+ type: time-series-classification
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+ name: Sleep Stage Classification
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+ dataset:
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+ type: ISRUC
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+ name: ISRUC
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+ metrics:
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+ - type: f1
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+ value: 78.23
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+ name: Weighted F1
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+ - task:
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+ type: time-series-classification
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+ name: Mental Disorder Diagnosis
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+ dataset:
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+ type: Mumtaz
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+ name: Mumtaz
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+ metrics:
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+ - type: roc_auc
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+ value: 98.23
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+ name: AUROC
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+ - task:
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+ type: time-series-classification
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+ name: Mental Stress Detection
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+ dataset:
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+ type: MentalArithmetic
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+ name: MentalArithmetic
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+ metrics:
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+ - type: roc_auc
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+ value: 68.97
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+ name: AUROC
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+ - task:
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+ type: time-series-classification
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+ name: Emotion Recognition
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+ dataset:
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+ type: SEED-V
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+ name: SEED-V
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+ metrics:
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+ - type: f1
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+ value: 28.16
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+ name: Weighted F1
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  ---
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  <div align="center">
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  <h1>S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring</h1>