The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ParserError
Message: Error tokenizing data. C error: Expected 1 fields in line 8, saw 14
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
return self.get_chunk()
~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
return self.read(nrows=size)
~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
) = self._engine.read( # type: ignore[attr-defined]
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
nrows
^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 8, saw 14Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
README
Dataset Structure
HCFSLN
├── Code
├── Dataset
├── Feature_Extraction
└── readme.txt
The dataset is organized into folders based on feature type.
Within the Dataset folder:
Dataset --> M2AD --> Audio, EDA, PPG, Video, Label.csv
- Audio: Contains spectral, temporal, and frequency-based features extracted using
librosa. - EDA: Contains skin conductance levels (SCL) and peaks.
- PPG: Includes clean PPG signals processed using
NeuroKit. - Video: Includes facial expression features such as Action Units (AUs), head pose, and eye gaze, extracted with
OpenFace. - Label.csv: Provides participant identifiers and experimental conditions.
All feature files and labels should be placed in their respective folders before running the notebooks.
Each CSV file follows a standardized naming convention, for example: P7_audio_features.csv
Here, P7 represents a participant identifier, ensuring easy correlation across modalities. For instance:
- P7_audio_features.csv contains extracted audio features for participant P7.
- P7_video_features.csv contains facial expression features for participant P7.
- Similarly for P7_eda_features.csv and P7_ppg_features.csv.
Feature Extraction
Use the provided Jupyter notebooks for feature extraction:
- Audio_Features_Extraction.ipynb: Extracts audio features using
librosa. - EDA_Cleaning_Components.ipynb: Processes EDA signals using
NeuroKit. - PPG_Cleaning.ipynb: Extracts PPG signals.
Video features are extracted using OpenFace. Provide the full dataset path, and it will extract raw features from the raw video files. OpenFace repository: https://github.com/TadasBaltrusaitis/OpenFace
Ensure extracted files are saved in their designated folders inside the Feature_Extraction directory.
Code
The Code folder contains:
- Baseline models and our proposed model implementations.
- Utility scripts (utils.py), runner (runner.py), and configuration (config.py).
config.py is used to set parameters and select feature lists. Each model .py file also initializes its own parameters.
Baseline models include: cplnet.py, emotracer.py, eshms.py, hyperboliccontrastive.py, nohub.py, and tamnet.py.
All plotting and evaluation code is in utils.py. The main execution is handled by runner.py.
Important Paths to Set in config.py
ROOT_DATA = "" # Root directory where dataset folders (e.g., D1, D2, D3) are stored RESULTS_ROOT = "results" # Directory for detailed results per run (e.g., CSVs, t-SNE plots) FINAL_ROOT = "final" # Directory where aggregated final results are saved
HCFSLN Model (Our Model)
Run hcfsln.py separately to store results. Provide paths to each modality folder and label CSV separately, e.g.:
audio_folder = '/home/Audio' eda_folder = '/home/EDA' ppg_folder = '/home/PPG' video_folder = '/home/Video' labels_path = '/home/Label.csv'
Ablation Studies
For ablation experiments, set the dataset path in hcfsln_ablation.py and run it. All ablation experiments are included there.
Usage Guidelines
- Install required dependencies (librosa, NeuroKit) before processing.
- Place extracted features and labels in the correct folder structure before running models.
- Please cite relevant tools and datasets when using this work.
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