| --- |
| license: cc-by-nc-4.0 |
| --- |
| # README |
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|
| ## Dataset Structure |
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|
| ```text |
| HCFSLN |
| ├── Code |
| ├── Dataset |
| ├── Feature_Extraction |
| └── readme.txt |
| ``` |
|
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| The dataset is organized into folders based on feature type. |
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| Within the Dataset folder: |
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| Dataset --> M2AD --> Audio, EDA, PPG, Video, Label.csv |
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| - 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. |
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| All feature files and labels should be placed in their respective folders before running the notebooks. |
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| Each CSV file follows a standardized naming convention, for example: |
| P7_audio_features.csv |
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| 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. |
|
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| ## Feature Extraction |
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| Use the provided Jupyter notebooks for feature extraction: |
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| - 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. |
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| ## Code |
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| The Code folder contains: |
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| - Baseline models and our proposed model implementations. |
| - Utility scripts (utils.py), runner (runner.py), and configuration (config.py). |
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| config.py is used to set parameters and select feature lists. Each model .py file also initializes its own parameters. |
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| Baseline models include: |
| cplnet.py, emotracer.py, eshms.py, hyperboliccontrastive.py, nohub.py, and tamnet.py. |
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| All plotting and evaluation code is in utils.py. The main execution is handled by runner.py. |
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| ### Important Paths to Set in config.py |
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| 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' |
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| ## Ablation Studies |
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| For ablation experiments, set the dataset path in hcfsln_ablation.py and run it. All ablation experiments are included there. |
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| ## Usage Guidelines |
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| - 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. |
