| # 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. | |