# 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.