smbu_de_cl: Identifiable EEG Embeddings by Contrastive Learning from Differential Entropy Features

This repository is the official implementation of the paper: 'Identifiable EEG Embeddings by Contrastive Learning from Differential Entropy Features.'

The following sections guide you through cloning the project, preparing the dataset, setting up the environment, and running the code.

Clone the Project

Clone the repository using the following command:

git clone https://github.com/liangfengsid/deConstrativeLearning.git
cd deContrastiveLearning

Dataset Preparation

Place the SEED dataset into the ./data directory. This dataset is used for training and evaluating the models.

Environment Setup

Create a virtual environment and install the required dependencies with the following commands:

conda create -n de_cl python=3.11 pytorch=2.0.0
conda activate de_cl
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

This sets up an environment with the necessary libraries and packages for the project.

Running the Code

Data Preprocessing

Execute the following command for data preprocessing, including feature extraction and dataset splitting:

python main.py preprocess config.yaml

Model Training

Train the model to obtain latent embeddings with:

python main.py train config.yaml

If you are training after preprocessing, you need to change the value of split_data_path in the config.yaml file to the directory where your data is saved. Otherwise, our provided sample data will be used for training.

SVM Decoding

Decode using the SVM model on different embeddings and perform classification tasks with:

python main.py svm_decode config.yaml

KNN Decoding

Decode using the KNN model on different embeddings and perform classification tasks with:

python main.py knn_decode config.yaml

Refer to the configurations in config.yaml to customize the training and other processes.

Support & Contribution

Feel free to open issues or contribute to the project. Your feedback is highly appreciated.