📌EDAN (Please Star this project and cite our paper if you use this code)

🍊 Overview: Electrode Domain Adaptation Network: Minimizing the Difference Across Electrodes in Single-Source to Single-Target Motor Imagery Classification

🍊 This project is based on our recent publication. You can access the original paper here: [Link: Electrode domain adaptation network].

🍊 In this study, to deal with the electrode data distribution difference problem, a novel electrode domain adaptation network (EDAN) is proposed, aiming to improve classification accuracy and enhance model robustness.

How to Run

This application is designed to run in a PyTorch environment. To execute main.py, which is the entry point of the program, follow the steps outlined below.

Prerequisites

Before running the application, ensure that you have the following prerequisites installed:

1. Python: The code is tested with Python 3.8. It should be compatible with most Python 3.x versions.

2. PyTorch: This project requires PyTorch. If you haven't installed PyTorch yet, you can find installation instructions on the official PyTorch website.

Running main.py

Once you have the environment set up, you can run main.py by following these steps:

1. Open your command line interface (CLI).

2. Navigate to the directory where main.py is located.

3. Select the network. You can select the correspoding testing model such as 'EDAN/IA_EDAN/IE_EDAN/EEGNet/DDC/DeepCoral.etc' with the setting in 'main.py':

    Net_number = 'EDAN' # Choose EDAN model

4. Run the script with the command:

   python main.py