This repository contains the implementation of InceptionResNet for Steady-State Visual Evoked Potential (SSVEP) based Brain-Computer Interface (BCI). The proposed model achieves state-of-the-art performance on several benchmark datasets.
- Clone the repository:
git clone https://github.com/Shengwei-Peng/InceptionResNet-SSVEP-BCI.git
- Navigate to the project directory:
cd InceptionResNet-SSVEP-BCI - Install the required packages:
pip install -r requirements.txt
To run the project, use the following command:
python main.pyThey dataset we used in our experiment is Benchmark dataset by Yijun Wang, Xiaogang Chen, Xiaorong Gao, Shangkai Gao.
The performance of the InceptionResNet model for SSVEP-based BCI was evaluated against two baseline models: InceptionSSVEP and ResNet. The experimental results show that the InceptionResNet model outperforms the baseline models in terms of classification accuracy. The following chart illustrates the accuracies achieved by InceptionSSVEP, ResNet, and InceptionResNet models across 35 subjects:
Contributions are welcome! Please follow these steps to contribute:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch). - Commit your changes (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature-branch). - Create a new Pull Request.
This project is licensed under the MIT License. See the LICENSE file for details.
For any questions or inquiries, please contact m11207330@mail.ntust.edu.tw
We would like to express our gratitude to the following repositories for their contributions and inspiration:
- torchsignal: A signal processing library for PyTorch. This library provided essential functions and algorithms that greatly aided in the development of our signal processing module.