Welcome! This repo studies data augmentation approaches for EEG analysis, specifically for brain-computer interface (BCI) applications.
The official implementation of Channel Reflection (CR), of paper Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces (Neural Networks, 2024)
Install dependencies based on environment.yml file.
We have already provided the processed data of BNCI2014001 of MOABB (details see paper) under ./data/ To use other datasets, follow a similar format.
Run
python within_baseline.pyor
python within_CR.pyfor comparison of with or without CR results for within-subject classification.
Results are shown in the following table:
| Approach | 5 | 10 | 15 | 20 | 25 | 30 | 35 | 40 | 45 | Avg. |
|---|---|---|---|---|---|---|---|---|---|---|
| Baseline | 52.49 | 65.77 | 67.54 | 67.63 | 68.20 | 70.50 | 70.87 | 74.48 | 74.07 | 67.95 |
| CR | 61.36 | 64.34 | 69.59 | 70.73 | 72.81 | 72.22 | 75.08 | 77.60 | 75.93 | 71.07 |
Please use Issues for any questions regarding the code, or contact me at [email protected] for any questions regarding the paper.
If you find this repo helpful, please cite our work:
@article{Wang2024CR,
title={Channel Reflection: Knowledge-driven data augmentation for {EEG}-based brain-computer interfaces},
author={Wang, Ziwei and Li, Siyang and Luo, Jingwei and Liu, Jiajing and Wu, Dongrui},
journal={Neural Networks},
pages={106351},
year={2024}
}
If you are interested in neural network based models, do check out Deep Transfer for EEG