Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks
This work addresses data scarcity in EEG-based brain-computer interfaces, but it is incremental as it applies an existing WGAN method to a new domain without major methodological innovations.
The paper tackled the limited availability and high variability of EEG signals for brain-computer interfaces by using a Wasserstein Generative Adversarial Network (WGAN) to generate synthetic EEG data, which improved classifier accuracies and achieved FID scores of 1.345 and 11.565 for eyes-open and closed conditions.
Electroencephalography (EEG) plays a vital role in recording brain activities and is integral to the development of brain-computer interface (BCI) technologies. However, the limited availability and high variability of EEG signals present substantial challenges in creating reliable BCIs. To address this issue, we propose a practical solution drawing on the latest developments in deep learning and Wasserstein Generative Adversarial Network (WGAN). The WGAN was trained on the BCI2000 dataset, consisting of around 1500 EEG recordings and 64 channels from 45 individuals. The generated EEG signals were evaluated via three classifiers yielding improved average accuracies. The quality of generated signals measured using Frechet Inception Distance (FID) yielded scores of 1.345 and 11.565 for eyes-open and closed respectively. Even without a spectral or spatial loss term, our WGAN model was able to emulate the spectral and spatial properties of the EEG training data. The WGAN-generated data mirrored the dominant alpha activity during closed-eye resting and high delta waves in the training data in its topographic map and power spectral density (PSD) plot. Our research testifies to the potential of WGANs in addressing the limited EEG data issue for BCI development by enhancing a small dataset to improve classifier generalizability.