Improving EEG Signal Classification Accuracy Using Wasserstein
  Generative Adversarial Networks
Joshua Park Priyanshu Mahey Ore Adeniyi
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.