Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks

arXiv:2402.09453v1 Announce Type: cross
Abstract: 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 …

abstract accuracy adversarial arxiv availability bci brain brain-computer interface challenges classification computer cs.ai cs.lg development eeg eess.sp generative generative adversarial networks integral issue networks recording role signal technologies type vital