Making Brain-Computer Interfaces More Secure

This study proposes a lightweight custom Convolutional Neural Network (CNN) architecture to investigate adversarial robustness in EEG-based Brain-Computer Interfaces (BCIs). The method is assessed using two EEG datasets and compared with other CNN models under gradient-based adversarial attack scenarios to ensure reliable BCI deployment.