Adversarial robustness benchmark for EEG-based brain–computer interfaces

Abstract

Many machine learning approaches have been successfully applied to electroencephalogram (EEG) based brain–computer interfaces (BCIs). Most existing approaches focused on making EEG-based BCIs more accurate, but few have considered their security. Recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks. To our knowledge, there is no study on adversarial defenses in EEG-based BCIs. This paper, for the first time, explores multiple classical and state-of-the-art adversarial defense approaches in EEG-based BCIs. Specifically, we demonstrated the performances of nine adversarial defense approaches on three convolutional neural networks and two EEG datasets, and established a comprehensive benchmark to evaluate their effectiveness in BCIs. Based on the evaluation results, we drew some key observations, hoping to inspire future adversarial defense research and attract more attention to the adversarial security of EEG-based BCIs.