Adversarial sample detection for EEG-based brain-computer interfaces

Abstract Deep neural networks (DNNs) play a pivotal role within the domain of brain-computer interfaces (BCIs). Nevertheless, DNNs are demonstrated to exhibit susceptibility to adversarial attacks. In BCIs, researchers have been concerned about the security of DNNs and have devised various adversarial defense methods to resist adversarial attacks. However, most defense methods encounter performance degradation when dealing with normal samples due to changes in the original model. As an alternative strategy, adversarial detection aims to devise additional modules or use statistical properties to identify potentially adversarial samples without changing the original model. Hence, the present study provides a comprehensive evaluation of several typical adversarial detection methods applied to EEG datasets. The experiments indicate that the detection method based on the kernel density estimation (KDE) shows the best performance under various adversarial attacks.