Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

• Published in: arXiv.org
• Main Authors: Chen, Xiaoqing, Wang, Ziwei, Wu, Dongrui
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 04.11.2024
• Subjects: Accuracy; Alignment; Artificial neural networks; Classification; Computer Science - Artificial Intelligence; Computer Science - Human-Computer Interaction; Computer Science - Learning; Computer vision; Electroencephalography; Human-computer interface; Machine learning; Robustness; System effectiveness
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2411.02094

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been propose...