"Study Unveils Security Vulnerabilities in EEG-Based Brain-Computer Interfaces"

Researchers at Huazhong University of Science and Technology did a study on the security of electroencephalography (EEG)-based brain-computer interfaces (BCIs). Breakthroughs in machine learning (ML) have led to the advancement of BCI spellers, which allow people to use their brain activity to control their computers. Much research on developing BCI classifiers has focussed on increasing speed and reliability instead of examining the security vulnerabilities they may have. Recent studies have shown that that ML algorithms such as those used in computer vision, speech recognition, and more, are vulnerable to a variety of attacks. These attacks could lead to misclassification or the production of incorrect output. In this study, researchers examined P300 BCI spellers, which are used in clinics to assess or detect disorders of consciousness. They discovered that adversarial attacks on BCI spellers could result in usability issues, misdiagnoses, and other consequences, posing a threat to the well-being of patients. Researchers hope that this research will help inform the development of better techniques for securing BCIs. This article continues to discuss the goal and key findings of the study on EEG-based BCI security.   

TechXplore reports "Study Unveils Security Vulnerabilities in EEG-Based Brain-Computer Interfaces"