[2606.02597] Making Brain-Computer Interfaces More Secure
[Submitted on 22 May 2026]
Title:Making Brain-Computer Interfaces More Secure
View a PDF of the paper titled Making Brain-Computer Interfaces More Secure, by Md Fahimul Kabir Chowdhury and 1 other authors
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Abstract:The development of brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) has advanced significantly mainly to machine learning. Although the majority of earlier research has been on increasing classification accuracy, relatively little focus has been placed on security and robustness. According to recent research, EEG-based BCIs are susceptible to adversarial attacks, which can cause misdiagnosis due to minute, well-crafted disturbances. Evaluating model robustness against such perturbations is therefore critical for ensuring reliable deployment. In this study, we propose a lightweight custom Convolutional Neural Network (CNN) architecture to investigate adversarial robustness in EEG-based BCIs. The suggested method is assessed using two EEG datasets and contrasted with three novel CNN models tailored to EEG, namely EEGNet, DeepConvNet, and SleepEEGNet, under gradient-based adversarial attack scenarios. According to experimental findings, the suggested model continuously performs better in classification under adversarial perturbations compared to baseline models, indicating improved robustness. These findings highlight the potential of lightweight architectures for enhancing the reliability of EEG-based BCI systems under adversarial conditions.
Comments: Accepted and presented at IEEE World AI IoT Congress 2026
Subjects:
Machine Learning (cs.LG); Cryptography and Security (cs.CR)
Cite as: arXiv:2606.02597 [cs.LG]
(or arXiv:2606.02597v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.02597
arXiv-issued DOI via DataCite
Submission history
From: Md Fahimul Kabir Chowdhury [view email] [v1] Fri, 22 May 2026 23:35:44 UTC (444 KB)
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