# Making Brain-Computer Interfaces More Secure

arXiv: https://arxiv.org/abs/2606.02597 · HTML: https://mlsanity.com/paper/2606.02597/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 8 of 8)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [On the Vulnerability of CNN Classifiers in EEG-Based BCIs](https://mlsanity.com/paper/1904.01002/) (2019)
- [White-Box Target Attack for EEG-Based BCI Regression Problems](https://mlsanity.com/paper/1911.04606/) (2019)
- [Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network](https://mlsanity.com/paper/2605.12560/) (2026)
- [Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design](https://mlsanity.com/paper/2010.12066/) (2020)

## Abstract (arXiv)

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.