mlsanityData to 7 Oct 2026

Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces

arXiv 2212.00727 ↗First posted 28 Nov 2022cs.CR cs.AI cs.HCcited by 4

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Abstract (arXiv)

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI research focused on improving its accuracy, but few had considered its security. Recent studies, however, have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detection of adversarial examples is crucial to both the understanding of this phenomenon and the defense. This paper, for the first time, explores adversarial detection in EEG-based BCIs. Experiments on two EEG datasets using three convolutional neural networks were performed to verify the performances of multiple detection approaches. We showed that both white-box and black-box attacks can be detected, and the former are easier to detect.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 59 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

29 of 59 references.

References · 29

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17 more
  1. 2017Adversarial Patchcited by 514
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decodingcited by 2
  2. 2026SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfacescited by 1
  3. 2025Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devicescited by 0
  4. 2026When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Modelscited by 0