# Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2212.00727 · HTML: https://mlsanity.com/paper/2212.00727/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 59 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality](https://mlsanity.com/paper/1801.02613/) (2018)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Detecting Adversarial Samples from Artifacts](https://mlsanity.com/paper/1703.00410/) (2017)
- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [Adversarial Perturbations Against Deep Neural Networks for Malware Classification](https://mlsanity.com/paper/1606.04435/) (2016)
- [LiBRe: A Practical Bayesian Approach to Adversarial Detection](https://mlsanity.com/paper/2103.14835/) (2021)
- [On the Vulnerability of CNN Classifiers in EEG-Based BCIs](https://mlsanity.com/paper/1904.01002/) (2019)
- [Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers](https://mlsanity.com/paper/2001.11569/) (2020)
- [White-Box Target Attack for EEG-Based BCI Regression Problems](https://mlsanity.com/paper/1911.04606/) (2019)
- [Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs](https://mlsanity.com/paper/1912.01171/) (2019)
- [EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks](https://mlsanity.com/paper/2011.00101/) (2020)
- [The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing](https://mlsanity.com/paper/2101.03924/) (2021)
- [Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)](https://mlsanity.com/paper/1702.02901/) (2017)
- [Detecting Adversarial Perturbations with Saliency](https://mlsanity.com/paper/1803.08773/) (2018)
- [Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review](https://mlsanity.com/paper/2102.02729/) (2021)

## Built on by (most cited first, 4 of 4)

- [PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding](https://mlsanity.com/paper/2412.11390/) (2024)
- [SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2601.05789/) (2026)
- [Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices](https://mlsanity.com/paper/2506.15028/) (2025)
- [When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models](https://mlsanity.com/paper/2607.06630/) (2026)

## 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.