EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

TL;DR

This paper demonstrates a novel, practical backdoor attack method on EEG-based brain-computer interfaces using narrow pulse poisoning, revealing significant security vulnerabilities in current machine learning models.

Contribution

It introduces a new poisoning attack technique that does not require synchronization with EEG trials, highlighting a critical security flaw in EEG-based BCIs.

Findings

The attack effectively creates backdoors in EEG classifiers.

Backdoors can be triggered without trial synchronization.

The method demonstrates robustness against defenses.

Abstract

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it.