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

Understand

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.