Universal adversarial perturbations for CNN classifiers in EEG-based BCIs

Objective. Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. Approach. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Main results. Experimental results demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems. Significance. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs.

Automated summary

The study introduces a novel total loss minimization approach to generate universal adversarial perturbations for EEG-based BCIs. Experimental results demonstrate the effectiveness of this method on three popular CNN classifiers for both target and non-target attacks, while also confirming the transferability of UAPs across different systems.