  EEG-based brainprint recognition with deep learning models has garnered much
attention in biometric identification. Yet, studies have indicated
vulnerability to adversarial attacks in deep learning models with EEG inputs.
In this paper, we introduce a novel adversarial attack method that jointly
attacks time-domain and frequency-domain EEG signals by employing wavelet
transform. Different from most existing methods which only target time-domain
EEG signals, our method not only takes advantage of the time-domain attack's
potent adversarial strength but also benefits from the imperceptibility
inherent in frequency-domain attack, achieving a better balance between attack
performance and imperceptibility. Extensive experiments are conducted in both
white- and grey-box scenarios and the results demonstrate that our attack
method achieves state-of-the-art attack performance on three datasets and three
deep-learning models. In the meanwhile, the perturbations in the signals
attacked by our method are barely perceptible to the human visual system.
