EEG

This paper introduces the generative perturbation network (GPN), an efficient model for generating universal adversarial examples in EEG-based brain-computer interface (BCI) systems. GPN can produce perturbations capable of fooling deep neural networks with minor undetectable changes, and it outperforms previous methods in crafting signal-agnostic perturbations. Additionally, GPN can efficiently generate perturbations for various targets and victim models, demonstrating high transferability across classification networks.