Before revision:  However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks , e. g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice.
After revision:  However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks .