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This paper's main goal is to provide an attacker's point of view on datapoisoning assaults that use label-flipping during the training phase of systemsthat use electroencephalogram (EEG) signals to evaluate human emotion. Toattack different machine learning classifiers such as Adaptive Boosting(AdaBoost) and Random Forest dedicated to the classification of 4 differenthuman emotions using EEG signals, this paper proposes two scenarios oflabel-flipping methods. The results of the studies show that the proposed datapoison attacksm based on label-flipping are successful regardless of the model,but different models show different degrees of resistance to the assaults. Inaddition, numerous Explainable Artificial Intelligence (XAI) techniques areused to explain the data poison attacks on EEG signal-based human emotionevaluation systems.

Explainable Label-flipping Attacks on Human Emotion Assessment System