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

Machine Learning