EEG signals can be used to successfully classify anger, fear, and surprise emotions with a mean accuracy of 66.3%.
Hypothesis-generating for EEG emotion classification; larger validation studies needed before clinical consideration.
It is difficult to classify anger, fear, and surprise emotions with autonomic nervous system response patterns, because these three emotions show similar levels of valence and arousal dimensions. The purpose of this study was to classify three emotions by using EEG signals. Linear discriminant analysis (LDA) using three types of EEG characteristics showed that the mean recognition accuracy was 66.3%. These findings reveal that three emotions were successfully able to be classified based on EEG signals.
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Park et al. (2013) studied this question.