Abstract
Emotion recognition plays a very important role nowadays. Emotional recognition of electroencephalogram (EEG) signals involves high-dimensional EEG data, which is still a challenging task now. This paper presents an emotion recognition framework based on EEG and peripheral signals, and the ReliefF algorithm is used to select features of EEG and peripheral signals. The DEAP data set is used to process multi-channel physiology signals. In this paper the human emotion is classified into two categories (happy, unhappy) and three categories (happy, neutral, unhappy), respectively. The performance of the proposed method is evaluated in conjunction with ReliefF algorithm. The best average accuracy of K-Nearest Neighbor (KNN) and random forest (RF) are 94.295% and 98.832% in the binary classification tasks, and 79.210% and 98.364% in the triple classification tasks, respectively. At the same time, we also have adopted F1-score as the metric in the binary classification, which are 93.944% and 98.835%. The evaluation on the DEAP data set shows that using the ReliefF algorithm for feature selection can achieve good results on the issue of physiological emotion recognition.
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