Gender-Sensitive EEG Channel Selection for Emotion Recognition Using Enhanced Genetic Algorithm

EEG channel selection aims to choose informative and representative channels to reduce data redundancy. It is very beneficial for improving the utility and efficiency of emotion recognition. Previous studies on EEG channel selection have not considered the influence of genders despite long-standing belief in gender differences with respect to emotion analysis. In this paper, we collected EEG signals from 20 subjects containing 10 males and 10 females by letting them watch short emotional videos. Then, to reduce data redundancy, we propose an enhanced genetic algorithm to select the optimal channel subsets separately for male and female subjects by incorporating a novel evolution operation. Experimental results show that the proposed algorithm achieves higher accuracy in terms of emotion recognition than several compared methods with a smaller channel subset. Besides, experimental results also indicate that the gender differences in neural patterns indeed exist. Through this study, the gender-sensitive channel selection offers a new avenue for further development of EEG based emotion recognition.