Gender recognition in emotion perception using EEG features

Gender recognition is widely studied in different areas and especially, many physiological theories have shown that there are many differences in emotional processing between different genders. However, there are many observations need to be verified. In our paper, we focus on the gender recognition in emotion perception using diverse EEG (electroencephalogram) features. The time-frequency and phase locked value (PLV) features are extracted and different feature selection methods are compared. We performed these methods on DEAP dataset. Results show that 1) the gender recognition accuracy using PLV feature is higher than using traditional time-frequency feature. 2) extreme learning machine (ELM) classifier has the best performance. 3) the gender recognition accuracy of theta and gamma bands is higher than other bands, which indicates that theta and gamma bands contain more discriminate gender information. 4) arousal has a greater impact than valence. And recognition accuracy of ‘calm’ emotion is lower than other emotions 5) feature selection methods can improve the accuracy.

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