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 timefrequency 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 timefrequency 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 xe2x80x98calmxe2x80x99 emotion is lower than other emotions 5) feature selection methods can improve the accuracy.

Research on EEG Channel Selection Method for Emotion Recognition In this paper, based on the DEAP dataset, we studied the effects of different EEG channel selection methods on the accuracy of emotion recognition for different frequency bands. First, the discrete wavelet transform method is used to divide the EEG signals into four bands of gamma, beta, alpha and theta, and extract the entropy and energy of each band as classification features. Then, the following three channel selection methods are compared to select the best EEG channel combination for the four emotions classification susing, the channel selection method based on experience, the indirect channel selection method based on the mRMR feature selection algorithm, and the direct channel selection method based on the mRMR feature selection algorithm. Finally, the extreme learning machine with kernel is used to verify the effectiveness of the channel selection method. The results show that based on the mRMR feature selection algorithm, the channel selection method taking each channel as a whole is more powerful in balancing the number of channels and classification accuracy. In the beta band, the number of channels is reduced from 32 to 22, which is only 1.37% (from 80.83% to 79.37%) lower than the best classification accuracy, and the emotion recognition performance remains at a high level. Compared with the results of others, this paper can use less channels to achieve similar or higher emotional recognition performance than others, which further proves the effectiveness of the method. In addition, we also found that high frequencies (gamma and beta bands) are better for emotional recognition. This study provides a reference for channel and band selection in EEGbased emotion recognition.