MultiChannel Physiological Signal Emotion Recognition Based on ReliefF Feature Selection Emotion recognition plays a very important role nowadays. Emotional recognition of electroencephalogram (EEG) signals involves highdimensional 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 multichannel 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 KNearest 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 F1score 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.

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.