Emotion Recognition Based on Brain Connectivity Reservoir and Valence Lateralization for Cyber-Physical-Social Systems.

• An EEG emotion recognition method is proposed for Cyber-Physical-Social Systems. • An emotion recognition model based on brain connectivity reservoir is established. • A training algorithm based on valence lateralization is proposed. • The proposed method improves the average recognition accuracy to 85.55 As an important application of pattern recognition, emotion recognition can make Cyber-Physical-Social Systems (CPSS) provide more efficient services for humans. In order to improve the recognition accuracy, this paper proposes an electroencephalogram (EEG) emotion recognition method based on brain connectivity reservoir (BCR) and valence lateralization (VL) for CPSS. First, for the purpose of comprehensively considering the temporality, nonlinearity, and correlation of EEG signals, an emotion recognition model based on BCR is established. Specifically, according to the connectivity index, the correlation between EEG channels is calculated to determine the brain connectivity structure of BCR, and the features of EEG signals are represented through BCR, then the classification result is obtained by the fully connected neural network according to the feature representation. Second, for the purpose of enhancing the feature representation capability of BCR, a training algorithm of BCR based on VL is proposed. Specifically, BCR is divided into two parts, i.e., the left hemi-BCR and the right hemi-BCR. These two parts are trained separately, so that the lateralization characteristic of the brain is better reflected. Finally, the experimental results on DEAP demonstrate that the proposed method achieves a recognition accuracy of 85.55% which is higher than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]