Electroencephalogram Emotion Recognition Based on Empirical Mode Decomposition and Optimal Feature Selection

摘要

Electroencephalogram (EEG) emotion recognition based on a hybrid feature extraction method in empirical mode decomposition domain combining with optimal feature selection based on sequence backward selection is proposed, which can reflect subtle information of multiscale components of unstable and nonlinear EEG signals and remove the reductant features to improve the performance of emotion recognition. The proposal is tested on DEAP dataset, in which the emotional states in the Valance dimension and Arousal dimension are classified by both K-nearest neighbor and support vector machine, respectively. In the experiments, temporal windows of different length and three kinds of rhythms of EEG signal are taken into account for comparison, from which the results show that EEG signal with 1s temporal window achieves highest recognition accuracy of 86.46% in Valence dimension and 84.90% in Arousal dimension, respectively, which is superior to some state-of-the-art works. The proposed method would be applied to real-time emotion recognition in multimodal emotional communication-based humans-robots interaction system.