Channel Contributions of Eeg in Emotion Modelling Based on Multivariate Adaptive Orthogonal Signal Decomposition

Abstract

Empirical Mode Decomposition (EMD) provides an adaptive signal processing tool, and its multivariate extension is useful to model multichannel signals. Recently, EMD and multivariate EMD have successfully been applied to solve different signal processing problems. Electroencephalogram signals are often employed to explore the emotional concepts for human-machine interaction. In this paper, an emotion recognition model is presented via EEG signal decomposition by utilizing multivariate EMD. Intrinsic Mode Functions extracted by the multivariate EMD algorithm are quasi-orthogonal. Hence the Gram-Schmidt Orthogonalization method is applied to the extracted IMFs. The number of orthogonal components reveals the number of modes used in the second step of the proposed method, where the Empirical Wavelet Transform is used to explore different features of the IMFs. By applying Ensemble and Decision Tree classifiers on the calculated features, the emotional states are classified as high-low arousal, valence, and dominance with 72.7%, 62.0%, and 64.7% highest classification performances using the selected channels, respectively.