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.

KEYWORDS:

• EEG
• Emotion recognition
• EWT
• MEMD
• Orthogonality

Additional information

Notes on contributors

Pinar Ozel

Pinar Ozel completed her BS degree from Erciyes University in 2007 and her MS degree from Bogazici University in 2011. She completed her PhD studies at Istanbul University in 2019. She has previous job experience as a product manager. Signal processing and biomedical signal methods are among her research interests. E-mail: [email protected]

Aydin Akan

Aydin Akan obtained his BS degree in electrical engineering from the University of Uludag in Bursa in 1988, MSc degree in electrical engineering from the Technical University of Istanbul in Istanbul, Turkey in 1991, and PhD degree in electrical engineering from the University of Pittsburgh in Pittsburgh, PA, USA in 1996. Between 1996 and 2017, he worked at Istanbul University, Department of Electrical and Electronics Engineering. He is currently a professor at Izmir University of Economics, Department of Electrical and Electronics Engineering. His research interests include non-stationary signal processing, time-frequency signal analysis techniques, and their applications to wireless communications and biomedical engineering. He is a Senior Member of the IEEE Signal Processing Society and associate editor of Digital Signal Processing Journal. E-mail: [email protected]