Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Abstract

Survey/review study Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,* 1 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom 2 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States * Correspondence: Mengjie.Huang@liverpool.ac.uk     Received: 12 October 2022 Accepted: 28 November 2022 Published: 22 December 2022   Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.