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

Mengjie Huang

doi:10.53941/ijndi0101007

Open Access

Survey/Review Study

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

Nanxi Yu^{1, 2}

,

Rui Yang¹

,

Mengjie Huang^{1, *}

Author Information

Submitted: 12 Oct 2022 | Accepted: 28 Nov 2022 | Published: 22 Dec 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.

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Keywords

brain–computer interface | common spatial pattern | electroencephalogram | motor imagery

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DOI

10.53941/ijndi0101007

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Volume 1, Issue 1

How to Cite

Yu, N., Yang, R., & Huang, M. (2022). Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function. International Journal of Network Dynamics and Intelligence, 1(1), 73–84. https://doi.org/10.53941/ijndi0101007

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