Variance characteristic preserving common spatial pattern for motor imagery BCI

Wei Liang; Jing Jin; Ren Xu; Xingyu Wang; Andrzej Cichocki

doi:10.3389/fnhum.2023.1243750

Variance characteristic preserving common spatial pattern for motor imagery BCI

Front Hum Neurosci. 2023 Nov 9:17:1243750. doi: 10.3389/fnhum.2023.1243750. eCollection 2023.

Authors

Wei Liang¹, Jing Jin^{1

2}, Ren Xu³, Xingyu Wang¹, Andrzej Cichocki^{4

5}

Affiliations

¹ Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
² Shenzhen Research Institute of East China University of Science and Technology, Shenzhen, China.
³ g.tec Medical Engineering GmbH, Schiedlberg, Austria.
⁴ Systems Research Institute of Polish Academy of Science, Warsaw, Poland.
⁵ Department of Informatics, Nicolaus Copernicus University, Toruń, Poland.

Abstract

Introduction: The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space.

Methods: This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly.

Results: The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm.

Discussion: The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.

Keywords: EEG; brain-computer interface; common spatial pattern; motor imagery; variance characteristic preserving.

Grants and funding

This work was supported by STI 2030-major projects 2022ZD0208900 and the Grant National Natural Science Foundation of China under Grant 62176090; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX, in part by the Program of Introducing Talents of Discipline to Universities through the 111 Project under Grant B17017; This research is also supported by National Government GuidedSpecial Funds for Local Science and Technology Development (Shenzhen, China) (No. 2021Szvup043) and by Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key research and development plan industry foresight and key core technologies) under Grant BE2022064-1.