Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score

Jinhyo Shin; Wonzoo Chung

doi:10.1007/978-3-031-02375-0_12

Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score

Jinhyo Shin, Wonzoo Chung

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose a novel motor imagery (MI) classification method in electroencephalogram (EEG)-based brain-computer interface (BCI) using local log Riemannian distance matrices (LRDM). The proposed method selects optimal local LRDM based on confusion area score which is designed to minimize the overlap between the class-dependent distributions of Riemannian distance between local covariance matrices that are generated from adjacent (local) channels centered on each channel. A feature vector is formed by concatenating vectorized selected local LRDM and used as input to support vector machine (SVM) in order to classify motor imagery. The performance of the proposed method is evaluated using BCI Competition III dataset IVa and BCI Competition IV dataset I. The results confirm performance improvement of the proposed method compared to existing MI classification methods.

Original language	English
Title of host publication	Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers
Editors	Christian Wallraven, Qingshan Liu, Hajime Nagahara
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	159-169
Number of pages	11
ISBN (Print)	9783031023743
DOIs	https://doi.org/10.1007/978-3-031-02375-0_12
Publication status	Published - 2022
Event	6th Asian Conference on Pattern Recognition, ACPR 2021 - Virtual, Online Duration: 2021 Nov 9 → 2021 Nov 12

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13188 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th Asian Conference on Pattern Recognition, ACPR 2021
City	Virtual, Online
Period	21/11/9 → 21/11/12

Bibliographical note

Funding Information:
Acknowledgements. This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development Of Non-invasive Integrated BCI SW Platform To Control Home Appliance And External Devices By User’s Thought Via AR/VR Interface), Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program(Korea University)).

Funding Information:
This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development Of Non-invasive Integrated BCI SW Platform To Control Home Appliance And External Devices By User’s Thought Via AR/VR Interface), Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Pro-gram(Korea University)).

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Brain-computer interface (BCI)
Electroencephalogram (EEG)
Local feature
Log Riemannian distance matrix
Motor imagery (MI)

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-02375-0_12

Cite this

Shin, J., & Chung, W. (2022). Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score. In C. Wallraven, Q. Liu, & H. Nagahara (Eds.), Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers (pp. 159-169). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13188 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-02375-0_12

Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score. / Shin, Jinhyo; Chung, Wonzoo.
Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers. ed. / Christian Wallraven; Qingshan Liu; Hajime Nagahara. Springer Science and Business Media Deutschland GmbH, 2022. p. 159-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13188 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shin, J & Chung, W 2022, Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score. in C Wallraven, Q Liu & H Nagahara (eds), Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13188 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 159-169, 6th Asian Conference on Pattern Recognition, ACPR 2021, Virtual, Online, 21/11/9. https://doi.org/10.1007/978-3-031-02375-0_12

Shin J, Chung W. Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score. In Wallraven C, Liu Q, Nagahara H, editors, Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 159-169. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-02375-0_12

Shin, Jinhyo ; Chung, Wonzoo. / Motor Imagery Classification Based on Local Log Riemannian Distance Matrices Selected by Confusion Area Score. Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers. editor / Christian Wallraven ; Qingshan Liu ; Hajime Nagahara. Springer Science and Business Media Deutschland GmbH, 2022. pp. 159-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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