Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping

Jinhyo Shin; Wonzoo Chung

doi:10.1109/BCI57258.2023.10078711

Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping

Jinhyo Shin^*
, Wonzoo Chung

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

In this paper, we propose a novel multi-class motor imagery (MI) classification method in electroencephalogram (EEG)-based brain-computer interface (BCI) using multi-class support vector machine (SVM). EEG signal is decomposed into multi-band signal and then for each sub-band, spatial sample covariance matrix is computed. By applying Riemannian tangent space mapping method which utilizes the geometric structure of covariance matrices to the sub-band spatial covariance matrices, sub-band features are extracted and combined to form a feature vector. In order to improve multi-class classification performance, the feature vector is passed into multi-class SVM which directly tackles multi-class problem, in contrast to the existing works where one-versus-one or one-versus-rest strategy is used to indirectly solve multi-class classification problem. The performance of the proposed method is evaluated on the 4-class BCI Competition IV dataset 2a and the experimental results show that the proposed method improves the mean classification accuracy.

Original language	English
Title of host publication	11th International Winter Conference on Brain-Computer Interface, BCI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665464444
DOIs	https://doi.org/10.1109/BCI57258.2023.10078711
Publication status	Published - 2023
Event	11th International Winter Conference on Brain-Computer Interface, BCI 2023 - Virtual, Online, Korea, Republic of Duration: 2023 Feb 20 → 2023 Feb 22

Publication series

Name	International Winter Conference on Brain-Computer Interface, BCI
Volume	2023-February
ISSN (Print)	2572-7672

Conference

Conference	11th International Winter Conference on Brain-Computer Interface, BCI 2023
Country/Territory	Korea, Republic of
City	Virtual, Online
Period	23/2/20 → 23/2/22

Bibliographical note

Funding Information:
ACKNOWLEDGMENT 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), 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)), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub), and the BK21 Four program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea.

Publisher Copyright:
© 2023 IEEE.

Keywords

Brain-Computer Interface (BCI)
Electroencephalogram (EEG)
Motor Imagery (MI)
Multi-band
Multi-class support vector machine (SVM)
Riemannian tangent space mapping

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Signal Processing

Access to Document

10.1109/BCI57258.2023.10078711

Cite this

Shin, J., & Chung, W. (2023). Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping. In 11th International Winter Conference on Brain-Computer Interface, BCI 2023 (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2023-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI57258.2023.10078711

Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping. / Shin, Jinhyo; Chung, Wonzoo.
11th International Winter Conference on Brain-Computer Interface, BCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2023-February).

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

Shin, J & Chung, W 2023, Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping. in 11th International Winter Conference on Brain-Computer Interface, BCI 2023. International Winter Conference on Brain-Computer Interface, BCI, vol. 2023-February, Institute of Electrical and Electronics Engineers Inc., 11th International Winter Conference on Brain-Computer Interface, BCI 2023, Virtual, Online, Korea, Republic of, 23/2/20. https://doi.org/10.1109/BCI57258.2023.10078711

Shin J, Chung W. Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping. In 11th International Winter Conference on Brain-Computer Interface, BCI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (International Winter Conference on Brain-Computer Interface, BCI). doi: 10.1109/BCI57258.2023.10078711

@inproceedings{da26e08e087d4b4e81227ffdd15c431f,

title = "Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping",

abstract = "In this paper, we propose a novel multi-class motor imagery (MI) classification method in electroencephalogram (EEG)-based brain-computer interface (BCI) using multi-class support vector machine (SVM). EEG signal is decomposed into multi-band signal and then for each sub-band, spatial sample covariance matrix is computed. By applying Riemannian tangent space mapping method which utilizes the geometric structure of covariance matrices to the sub-band spatial covariance matrices, sub-band features are extracted and combined to form a feature vector. In order to improve multi-class classification performance, the feature vector is passed into multi-class SVM which directly tackles multi-class problem, in contrast to the existing works where one-versus-one or one-versus-rest strategy is used to indirectly solve multi-class classification problem. The performance of the proposed method is evaluated on the 4-class BCI Competition IV dataset 2a and the experimental results show that the proposed method improves the mean classification accuracy.",

keywords = "Brain-Computer Interface (BCI), Electroencephalogram (EEG), Motor Imagery (MI), Multi-band, Multi-class support vector machine (SVM), Riemannian tangent space mapping",

author = "Jinhyo Shin and Wonzoo Chung",

note = "Funding Information: ACKNOWLEDGMENT 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{\textquoteright}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{\textquoteright}s Intentions using Deep Learning), 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)), Institute of Information \& communications Technology Planning \& Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub), and the BK21 Four program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea. Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th International Winter Conference on Brain-Computer Interface, BCI 2023 ; Conference date: 20-02-2023 Through 22-02-2023",

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doi = "10.1109/BCI57258.2023.10078711",

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series = "International Winter Conference on Brain-Computer Interface, BCI",

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N1 - Funding Information: ACKNOWLEDGMENT 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), 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)), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub), and the BK21 Four program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea. Publisher Copyright: © 2023 IEEE.

PY - 2023

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N2 - In this paper, we propose a novel multi-class motor imagery (MI) classification method in electroencephalogram (EEG)-based brain-computer interface (BCI) using multi-class support vector machine (SVM). EEG signal is decomposed into multi-band signal and then for each sub-band, spatial sample covariance matrix is computed. By applying Riemannian tangent space mapping method which utilizes the geometric structure of covariance matrices to the sub-band spatial covariance matrices, sub-band features are extracted and combined to form a feature vector. In order to improve multi-class classification performance, the feature vector is passed into multi-class SVM which directly tackles multi-class problem, in contrast to the existing works where one-versus-one or one-versus-rest strategy is used to indirectly solve multi-class classification problem. The performance of the proposed method is evaluated on the 4-class BCI Competition IV dataset 2a and the experimental results show that the proposed method improves the mean classification accuracy.

AB - In this paper, we propose a novel multi-class motor imagery (MI) classification method in electroencephalogram (EEG)-based brain-computer interface (BCI) using multi-class support vector machine (SVM). EEG signal is decomposed into multi-band signal and then for each sub-band, spatial sample covariance matrix is computed. By applying Riemannian tangent space mapping method which utilizes the geometric structure of covariance matrices to the sub-band spatial covariance matrices, sub-band features are extracted and combined to form a feature vector. In order to improve multi-class classification performance, the feature vector is passed into multi-class SVM which directly tackles multi-class problem, in contrast to the existing works where one-versus-one or one-versus-rest strategy is used to indirectly solve multi-class classification problem. The performance of the proposed method is evaluated on the 4-class BCI Competition IV dataset 2a and the experimental results show that the proposed method improves the mean classification accuracy.

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KW - Electroencephalogram (EEG)

KW - Motor Imagery (MI)

KW - Multi-band

KW - Multi-class support vector machine (SVM)

KW - Riemannian tangent space mapping

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DO - 10.1109/BCI57258.2023.10078711

M3 - Conference contribution

AN - SCOPUS:85152205864

T3 - International Winter Conference on Brain-Computer Interface, BCI

BT - 11th International Winter Conference on Brain-Computer Interface, BCI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th International Winter Conference on Brain-Computer Interface, BCI 2023

Y2 - 20 February 2023 through 22 February 2023

ER -

Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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