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Asian Conference on Pattern Recognition

ACPR 2021: Pattern Recognition pp 268–281Cite as

Subject-Independent Motor Imagery EEG Classification Based on Graph Convolutional Network

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13189))

Abstract

Electroencephalogram (EEG) motor imagery (MI) has attracted much attention in brain-computer interfaces (BCIs) as it directly encodes human intentions. However, the variability of EEG-based brain signals between individuals requires current BCI systems to undergo calibration procedures before its usage. In this paper, we propose a model that targets minimizing such procedures by improving inter-subject classification performance. The purpose of our proposed method is to extract features using previously studied convolution-based deep learning structures while utilizing a graph structure to analyze inter-subject relationships with multiple subjects. By utilizing not only features but also the relationship between subject-specific features, it becomes possible to make predictions focusing on subjects with high similarity. Therefore, even new users not seen during the training process are predicted relatively efficiently. To validate our method, we evaluated our model with the public dataset BCI Competition IV IIa. The results in our study suggest that our proposed method improved the cross-subject classification accuracy by combining it with the previous deep learning model and induced a balanced prediction for the classes. Our study has shown the potential to develop MI-based BCI applications that do not require user calibration by training the model with pre-existing datasets.

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Acknowledgment

This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432) and the Defense Challengeable Future Technology Program of Agency for Defense Development, Republic of Korea.

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Authors and Affiliations

  1. School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea

    Juho Lee, Jin Woo Choi & Sungho Jo

Authors
  1. Juho Lee
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  2. Jin Woo Choi
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  3. Sungho Jo
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Corresponding author

Correspondence to Sungho Jo .

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Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Lee, J., Choi, J.W., Jo, S. (2022). Subject-Independent Motor Imagery EEG Classification Based on Graph Convolutional Network. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-02444-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02443-6

  • Online ISBN: 978-3-031-02444-3

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