Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface

Ji Hyeok Jeong; Dong Jin Sung; Keun Tae Kim; Song Joo Lee; Dong Joo Kim; Hyungmin Kim

doi:10.1109/BCI57258.2023.10078479

Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface

Ji Hyeok Jeong, Dong Jin Sung, Keun Tae Kim, Song Joo Lee, Dong Joo Kim, Hyungmin Kim

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

1 Citation (Scopus)

Abstract

Various convolutional neural network (CNN) -based models have been proposed to improve classification performance in the MI (motor imagery) -based BCI (brain-computer interface) dataset with multiple subjects. However, most studies have not investigated whether the subject-transfer with fine-tuning is effective. In this study, we proposed a subject-transfer method with subject-specific fine-tuning based on Multi-Model CNN and compared classification accuracies with various CNN models. For evaluation, we used the public 2020 international BCI competition track 4 datasets with 15 subjects and 2 sessions. Each CNN model was pre-trained with other subjects' training sets, fine-tuned with the target subject's training set, and evaluated on their validation set. The classification accuracy of the proposed subject-transfer method with Multi-Model CNN (59.44± 16.57%) was significantly increased compared to the conventional method (57.37± 15.92%). The proposed subject-transfer method can contribute to developing effective models with high classification accuracy in datasets with multiple subjects and sessions.

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/BCI57258.2023.10078479
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:
This work was supported by a National Research Foundation of Korea (NRF) Grant funded by the Korean government (Ministry of Science and ICT, MSIT) (No. 2022R1A2C1013205) and Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIT) (No. 2017-0-00432).

Publisher Copyright:
© 2023 IEEE.

Keywords

Brain-Computer interface
Convolutional Neural Network
EEG
Motor Imagery
Subject-Transfer

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Signal Processing

Access to Document

10.1109/BCI57258.2023.10078479

Cite this

Jeong, J. H., Sung, D. J., Kim, K. T., Lee, S. J., Kim, D. J., & Kim, H. (2023). Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface. 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/BCI57258.2023.10078479

Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface. / Jeong, Ji Hyeok; Sung, Dong Jin; Kim, Keun Tae et al.
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

Jeong, JH, Sung, DJ, Kim, KT, Lee, SJ, Kim, DJ & Kim, H 2023, Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface. 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/BCI57258.2023.10078479

Jeong JH, Sung DJ, Kim KT, Lee SJ, Kim DJ, Kim H. Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface. 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/BCI57258.2023.10078479

Jeong, Ji Hyeok ; Sung, Dong Jin ; Kim, Keun Tae et al. / Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface. 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).

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abstract = "Various convolutional neural network (CNN) -based models have been proposed to improve classification performance in the MI (motor imagery) -based BCI (brain-computer interface) dataset with multiple subjects. However, most studies have not investigated whether the subject-transfer with fine-tuning is effective. In this study, we proposed a subject-transfer method with subject-specific fine-tuning based on Multi-Model CNN and compared classification accuracies with various CNN models. For evaluation, we used the public 2020 international BCI competition track 4 datasets with 15 subjects and 2 sessions. Each CNN model was pre-trained with other subjects' training sets, fine-tuned with the target subject's training set, and evaluated on their validation set. The classification accuracy of the proposed subject-transfer method with Multi-Model CNN (59.44± 16.57%) was significantly increased compared to the conventional method (57.37± 15.92%). The proposed subject-transfer method can contribute to developing effective models with high classification accuracy in datasets with multiple subjects and sessions.",

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Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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