TY - GEN
T1 - Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface
AU - Jeong, Ji Hyeok
AU - Kim, Keun Tae
AU - Kim, Dong Joo
AU - Lee, Song Joo
AU - Kim, Hyungmin
N1 - Funding Information:
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00432), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1C1C2008446 and 2022R1A2C1013205).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54\pm7.78\% (288 trials), 85.76 \pm 8.00\% (240 trials), 84.65\pm 8.11\% (192 trials), and 83.29 \pm 8.25\% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.
AB - Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54\pm7.78\% (288 trials), 85.76 \pm 8.00\% (240 trials), 84.65\pm 8.11\% (192 trials), and 83.29 \pm 8.25\% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.
UR - http://www.scopus.com/inward/record.url?scp=85138126664&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871463
DO - 10.1109/EMBC48229.2022.9871463
M3 - Conference contribution
C2 - 36086005
AN - SCOPUS:85138126664
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 48
EP - 51
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -