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

Authors
Jeong, Ji-HyeokSung, Dong-JinKim, Keun-TaeLee, Song JooKim, Dong-JooKim, Hyungmin
Issue Date
2023-02
Publisher
IEEE
Citation
11th International Winter Conference on Brain-Computer Interface (BCI)
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.
ISSN
2572-7672
URI
https://pubs.kist.re.kr/handle/201004/76500
DOI
10.1109/BCI57258.2023.10078479
Appears in Collections:
KIST Conference Paper > 2023
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