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Title: MIN2Net: end-to-end multi-task learning for subject-independent motor imagery EEG classification
Authors: Autthasan, Phairot
Chaisaen, Rattanaphon
Sudhawiyangkul, Thapanun
Rangpong, Phurin
Kiatthaveephong, Suktipol
Dilokthanakul, Nat
Bhakdisongkhram, Gun
Phan, Huy
Guan, Cuntai
Wilaiprasitporn, Theerawit
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Autthasan, P., Chaisaen, R., Sudhawiyangkul, T., Rangpong, P., Kiatthaveephong, S., Dilokthanakul, N., Bhakdisongkhram, G., Phan, H., Guan, C. & Wilaiprasitporn, T. (2022). MIN2Net: end-to-end multi-task learning for subject-independent motor imagery EEG classification. IEEE Transactions On Bio-Medical Engineering, 69(6), 2105-2118.
Journal: IEEE Transactions on Bio-Medical Engineering
Abstract: Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subjectindependent manner. Methods: To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. Results: This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively. Conclusion: We demonstrate that MIN2Net improves discriminative information in the latent representation. Significance: This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.
ISSN: 0018-9294
DOI: 10.1109/TBME.2021.3137184
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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