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arXiv · 2102.03814

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Phairot Autthasan·Rattanaphon Chaisaen·Thapanun Sudhawiyangkul·Phurin Rangpong·Suktipol Kiatthaveephong·Nat Dilokthanakul·Gun Bhakdisongkhram·Huy Phan·Cuntai Guan·Theerawit Wilaiprasitporn
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Abstract

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 great 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 subject-independent manner. 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. 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. We demonstrate that MIN2Net improves discriminative information in the latent representation. This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without the need for calibration.

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Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn. 2022-01-07. MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification. https://doi.org/10.1109/tbme.2021.3137184

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