MIN2Net: end-to-end multi-task learning for subject-independent motor imagery EEG classification

MIN2Net: end-to-end multi-task learning for subject-independent motor imagery EEG classification

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...

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Bibliographic Details
Main Authors: Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Brain-Computer Interfaces
Motor Imagery
Online Access:https://hdl.handle.net/10356/162519

Internet

https://hdl.handle.net/10356/162519

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