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

• 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: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Brain-Computer Interfaces; Motor Imagery
• Online Access: https://hdl.handle.net/10356/162519
• Institution: Nanyang Technological University

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...