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

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 6; pp. 2105 - 2118
• Main Authors: Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: autoencoder (AE); Brain-computer interfaces (BCIs); Calibration; Classification; deep metric learning (DML); EEG; Electroencephalography; Feature extraction; Human-computer interface; Image classification; Interfaces; Learning; Measurement; Mental task performance; motor imagery (MI); Motor skill learning; multi-task learning; Multitasking; Representations; Task analysis; Training

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...