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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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 69; H. 6; S. 2105 - 2118
Hauptverfasser:	Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
ISSN:	0018-9294, 1558-2531, 1558-2531
Online-Zugang:	Volltext
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