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

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...

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Bibliographic Details

Published in:	arXiv.org
Main Authors:	Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 07.01.2022
Subjects:	Classification Electroencephalography Human-computer interface Image classification Learning Representations
ISSN:	2331-8422
Online Access:	Get full text
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