Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm

Variation in human brains creates difficulty in implementing electroencephalography into universal brain–machine interfaces. Conventional electroencephalography systems typically suffer from motion artefacts, extensive preparation time and bulky equipment, while existing electroencephalography class...

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Bibliographic Details
Published in:	Nature machine intelligence Vol. 1; no. 9; pp. 412 - 422
Main Authors:	Mahmood, Musa, Mzurikwao, Deogratias, Kim, Yun-Soung, Lee, Yongkuk, Mishra, Saswat, Herbert, Robert, Duarte, Audrey, Ang, Chee Siang, Yeo, Woon-Hong
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 01.09.2019 Nature Publishing Group
Subjects:	639/166 639/301/1005 9/10 Aerosols Algorithms Artificial neural networks Brain Circuits Classification Deep learning Electrodes Electroencephalography Electromagnetic interference Electronic systems Electronics Engineering Evoked potentials Eye movements Flexible components Information transfer Interfaces Keyboards Machine learning Man-machine interfaces Motor vehicles Neural networks Portability Real time Steady state Time domain analysis Wheelchairs
ISSN:	2522-5839, 2522-5839
Online Access:	Get full text
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