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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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
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Abstract	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 classification methods require training on a per-subject or per-session basis. Here, we introduce a fully portable, wireless, flexible scalp electronic system, incorporating a set of dry electrodes and a flexible membrane circuit. Time-domain analysis using convolutional neural networks allows for accurate, real-time classification of steady-state visually evoked potentials in the occipital lobe. Compared to commercial systems, the flexible electronics show the improved performance in detection of evoked potentials due to significant reduction of noise and electromagnetic interference. The two-channel scalp electronic system achieves a high information transfer rate (122.1 ± 3.53 bits per minute) with six human subjects, allowing for wireless, real-time, universal electroencephalography classification for an electric wheelchair, a motorized vehicle and a keyboard-less presentation. Brain–machine interfaces using steady-state visually evoked potentials (SSVEPs) show promise in therapeutic applications. With a combination of innovations in flexible and soft electronics and in deep learning approaches to classify potentials from two channels and from any subject, a compact, wireless and universal SSVEP interface is designed. Subjects can operate a wheelchair in real time with eye movements while wearing the new brain–machine interface.
AbstractList	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 classification methods require training on a per-subject or per-session basis. Here, we introduce a fully portable, wireless, flexible scalp electronic system, incorporating a set of dry electrodes and a flexible membrane circuit. Time-domain analysis using convolutional neural networks allows for accurate, real-time classification of steady-state visually evoked potentials in the occipital lobe. Compared to commercial systems, the flexible electronics show the improved performance in detection of evoked potentials due to significant reduction of noise and electromagnetic interference. The two-channel scalp electronic system achieves a high information transfer rate (122.1 ± 3.53 bits per minute) with six human subjects, allowing for wireless, real-time, universal electroencephalography classification for an electric wheelchair, a motorized vehicle and a keyboard-less presentation. Brain–machine interfaces using steady-state visually evoked potentials (SSVEPs) show promise in therapeutic applications. With a combination of innovations in flexible and soft electronics and in deep learning approaches to classify potentials from two channels and from any subject, a compact, wireless and universal SSVEP interface is designed. Subjects can operate a wheelchair in real time with eye movements while wearing the new brain–machine interface. 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 classification methods require training on a per-subject or per-session basis. Here, we introduce a fully portable, wireless, flexible scalp electronic system, incorporating a set of dry electrodes and a flexible membrane circuit. Time-domain analysis using convolutional neural networks allows for accurate, real-time classification of steady-state visually evoked potentials in the occipital lobe. Compared to commercial systems, the flexible electronics show the improved performance in detection of evoked potentials due to significant reduction of noise and electromagnetic interference. The two-channel scalp electronic system achieves a high information transfer rate (122.1 ± 3.53 bits per minute) with six human subjects, allowing for wireless, real-time, universal electroencephalography classification for an electric wheelchair, a motorized vehicle and a keyboard-less presentation.Brain–machine interfaces using steady-state visually evoked potentials (SSVEPs) show promise in therapeutic applications. With a combination of innovations in flexible and soft electronics and in deep learning approaches to classify potentials from two channels and from any subject, a compact, wireless and universal SSVEP interface is designed. Subjects can operate a wheelchair in real time with eye movements while wearing the new brain–machine interface.
Author	Mzurikwao, Deogratias Duarte, Audrey Mahmood, Musa Herbert, Robert Yeo, Woon-Hong Mishra, Saswat Kim, Yun-Soung Lee, Yongkuk Ang, Chee Siang
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Snippet	Variation in human brains creates difficulty in implementing electroencephalography into universal brain–machine interfaces. Conventional...
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SubjectTerms	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
Title	Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm
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