Abstract
Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain–machine interfaces. Conventional electroencephalography (EEG) for motor imagery requires a hair cap with multiple wired electrodes and messy gels, causing motion artifacts. Here, a wireless scalp electronic system with virtual reality for real-time, continuous classification of motor imagery brain signals is introduced. This low-profile, portable system integrates imperceptible microneedle electrodes and soft wireless circuits. Virtual reality addresses subject variance in detectable EEG response to motor imagery by providing clear, consistent visuals and instant biofeedback. The wearable soft system offers advantageous contact surface area and reduced electrode impedance density, resulting in significantly enhanced EEG signals and classification accuracy. The combination with convolutional neural network-machine learning provides a real-time, continuous motor imagery-based brain–machine interface. With four human subjects, the scalp electronic system offers a high classification accuracy (93.22 ± 1.33% for four classes), allowing wireless, real-time control of a virtual reality game.
Original language | English |
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Article number | 2101129 |
Journal | Advanced Science |
Volume | 8 |
Issue number | 19 |
DOIs | |
Publication status | Published - 2021 Oct 6 |
Bibliographical note
Publisher Copyright:© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH.
All Science Journal Classification (ASJC) codes
- Medicine (miscellaneous)
- General Chemical Engineering
- General Materials Science
- Biochemistry, Genetics and Molecular Biology (miscellaneous)
- General Engineering
- General Physics and Astronomy