Skip to main content

Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery-based Brain-Machine Interfaces

Mahmood, Musa, Kwon, Shinjae, Kim, Yun-Soung, Siriaraya, Panote, Choi, Jeongmoon, Boris, Otkhmezuri,, Kang, Kyowon, Jun Yu, Ki, Jang, Young C, Ang, Chee Siang, and others. (2021) Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery-based Brain-Machine Interfaces. Advanced Science, . Article Number 2101129. ISSN 2198-3844. (doi:10.1002/advs.202101129) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:88354)

PDF Publisher pdf
Language: English

Restricted to Repository staff only

Contact us about this Publication
[thumbnail of advs.202101129.pdf]
PDF Author's Accepted Manuscript
Language: English

Restricted to Repository staff only
Contact us about this Publication
[thumbnail of 0. Revised Manuscript.pdf]
PDF Supplemental Material
Language: English

Restricted to Repository staff only
Contact us about this Publication
[thumbnail of 2. SI.pdf]
Official URL
http://dx.doi.org/10.1002/advs.202101129

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, we introduce a wireless scalp electronic system with virtual reality for real-time, continuous classification of motor imagery brain signals. 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.

Item Type: Article
DOI/Identification number: 10.1002/advs.202101129
Subjects: Q Science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Jim Ang
Date Deposited: 25 May 2021 11:31 UTC
Last Modified: 17 Aug 2021 10:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/88354 (The current URI for this page, for reference purposes)
Ang, Chee Siang: https://orcid.org/0000-0002-1109-9689
  • Depositors only (login required):