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

Musa Mahmood, Shinjae Kwon, Hojoong Kim, Yun Soung Kim, Panote Siriaraya, Jeongmoon Choi, Boris Otkhmezuri, Kyowon Kang, Ki Jun Yu, Young C. Jang, Chee Siang Ang, Woon Hong Yeo

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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 languageEnglish
Article number2101129
JournalAdvanced Science
Volume8
Issue number19
DOIs
StatePublished - 6 Oct 2021
Externally publishedYes

Keywords

  • brain–machine interfaces
  • motor imagery brain signals
  • virtual reality system
  • wireless soft scalp electronics

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