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Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery-Based Brain-Machine Interfaces.
Mahmood, Musa; Kwon, Shinjae; Kim, Hojoong; Kim, Yun-Soung; Siriaraya, Panote; Choi, Jeongmoon; Otkhmezuri, Boris; Kang, Kyowon; Yu, Ki Jun; Jang, Young C; Ang, Chee Siang; Yeo, Woon-Hong.
Afiliación
  • Mahmood M; George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Kwon S; Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Kim H; George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Kim YS; Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Siriaraya P; George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Choi J; Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Otkhmezuri B; George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Kang K; Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Yu KJ; School of Computing, University of Kent, Canterbury, Kent, CT2 7NT, UK.
  • Jang YC; School of Biological Sciences, College of Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
  • Ang CS; School of Computing, University of Kent, Canterbury, Kent, CT2 7NT, UK.
  • Yeo WH; School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Adv Sci (Weinh) ; 8(19): e2101129, 2021 10.
Article en En | MEDLINE | ID: mdl-34272934
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Interfaz Usuario-Computador / Encéfalo / Electroencefalografía / Interfaces Cerebro-Computador / Realidad Virtual Límite: Humans Idioma: En Revista: Adv Sci (Weinh) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Interfaz Usuario-Computador / Encéfalo / Electroencefalografía / Interfaces Cerebro-Computador / Realidad Virtual Límite: Humans Idioma: En Revista: Adv Sci (Weinh) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos