Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Comparative Analysis of Motor Imagery, Execution, and Observation for Motor Imagery-based Brain-Computer Interface

움직임 상상 기반 뇌-컴퓨터 인터페이스를 위한 운동 심상, 실행, 관찰 뇌파 비교 분석

  • Daeun, Gwon (Department of Computer Science and Electrical Engineering, Handong Global University) ;
  • Minjoo, Hwang (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Jihyun, Kwon (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Yeeun, Shin (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Minkyu, Ahn (Department of Computer Science and Electrical Engineering, Handong Global University)
  • 권다은 (한동대학교 전산전자공학과) ;
  • 황민주 (한동대학교 전산전자공학부) ;
  • 권지현 (한동대학교 전산전자공학부) ;
  • 신예은 (한동대학교 전산전자공학부) ;
  • 안민규 (한동대학교 전산전자공학과)
  • Received : 2022.10.19
  • Accepted : 2022.11.18
  • Published : 2022.12.31
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Abstract

Brain-computer interface (BCI) is a technology that allows users with motor disturbance to control machines by brainwaves without a physical controller. Motor imagery (MI)-BCI is one of the popular BCI techniques, but it needs a long calibration time for users to perform a mental task that causes high fatigue to the users. MI is reported as showing a similar neural mechanism as motor execution (ME) and motor observation (MO). However, integrative investigations of these three tasks are rarely conducted. In this study, we propose a new paradigm that incorporates three tasks (MI, ME, and MO) and conducted a comparative analysis. For this study, we collected Electroencephalograms (EEG) of motor imagery/execution/observation from 28 healthy subjects and investigated alpha event-related (de)synchronization (ERD/ERS) and classification accuracy (left vs. right motor tasks). As result, we observed ERD and ERS in MI, MO and ME although the timing is different across tasks. In addition, the MI showed strong ERD on the contralateral hemisphere, while the MO showed strong ERD on the ipsilateral side. In the classification analysis using a Riemannian geometry-based classifier, we obtained classification accuracies as MO (66.34%), MI (60.06%) and ME (58.57%). We conclude that there are similarities and differences in fundamental neural mechanisms across the three motor tasks and that these results could be used to advance the current MI-BCI further by incorporating data from ME and MO.

Keywords

Acknowledgement

본연구는과학기술정보통신부와정보통신기획평가원의소프트웨어중심대학지원사업(2017-0-00130), 한국연구재단(No. 2021R1I1A3060828) 및 과학기술정보통신부와 한국여성과학기술인육성재단의 과학기술정보통신부 여대학원생공학연구팀제 지원사업(WISET-2022-118) 과제의 지원을 받아 수행하였음.

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