Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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EEG Feature Classification for Precise Motion Control of Artificial Hand

의수의 정확한 움직임 제어를 위한 동작 별 뇌파 특징 분류

  • Kim, Dong-Eun (Department of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Yu, Je-Hun (Department of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (Department of Electrical and Electronics Engineering, Chung-Ang University)
  • 김동은 (중앙대학교 전자전기공학과) ;
  • 유제훈 (중앙대학교 전자전기공학과) ;
  • 심귀보 (중앙대학교 전자전기공학과)
  • Received : 2014.12.04
  • Accepted : 2015.02.17
  • Published : 2015.02.25
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Abstract

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

Brain-computer interface 기술은 일상에서 편안한 생활을 위해 다방면으로 연구가 진행 중이다. 본 연구는 3가지 동작의 뇌파특성을 분석하여 의수와 같은 외부기기의 세밀한 동작 제어를 목적으로 한다. 피험자들은 악력기를 쥘 때 (Grip), 손가락만을 움직일 때 (Move), 아무런 동작을 취하지 않을 때 (Relax)의 3가지 동작을 수행하였고, 뇌파를 측정하여 power spectrum analysis와 multi-common spatial pattern 알고리즘으로 특징추출을 수행하였으며, 분류알고리즘인 SVM(support vector machine)으로 뇌파의 특징데이터들을 분류하였다. 실험결과 3개의 다른 동작을 분류한 결과, 실험에 참여한 3명의 피험자 중 2명에게서 Grip 클래스의 분류율이 가장 높은 분류율을 보였다. 본 연구의 결과는 뇌파를 이용하여 의수가 필요한 환자들에게 유용할 것으로 기대한다.

Keywords

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