Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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EEG Analysis Following Change in Hand Grip Force Level for BCI Based Robot Arm Force Control

BCI 기반 로봇 손 제어를 위한 악력 변화에 따른 EEG 분석

  • Kim, Dong-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Lee, Tae-Ju (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 김동은 (중앙대학교 전자전기공학부) ;
  • 이태주 (중앙대학교 전자전기공학부) ;
  • 박승민 (중앙대학교 전자전기공학부) ;
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2013.02.10
  • Accepted : 2013.03.26
  • Published : 2013.04.25
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Abstract

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

BCI (Brain Computer Interface)는 인간의 뇌에서 측정된 EEG (Electroencephalogram)를 활용하여 의수와 같은 기기를 조종할 수 있는 좋은 방법 중 하나이다. 본 논문에서는 EEG와 힘과의 관계를 알아보고자 최대수축악력 (MVC)의 25%, 50%, 75%로 3개의 등급으로 나누어 EEG 변화를 측정하였다. 얻어진 EEG data를 FFT와 power spectrum analysis로 ${\alpha}$, ${\beta}$, ${\gamma}$파로 나누어 각 파형의 파워 값을 구한 뒤, 구해진 EEG 파워 값을 PCA와 LDA를 사용하여 특징 추출 및 분류를 하였다. 실험 결과 25%의 악력을 가할 때 보다 75%의 악력 때 더 높은 EEG 파워의 증가를 확인하였고, 왼손과 오른손은 각각 52.03%와 77.7%의 분류율을 나타내었다.

Keywords

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