The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Independent Component Analysis of EEG and Source Position Estimation

EEG신호의 독립성분 분석과 소스 위치추정

  • 김응수 (대전대학교 공과대학 컴퓨터정보통신공학부)
  • Published : 2002.02.01
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Abstract

The EEG is a time series of electrical potentials representing the sum of a very large number of neuronal dendrite potentials in the brain. The collective dynamic behavior of neural mass of different brain structures can be assessed from EEG with depth electrodes measurements at regular time intervals. In recent years, the theory of nonlinear dynamics has developed methods for quantitative analysis of brain function. In this paper, we considered it is reasonable or not for ICA apply to EEG analysis. Then we applied ICA to EEG for big toe movement and separated the independent components for 15 samples. The strength of each independent component can be represented on the topological map. We represented ICA can be applied for time and spatial analysis of EEG.

뇌파(Electroencephalogram, EEG)는 뇌에서 막대한 수의 뉴런들의 전위차의 합으로 표현되는 시계열 전위차이다. 규칙적인 시간 간격으로 깊이를 가진 전극 측정에 의한 EEG로부터 서로 다른 구조를 가진 뇌에서의 뉴런 집단의 동역학을 평가할 수 있다. 최근에는 비선형 동역학 연구를 통해 뇌 기능 연구를 정량적으로 분석할 수 있는 방법이 개발되고 있다. 본 논문은 뇌파 신호를 분석함에 있어서 독립성분분석(Independent Component Analysis, ICA)의 적합성을 고려해 보았고, 15명의 정상인의 발가락 자극에 대한 EEG 신호에 이를 적용하여 독립 소스들을 분리해 내었다. 또한 Topological Hawing을 이용하여 각각의 독립 소스들의 기여도를 나타내었다. 이를 통하여 EEG에 독립성분분석을 적용함으로써 뇌 활동의 시간적, 공간적 분석이 가능하고 유용함을 나타내었다.

Keywords

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