보행 관련 뇌파의 신호원 추정을 위한비통합 데이터 분석 방법
A non-merging data analysis method to localize brain source for gait-related EEG
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초록
보행능력은 의학적으로 다양한 뇌신경계 질환에서 사용되는 평가 지표이다. 보행에 관련된 뇌 활성화를 측정하고 분석하는방법으로 뇌파 데이터에 대해 독립성분을 추출한 뒤 신호원 추정 및 시간-주파수 분석이 주로 사용된다. 기존의 트레드밀 기반보행 뇌파 분석은 분할 측정한 뒤, 데이터를 병합하여 신호 전처리, 독립성분분석 및 신호원 추정을 수행하고 피험자 간 군집화를 통하여 대표 성분 클러스터들을 추출한다. 본 연구에서는 보행 뇌파에 대하여 데이터 통합 없이 각각의 분할 측정된 데이터에 대하여 개별적으로 신호 전처리, 독립성분분석 및 신호원 추정을 수행하고 모든 피험자로부터 추정된 독립성분에 대하여 피험자 간 군집화를 수행하는 새로운 방법을 제안한다. 데이터 통합이 독립성분 군집화 및 시간-주파수 분석에 미치는 영향을 조사하기 위해 기존의 통합 데이터에 기반한 두 가지 분석 방법과 본 연구에서 제안하는 데이터 통합이 없는 분석 방법을 비교하였다. 그 결과, 통합 데이터 방법들에서는 각각 2개씩의 성분 클러스터를 도출하였으나 제안하는 방법을 통해 4개의 성분 클러스터를 도출, 적은 피험자 수에도 불구하고 세분화된 보행 뇌 신호 성분 클러스터를 도출할 수 있었음을 확인하였다.
Abstract
Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizingand time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as amethod of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performssignal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEGmeasurements, and extracts representative component clusters through inter-subject clustering. In this study we propose ananalysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on eachmeasurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging onthe IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. Asa result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed“non-merging” method (4 clusters) despite the small number of subjects, than conventional method (2 clusters)
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