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http://dx.doi.org/10.7471/ikeee.2021.25.4.679

A non-merging data analysis method to localize brain source for gait-related EEG  

Song, Minsu (Dept. of Medical Device, Daegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery and Materials)
Jung, Jiuk (Dept. of Medical Device, Daegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery and Materials)
Jee, In-Hyeog (School of Electronics Engineering, Kyungpook National University)
Chu, Jun-Uk (Dept. of Medical Device, Daegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery and Materials)
Publication Information
Journal of IKEEE / v.25, no.4, 2021 , pp. 679-688 More about this Journal
Abstract
Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a 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).
Keywords
Electroencephalogram; gait; independent component analysis; dipole fitting; clustering;
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