Title: Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG
Authors: Tugce Balli; Ramaswamy Palaniappan
Addresses: School of Engineering and Architecture, Istanbul Kemerburgaz University, Istanbul, Turkey ' Department of Engineering, School of Technology, University of Wolverhampton, Room SC035, Shifnal Road, Priorslee, TF2 9NT Telford, UK
Abstract: This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings.
Keywords: electroencephalogram; EEG signals; nonlinearity; surrogate data; approximate entropy; voluntary finger movement; nonlinear dynamic structures.
DOI: 10.1504/IJMEI.2013.053327
International Journal of Medical Engineering and Informatics, 2013 Vol.5 No.2, pp.103 - 116
Published online: 28 Jan 2014 *
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