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Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG

Balli, Tugce, Palaniappan, Ramaswamy (2013) Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG. International Journal of Medical Engineering and Informatics, 5 (2). p. 103. ISSN 1755-0653. (doi:10.1504/IJMEI.2013.053327) (KAR id:50392)

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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.

Item Type: Article
DOI/Identification number: 10.1504/IJMEI.2013.053327
Uncontrolled keywords: electroencephalogram; EEG signals; nonlinearity; surrogate data; approximate entropy; voluntary finger movement; nonlinear dynamic structures
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Palaniappan Ramaswamy
Date Deposited: 03 Sep 2015 17:25 UTC
Last Modified: 16 Feb 2021 13:27 UTC
Resource URI: (The current URI for this page, for reference purposes)
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