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Please use this identifier to cite or link to this item: http://dyuthi.cusat.ac.in/purl/2602

Title: Complexity quantification of dense array EEG using sample entropy analysis
Authors: Pravitha, Ramanand
Nampoori, V P N
Sreenivasan, R
Technology
Keywords: Dense array EEG
brain dynamics
complexity analysis
surrogate data
sample entropy
Issue Date: 2004
Publisher: Imperial College Press
Abstract: n this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.
URI: http://dyuthi.cusat.ac.in/purl/2602
Appears in Collections:Dr. V P N Nampoori

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