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- W187828540 abstract "Nonlinear time series analysis is a potential method for gaining information regarding the dynamics of complex physiological systems. The human brain is by far one of the most complex systems in nature and the electrical signal from the brain, the electroencephalogram (EEG) has been a subject of nonlinear techniques to delve the dynamics of the brain. This paper deals with the computation of a measure of complexity for dense array EEG signals and the subsequent analysis of various brain conditions. The complexity measure used in the study is the Sample Entropy (SampEn), which quantifies the regularity and ‘randomness’ in a time varying signal arising from a system The present work computes the SampEn of the EEG signal recorded for the three cases of passive, eyes closed condition; mental arithmetic task and for epileptic spike data. The analysis helps in gaining an insight into the nature of complexity variations with brain state. It is observed that while during the mental task only specific brain regions exhibit lowered complexity, the dynamical complexity is very much lowered for the pathological spike condition. KeywordsBrain, EEG, Nonlinear time series analysis, Sample Entropy (SampEn)." @default.
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- W187828540 date "2003-01-01" @default.
- W187828540 modified "2023-09-23" @default.
- W187828540 title "Complexity of Brain Dynamics Inferred from the Sample Entropy Analysis of Electroencephalogram" @default.
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