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QUANTITATIVE CHARACTERIZATION OF THE COMPLEXITY OF MULTICHANNEL HUMAN EEGS
Journal article   Peer reviewed

QUANTITATIVE CHARACTERIZATION OF THE COMPLEXITY OF MULTICHANNEL HUMAN EEGS

P. E Rapp, C. J Cellucci, T. A. A Watanabe and A. M Albano
International journal of bifurcation and chaos in applied sciences and engineering, v 15(5), pp 1737-1744
May 2005
DOI: https://doi.org/10.1142/S0218127405012764

Abstract

consciousness EEG Complexity
In this contribution, eleven different measures of the complexity of multichannel EEGs are described, and their effectiveness in discriminating between two behavioral conditions (eyes open resting versus eyes closed resting) is compared. Ten of the methods were variants of the algorithmic complexity and the covariance complexity. The eleventh measure was a multivariate complexity measure proposed by Tononi and Edelman. The most significant between-condition change was observed with Tononi–Edelman complexity which decreased in the eyes open condition. Of the algorithmic complexity measures tested, the binary Lempel–Ziv complexity and the binary Lempel–Ziv redundancy of the first principal component following mean normalization and normalization against the standard deviation gave the most significant between-group discrimination. A time-dependent generalization of the covariance complexity that can be applied to nonstationary multichannel signals is also described.

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11 citations in Web of Science
12 citations in Scopus

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Domestic collaboration
Web of Science research areas
Mathematics, Interdisciplinary Applications
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