QUANTITATIVE CHARACTERIZATION OF THE COMPLEXITY OF MULTICHANNEL HUMAN EEGS
| Autor: | Paul E. Rapp, T. A. A. Watanabe, Alfonso M Albano, C. J. Cellucci |
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| Rok vydání: | 2005 |
| Předmět: |
Normalization (statistics)
Multivariate statistics Theoretical computer science medicine.diagnostic_test business.industry Applied Mathematics Binary number Pattern recognition Electroencephalography Covariance Standard deviation Algorithmic complexity Modeling and Simulation Principal component analysis medicine Artificial intelligence business Engineering (miscellaneous) Mathematics |
| Zdroj: | International Journal of Bifurcation and Chaos. 15:1737-1744 |
| ISSN: | 1793-6551 0218-1274 |
| DOI: | 10.1142/s0218127405012764 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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