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Updated: May 17, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
Analysis of fMRI time-series by entropy measures
Pavol Mikoláš1, Jan Vyhnánek, Antonín Škoch
1Prague Psychiatric Centre, 3rd Faculty of Medicine, Charles University, Prague, Czech Republic. mikolas@pcp.lf3.cuni.cz
Abstract:
Entropy is a measure of information content or complexity. Information-theoretic modeling has been successfully used in various biological data analyses including functional magnetic resonance (fMRI). Several studies have tested and evaluated entropy measures on simulated datasets and real fMRI data. The efficiency of entropy algorithms has been compared to classical methods based on the linear model. Here we explain and summarize entropy algorithms that have been used in fMRI analysis, their advantages over classical methods and their potential use in event-related and block design fMRI.
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