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Complexity measures of brain wave dynamics

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Abstract

To understand the nature of brain dynamics as well as to develop novel methods for the diagnosis of brain pathologies, recently, a number of complexity measures from information theory, chaos theory, and random fractal theory have been applied to analyze the EEG data. These measures are crucial in quantifying the key notions of neurodynamics, including determinism, stochasticity, causation, and correlations. Finding and understanding the relations among these complexity measures is thus an important issue. However, this is a difficult task, since the foundations of information theory, chaos theory, and random fractal theory are very different. To gain significant insights into this issue, we carry out a comprehensive comparison study of major complexity measures for EEG signals. We find that the variations of commonly used complexity measures with time are either similar or reciprocal. While many of these relations are difficult to explain intuitively, all of them can be readily understood by relating these measures to the values of a multiscale complexity measure, the scale-dependent Lyapunov exponent, at specific scales. We further discuss how better indicators for epileptic seizures can be constructed.

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Acknowledgments

This work is partially supported by US NSF grants CMMI-1031958 and 0826119.

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Authors and Affiliations

  1. PMB Intelligence LLC, P.O. Box 2077, West Lafayette, IN, 47996, USA

    Jianbo Gao

  2. Affymetrix, 3380 Central Express Way, Santa Clara, CA, USA

    Jing Hu

  3. Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, IN, 47907, USA

    Wen-wen Tung

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  1. Jianbo Gao
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  2. Jing Hu
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  3. Wen-wen Tung
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Correspondence to Jing Hu.

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Gao, J., Hu, J. & Tung, Ww. Complexity measures of brain wave dynamics. Cogn Neurodyn 5, 171–182 (2011). https://doi.org/10.1007/s11571-011-9151-3

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