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Related Experiment Video

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior.

T A A Watanabe1, C J Cellucci, E Kohegyi

  • 1Department of Pharmacology and Physiology, Drexel University, College of Medicine, Philadelphia, Pennsylvania, USA.

Psychophysiology
|May 20, 2003
PubMed
Summary

New symbolic measures analyze electroencephalographic (EEG) and event-related brain potential (ERP) data. These methods overcome limitations in comparing different signal lengths and analyzing multichannel EEG, revealing changes in behavioral states.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Symbolic complexity measures characterize symbol sequence structure.
  • Existing measures have limitations: difficulty comparing signals of different lengths and poor generalization to multichannel data.
  • Electroencephalographic (EEG) and event-related brain potential (ERP) analyses show promise with these methods.

Purpose of the Study:

  • To address limitations of existing symbolic complexity measures.
  • To develop and apply new measures for analyzing single and multichannel EEG.
  • To assess the sensitivity of these measures to behavioral state changes.

Main Methods:

  • Analysis of single and multichannel EEG using signal complexity and algorithmic redundancy.
  • Algorithmic redundancy defined as a sequence-sensitive generalization of Shannon's redundancy.
  • Covariance complexity calculated from the singular value spectrum of multichannel signals.
  • Binary partition of EEG activity about the median.

Main Results:

  • Algorithmic redundancy was insensitive to dataset size but sensitive to behavioral state (eyes open vs. closed).
  • Covariance complexity also proved sensitive to behavioral state changes.
  • Statistical separation between conditions decreased after removing 8- to 12-Hz EEG content but remained significant.
  • The study describes the use of symbolic measures in multivariate signal classification.

Conclusions:

  • Developed symbolic measures overcome limitations of existing methods for EEG analysis.
  • New measures are sensitive to behavioral state changes and applicable to multichannel data.
  • Symbolic measures offer a robust approach for multivariate signal classification in neuroscience.