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Computational Intelligence and Neuroscience
Volume 2010 (2010), Article ID 946089, 12 pages
http://dx.doi.org/10.1155/2010/946089
Research Article

Coupling among Electroencephalogram Gamma Signals on a Short Time Scale

1Department of Statistics, University of California Davis, MSB 4118 One Shields Avenue, Davis, CA 95616, USA
2Department of Anesthesiology and Pain Medicine, University of California TB-170, One Shields Avenue, Davis, CA 95616, USA

Received 16 October 2009; Revised 22 March 2010; Accepted 16 June 2010

Academic Editor: Francois Vialatte

Copyright © 2010 Michael P. McAssey et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An important goal in neuroscience is to identify instances when EEG signals are coupled. We employ a method to measure the coupling strength between gamma signals (40–100 Hz) on a short time scale as the maximum cross-correlation over a range of time lags within a sliding variable-width window. Instances of coupling states among several signals are also identified, using a mixed multivariate beta distribution to model coupling strength across multiple gamma signals with reference to a common base signal. We first apply our variable-window method to simulated signals and compare its performance to a fixed-window approach. We then focus on gamma signals recorded in two regions of the rat hippocampus. Our results indicate that this may be a useful method for mapping coupling patterns among signals in EEG datasets.