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Coupling among electroencephalogram gamma signals on a short time scale.

McAssey MP, Hsieh F, Smith AC - Comput Intell Neurosci (2010)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California Davis, MSB 4118 One Shields Avenue, Davis, CA 95616, USA.

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.

Show MeSH
Means of the subsets of IC estimates corresponding to each of the four IC states, with the base signal at Tetrode 1, based on optimized mixed MVB model.
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Related In: Results  -  Collection


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fig9: Means of the subsets of IC estimates corresponding to each of the four IC states, with the base signal at Tetrode 1, based on optimized mixed MVB model.

Mentions: We plot the group means in Figure 9.


Coupling among electroencephalogram gamma signals on a short time scale.

McAssey MP, Hsieh F, Smith AC - Comput Intell Neurosci (2010)

Means of the subsets of IC estimates corresponding to each of the four IC states, with the base signal at Tetrode 1, based on optimized mixed MVB model.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2926578&req=5

fig9: Means of the subsets of IC estimates corresponding to each of the four IC states, with the base signal at Tetrode 1, based on optimized mixed MVB model.
Mentions: We plot the group means in Figure 9.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California Davis, MSB 4118 One Shields Avenue, Davis, CA 95616, USA.

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.

Show MeSH