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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.

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Scaled difference in average frequency between X and Y throughout the epoch.
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fig2: Scaled difference in average frequency between X and Y throughout the epoch.

Mentions: We subtract the observed frequency of Y from that of X, resulting in a difference which ranges in absolute value between 0 Hz and 130 Hz, with a median of 120 Hz. Then, so that we will be able to plot the computed IC alongside the frequency difference and to make comparisons, we divide the difference in observed frequencies by 130 in order to place the difference on a scale of −1 to 1. A plot of this normalized frequency difference is displayed in Figure 2. The computed IC between X and Y should be equal to one at points where this plot crosses the horizontal axis, as it is around these points (plus or minus a small lag) that the two simulated gamma signals become synchronized. The IC should fall to zero elsewhere. We note that the duration of the synchronization in this simulation does not necessarily last for several cycles, as is assumed for the EEG gamma signals. Again, the benefits that we intend to demonstrate in this simulation study will be carried over to the EEG data analysis without requiring us to mimic the lengthier duration of synchrony.


Coupling among electroencephalogram gamma signals on a short time scale.

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

Scaled difference in average frequency between X and Y throughout the epoch.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Scaled difference in average frequency between X and Y throughout the epoch.
Mentions: We subtract the observed frequency of Y from that of X, resulting in a difference which ranges in absolute value between 0 Hz and 130 Hz, with a median of 120 Hz. Then, so that we will be able to plot the computed IC alongside the frequency difference and to make comparisons, we divide the difference in observed frequencies by 130 in order to place the difference on a scale of −1 to 1. A plot of this normalized frequency difference is displayed in Figure 2. The computed IC between X and Y should be equal to one at points where this plot crosses the horizontal axis, as it is around these points (plus or minus a small lag) that the two simulated gamma signals become synchronized. The IC should fall to zero elsewhere. We note that the duration of the synchronization in this simulation does not necessarily last for several cycles, as is assumed for the EEG gamma signals. Again, the benefits that we intend to demonstrate in this simulation study will be carried over to the EEG data analysis without requiring us to mimic the lengthier duration of synchrony.

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