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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
Simulated EEG gamma signals X(t) (top) and Y(t) (first two seconds).
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Related In: Results  -  Collection


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fig1: Simulated EEG gamma signals X(t) (top) and Y(t) (first two seconds).

Mentions: (13)X(t)=sin{2π[70+10sin(0.5πt)]t},Y(t)=sin{2π[50+10sin(0.5π(t−2))]t}, where t varies from 0 to 20 seconds at a resolution of 1500 points per second. This resolution mimics that of the real EEG data. A plot of the first two seconds of this signal pair is shown in Figure 1. We intend to show that, using a variable window, with the parameter w well-tuned, the IC computation is close to one when the two signals are at about the same frequency, plus or minus a small lag, and close to zero otherwise.


Coupling among electroencephalogram gamma signals on a short time scale.

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

Simulated EEG gamma signals X(t) (top) and Y(t) (first two seconds).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Simulated EEG gamma signals X(t) (top) and Y(t) (first two seconds).
Mentions: (13)X(t)=sin{2π[70+10sin(0.5πt)]t},Y(t)=sin{2π[50+10sin(0.5π(t−2))]t}, where t varies from 0 to 20 seconds at a resolution of 1500 points per second. This resolution mimics that of the real EEG data. A plot of the first two seconds of this signal pair is shown in Figure 1. We intend to show that, using a variable window, with the parameter w well-tuned, the IC computation is close to one when the two signals are at about the same frequency, plus or minus a small lag, and close to zero otherwise.

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