title: Coupling among electroencephalogram gamma signals on a short time scale.
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.
authors.affiliation: Department of Statistics, University of California Davis, MSB 4118 One Shields Avenue, Davis, CA 95616, USA.
authors.forename: Michael P
authors.initials: MP
authors.lastname: McAssey
authors.forename: Fushing
authors.initials: F
authors.lastname: Hsieh
authors.forename: Anne C
authors.initials: AC
authors.lastname: Smith
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D058256:Brain Waves / Q000502:physiology*; D003198:Computer Simulation; D003344:Cortical Synchronization / Q000502:physiology; D004566:Electrodes / Q000592:standards; D004569:Electroencephalography / Q000379:methods*; D006624:Hippocampus / Q000502:physiology; D006801:Humans; D009474:Neurons / Q000502:physiology; D051381:Rats; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
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