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Modeling the Evolution of Neurophysiological Signals

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If you have a question about this talk, please contact Mustapha Amrani.

Inference for Change-Point and Related Processes

In recent years, research into analyzing brain signals has dramatically increased, and the rich data sets being collected require more advanced statistical tools and developments in order to perform proper statistical analyses. Consider an experiment where a stimulus is presented many times, and after each stimulus presentation, time series data is collected. The time series data exhibit nonstationary characteristics. Moreover, across stimuli presentation the time series are non-identical and their spectral properties may even change over the course of the experiment. In this talk, we will look at a novel approach for analyzing nonidentical nonstationary time series data. We consider two sources of nonstationarity: 1) within each replicate and 2) across the replications, so that the spectral properties of the time series data are evolving over time within a replicate, and are also evolving over the course of the experiment. We extend the locally stationary time series model t o account for replicated data, with potentially correlated replicates. We analyze a local field potential data set to study how the spectral properties of the local field potentials obtained from the nucleus accumbens and the hippocampus of a monkey evolve over the course of a learning association experiment.

This talk is part of the Isaac Newton Institute Seminar Series series.

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