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Activity Number:
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566
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304267 |
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Title:
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Coherence Analysis of EEG Signals: A Nonparametric Likelihood Approach
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Author(s):
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Suddhasatta Acharyya*+ and Hernando Ombao
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Companies:
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Novartis Pharmaceuticals and Brown University
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Address:
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, Florham Park, NJ, 07932,
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Keywords:
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coherence ; brain-signal ; nonparametric-likelihood
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Abstract:
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Coherence is a measure used to characterize linear dependence between a pair of EEG signals. Inference relies on the assumption of asymptotic Gaussian distributions which could be problematic when the length of the time series is not sufficiently large and when the noise innovations come from distributions with heavy tails. Nonparametric likelihood-based methods have provided competitive alternatives to standard parametric approaches for many problems. The application of such methods to the spectral domain has not been well-studied nor their usefulness assessed in the context of analyzing biological signals. We shall develop a nonparametric likelihood method and apply it to EEG signals with the goal of comparing coherence between pre and post stimulus in a motor task experiment.
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