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Activity Number: 319
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #319928
Title: Inferring Brain Signals Synchronicity from a Sample of EEG Readings
Author(s): Qian Li* and Donatello Telesca and Damla Senturk
Companies: University of California at Los Angeles and University of California at Los Angeles and University of California at Los Angeles
Keywords: Spectral Synchronicity ; EEG Data ; Consensus Clustering ; Time Series Clustering ; Integrative Clustering ; Bayesian Clustering
Abstract:

Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically chal- lenging. While it is statistically appealing to rely on readings from more than one individual, in order to highlight patterns of coordinated brain activities, pooling information across subjects presents with non trivial methodologi- cal problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodolog- ical framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, cluster and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention will be paid to computational issues, with a proposal based on the hybrid combination of machine learnig and Bayesian techniques.


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