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Activity Number:
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74
- Invited E-Poster Session I
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Type:
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Invited
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Date/Time:
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Sunday, August 7, 2022 : 8:30 PM to 9:25 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #322753
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Title:
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Classification of High-Dimensional Electroencephalography Data with Location Selection Using Structured Spike-and-Slab Prior
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Author(s):
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Shariq Mohammed* and Dipak K Dey and Yuping Zhang
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Companies:
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Boston University and University of Connecticut and University of Connecticut
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Keywords:
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Bayesian variable selection;
Gibbs sampling;
neuroimaging data;
slice sampling;
spatial clustering;
spatio-temporal
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Abstract:
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We present a Bayesian approach for the classification of multi-subject high-dimensional electroencephalography (EEG) data. Each subject belongs to either the alcoholic or control group and the covariates have a natural spatial correlation based on the locations of the brain, and temporal correlation as the measurements are taken over time. We build local models at each time point and incorporate the spatial structure through the structured spike-and-slab prior. The temporal structure is incorporated within the prior by learning from the local model from the previous time point. We pool the information from the local models and use a weighted average to design a prediction method. We perform simulation studies to show the efficiency of our approach and demonstrate the local Bayesian modeling with a case study on EEG data.
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Authors who are presenting talks have a * after their name.
