Scalable spatio-temporal Bayesian analysis of high-dimensional electroencephalography data

We present a scalable Bayesian modelling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. More specifically, we deal with multi-subject electroencephalography (EEG) data with a binary response distinguishing between alcoholic and control gr...

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Bibliographic Details
Published in:	Canadian journal of statistics Vol. 49; no. 1; pp. 107 - 128
Main Authors:	MOHAMMED, Shariq, DEY, Dipak K.
Format:	Journal Article
Language:	English
Published:	Hoboken, USA Wiley 01.03.2021 John Wiley & Sons, Inc Wiley Subscription Services, Inc
Subjects:	Alcoholism Algorithms Approximation Bayesian analysis Brain Case studies Classification Clustering Computation Correlation analysis Data Dimensional analysis EEG inverse problem Electroencephalography Estimation Feature selection Gibbs sampling Kronecker product likelihood approximation Localization Matrices Murphy diagram Performance evaluation Scores Simulation Statistical methods Stimuli Stimulus Time variable selection Variables
ISSN:	0319-5724, 1708-945X
Online Access:	Get full text
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