Classification of high-dimensional electroencephalography data with location selection using structured spike-and-slab prior

Publication
Statistical Analysis and Data Mining: The ASA Data Science Journal

Statistical Analysis and Data Mining: The ASA Data Science Journal (2020).

Abstract:

With the advent of modern technologies, it is increasingly common to deal with data of large dimensions in various scientific fields of study. In this paper, we develop a Bayesian approach for the classification of multi-subject high-dimensional electroencephalography (EEG) data. In this EEG data, we have a matrix of covariates corresponding to each subject from either the alcoholic or control group. The matrix covariates have a natural spatial correlation based on the locations of the brain, and temporal correlation as the measurements are taken over time. We employ a divide and conquer strategy by building multiple local Bayesian models at each time point separately. We incorporate the spatial structure through the structured spike-and-slab prior, which has inherent variable selection properties. The temporal structure is incorporated within the prior by learning from the local model from the previ- ous 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.

Avatar
Shariq Mohammed

My research interests include Bayesian modeling, variable selection, data integration - cancer imaging genomics, statistical methods for medical imaging, precision health.

Related