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Title: EEG spectral and heritability analysis using a nested Dirichlet process Authors:  Mark Fiecas - University of Minnesota (United States) [presenting]
Abstract: A novel approach is presented for conducting spectral analysis on resting-state EEG (RS-EEG) data collected from the Minnesota Twin Family Study (MTFS). Typically, spectral analysis methods treat time series from each subject separately, and independent spectral densities are fit to each time series. In certain scenarios, such as our EEG data collected on twins, it is reasonable to assume that time series may have similar underlying characteristics, and borrowing information across subjects can significantly improve estimation. However, there are currently very few methods that share information across subjects when estimating spectral densities. We develop a Bayesian nonparametric modeling approach for estimating EEG spectra. In our methodology, we use Bernstein polynomials and a Dirichlet process (DP) to estimate each subject-specific spectrum. In order to estimate the spectra for the entire sample, we nest this model using a nested DP process. Thus, the top level DP cluster subjects with similar spectral densities and the bottom-level dependent DP fits a functional curve to the subjects within that cluster. We illustrate our methodology by conducting spectral analysis on resting state EEG data collected from the MTFS. The MTFS collected resting-state EEG and behavioral information from 379 monozygotic and 199 dizygotic twin pairs.