Event Details

STOR-i Seminar: Mark Fiecas, University of Minnesota, will give a seminar as part of the International Visitor Programme

Mark Fiecas, University of Minnesota, will give a STOR-i seminar as part the International Visitor Programme on Thursday 12th December 3.30-4.30pm in the PSC Lecture Theatre.

A Non-parametric Bayesian Model for Estimating Spectral Densities of Resting-State EEG Twin Data

Abstract: Electroencephalography (EEG) is a non-invasiveneuroimaging modality that captures electrical brain activity many times per second. We seek to estimate power spectra from EEG data that was gathered for 557 adolescent twin pairs through 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. Since the EEG data was collected on twins, it is reasonable to assume that the time series have similar underlying characteristics, so borrowing information across subjects can significantly improve estimation. We propose a Nested Bernstein Dirichlet Prior model to estimate the power spectrum of the EEG signal for each subject. We then leverage the MTFS twin study design to estimate the heritability of EEG power spectra. Our method estimates spectral densities through data driven smoothing of periodograms within and across subjects while requiring minimal user input to tuning parameters. The method also facilitates heritability analyses on features of the estimated spectral density curves such as peak frequency and frequency band power.