Smoothing spline analysis of variance models for electroencephalography data analysis

Speaker: Nathaniel Helwig

Date: June 6, 2013

Time: 4:00 pm – 5:00 pm

Location: 122 Illini Hall (Conference Room)

Sponsor: Statistics Department

Electroencephalography (EEG) data consist of electrical activity recorded over time from electrodes on the scalp. Typical EEG studies collect multi-electrode data from many subjects in attempt to compare differences in brain activity between different subject groups. In this talk, I discuss how smoothing spline analysis of variance (SSANOVA) models can be used to reliably analyze differences EEG data. First, I provide an overview of the SSANOVA framework and discuss the model”s large sample and dimensionality issues. Next, I present some approximations for fitting tensor-product SSANOVA models to large samples. Then, I discuss how two- and three-way SSANOVA models can be used to reliably compare differences in single- or multi-electrode EEG data. I conclude by discussing some methodological and substantive extensions of this work.