Isolation of ventral stream EEG sources using Steady State EEG and Independent Components Analysis

Abstract

Abstract It has long been known that the ventral stream of the visual system contains discrete areas sensitive to object categories, including the Fusiform Face Area (FFA), the Parahippocampal Place Area (PPA) and the Lateral Occipital Complex (LOC). These areas have been well studied using fMRI, but the limited temporal resolution of fMRI has prevented investigation of the temporal dynamics of interactions between them. While event related potentials (ERPs) have high temporal resolution, their limited spatial resolution has hampered the isolation of signals from discrete ventral stream sources, especially non-face areas. Here we explore a possible solution to this problem using a combination of steady state EEG (SSEEG) and Independent Components Analysis (ICA). Our goal is to use ICA to generate a reliable set of spatial filters for ventral stream sources that can be used in ERP paradigms similarly to the way localizer scans are used in fMRI. SSEEG was recorded from 10 subjects while they viewed pictures of objects, faces, scenes, and scrambled objects flashing at a rate of 3.5 hz in a block design. The data for each subject were decomposed using ICA and the scalp distribution and spectral decomposition of each Independent Component (IC) was examined for each stimulus type. After removal of artifactual components, ICs were labeled as face-sensitive, object-sensitive, or scene-sensitive if 1) they showed an increase in power at 3.5 hz and 2) 3.5 hz power was selectively enhanced for a stimulus type. The vast majority of the category-sensitive components had postero-lateral scalp distributions as one would expect from ventral stream sources. All 10 subjects had face-sensitive ICs; object- and scene-sensitive ICs were each seen in 6 subjects. These data suggest that it is possible to isolate ventral stream sources of EEG. Future work will test the reliability and functional validity of category-sensitive ICs. Meeting abstract presented at VSS 2015.