Mapping and Localizing EEG/MEG Sources on the Cortical Surface Through a Spatiotemporal Modality

Abstract

Electro/Magnetoencephalography (EEG/MEG) tools are well suited for studies involving fast activation sequences of human cortical functions in real-life situations, and it also allows comparison of timing between distinct brain regions. In this talk, a statistical framework for electromagnetic spatiotemporal independent component analysis (EMSICA) using explicit and implicit constraints (cortically constrained and spatio/temporally independent sources with sparse priors) will be presented to separate the recorded mixture of EEG features and to effectively isolate neurophysiologically and neuroanatomically meaningful components on the cortical surface. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus. If time permits, our ongoing improvement method that uses Gaussian Mixture modeling will also be briefly mentioned.