Electrophysiological Mapping and Source Imaging

Abstract

In this chapter, we present the physical and physiological basics behind EEG and MEG signal generation and propagation. We first start by presenting the biophysical principles that explain how the coordinated movements of ions inside and outside neuronal cells result in macroscale phenomena at the scalp, such as electric potentials recorded by EEG and magnetic fields sensed by MEG. These physical principles enforce EEG and MEG signals to have specific spatial and temporal features, which can be used to study brain’s response to internal and external stimuli. We continue our exploration by developing a mathematical framework within which EEG and MEG signals can be computed if the distribution of underlying brain sources is known, a process called forward problem. We further continue to discuss methods that attempt the reverse, i.e., solving for underlying brain sources given scalp measurements such as EEG and MEG, a process called source imaging. We will provide various examples of how electrophysiological source imaging techniques can help study the brain during its normal and pathological states. We will also briefly discuss how combining electrophysiological signals from EEG with hemodynamic signals from functional magnetic resonance imaging (fMRI) helps improve the spatiotemporal resolution of estimates of the underlying brain sources, which is critical for studying spatiotemporal processes within the brain. The goal of this chapter is to provide proper physical and physiological intuition and biophysical principles that explain EEG/MEG signal generation, its propagation from sources in the brain to EEG/MEG sensors, and how this process can be inverted using signal processing and machine learning techniques and algorithms.