eCOALIA: Neocortical neural mass model for simulating electroencephalographic signals

Abstract

This paper introduces eCOALIA, a Python-based environment for simulating intracranial local field potentials and scalp electroencephalography (EEG) signals with neural mass models. The source activity is modeled by a novel neural mass model respecting the layered structure of the neocortex. The whole-brain model is composed of coupled neural masses, each representing a brain region at the mesoscale and connected through the human connectome matrix. The forward solution on the electrode contracts is computed using biophysical modeling. eCOALIA allows parameter evolution during a simulation time course and visualizes the local field potential at the level of cortex and EEG electrodes. Advantaged with the neurophysiological modeling, eCOALIA advances the in silico modeling of physiological and pathological brain activity.

Keywords

• Neural mass modeling
• Whole-brain modeling
• Forward solution
• EEG
• Python