Effect of sleep deprivation on estimated distributed sources for Scalp EEG signals: A case study on human drivers

Abstract

The Scalp EEG is a large-scale & robust information source about neocortical dynamic functions. In this paper, we analyze a scalp Electro-Encephalogram (EEG) database of 12 human subjects driving on a simulated condition in laboratory, in 11 different stages of fatigue for characterizing the source natures on the cortex surface. In this paper, the Linear Distributed Current Dipole Approach is used. We have used standardized Low Resolution Brain Electromagnetic Tomography (sLORETA) algorithm, which upon construction of a Lead-field Matrix or a Head model consisting of a grid of 10014 voxels, spatially maps the surface data to corresponding corticular dipole sources at each voxel. The information measures such as Renyi, Shannon & Tsallis entropies of the scouts or voxels nearest to specific electrodes are calculated for various subjects & for varying fatigue levels.