MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

Abstract

This paper is focused on optimizing EEG source localization of visual neural activities generated in the posterior lobe of the human brain. The visual and neural systems of the human brain process the captured images of faces or scenes into optical and chemical neurons in order to produce electrical potentials over the scalp surface, where EEG electrodes measure these signals to sense the underneath visual brain activity. However, it is categorically hard to localize the true neural sources in the human brain's visual cortex due to overlapping and interaction of other active areas of the lobes of the brain. Thus, a novel algorithm of MSP inversion-based Bayesian framework with varying patches for providing the optimal free energy and minimum location in the visual cortex is proposed to address this issue. This algorithm is integrated with a synthetic EEG dataset generation scheme to validate active neural sources. This proposed algorithm provides satisfactory results in terms of optimal free energy with minimum localization error and validates true active sources in the brain. The SPM12 Toolbox is applied in processing the visual EEG dataset in this research. The application of this proposed algorithm is beneficial in terms of localizing the optimum visual sources and identifying the visual disorders or diseases in the human brain.