Aprimoramento da resolução espacial do EEG: um modelo computacional baseado no algoritmo eLORETA

Abstract

Electroencephalography (EEG) presents excellent temporal resolution, but suffers from spatial resolution limitations due to the nature of the brain volume conductor and the distance between the electrodes and the cortex. To circumvent this limitation, this work proposes and validates a model based on the eLORETA (Exact Low Resolution Brain Electromagnetic Tomography) algorithm to solve the Inverse Problem, focusing on the three-dimensional tracking of neural sources under electrotactile somatosensory stimulation. The pipeline developed was structured into three main stages: two-dimensional statistical inference, computational simulation of the Forward Problem, and spatial reconstruction on real data. Preliminary statistical analysis (ANOVA) confirmed that high-frequency tactile stimuli (125 Hz) elicit the P300 complex with greater amplitude and signal-to-noise ratio, optimizing the input matrices. During synthetic algorithmic validation, the model demonstrated a high capacity for multiple source separability, maintaining low-magnitude localization errors (restricted to distances of 1 to 2 voxels) even under acute 50\% white noise degradation. In the application on real biological data extracted from 10 volunteers using a 60-channel array, the software successfully isolated the anatomophysiological macroregion in the somatosensory cortex, suppressing the spatial scattering typical of surface EEG. Performance analysis further revealed that the adopted parametric mesh (5 mm and 12,213 voxels) established an optimal balance between spatial detail and memory processing viability. It is concluded that the consolidated system establishes a robust computational analytical tool, ensuring high precision in feature extraction for advanced electrophysiological data processing.