Enhancing Visual Evoked Potentials Detection with Use of Computational Intelligence Tools

The analysis of evoked potentials (EPs) in the electroencephalogram (EEG) is usually inspected visually and demands subjective interpretation of the results. This paper aims at combining an statistical criterion based on the magnitude square multiple coherence (MSMC) estimate with computational intelligence methods in order to estimate the EPs detection rate (DR) using only portions of the frequency spectrum. 

Thus, networks were used to predict the DR in EEG signals of 15 normal subjects during stroboscopic stimulation. The algorithms were designed to receive the spectral information of two, four or six EEG derivations as the input and DR as the output. Our best result shows that the artificial neural networks can estimate DR with correlation coefficient of 0.97 compared with MSMC, even when a reduced amount of spectral information from the data is available.