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.

Editorial for International Journal of biomedical Data Mining

This issue of the International Journal of Biomedical Data Mining presents two contributed articles. The first article, entitled Data Inventory for CancerPatients Receiving Radiotherapy for Outcome Analysis and Modeling, authored by Jason Vickress, Rob Barnett and Slav Yartsev, describes a database created for storing and analyzing patient specific data related to pre-treatment condition, treatment planning, and treatment outcomes, for patients receiving radiotherapy based cancer treatment. 

The proposed database can perform automated analysis regarding quality assurance, dose accumulation for multiple treatments on different machines and can assist physicians in choosing the optimal radiation therapy for new patients. The second article, entitled Likelihood Ratio Test ofHardy-Weinberg Equilibrium Using Uncertain Genotypes for Sibship Data, authoredby Qiong Li, Helene Massam and Xin Gao, is concerned with the problem of testing for Hardy-Weinberg equilibrium of genotype frequencies in the area of population genetics.