BRAIN MATURATION ESTIMATION USING NEURAL CLASSIFIER

Quantitative electroencephalographic (EEG) signal analysis has reveale d itself as an important diagnostic tool in the last few years. Throug h the use of signal processing techniques, new quantitative representa tions of EEG data are obtained. To automate the diagnosis, a problem o f supervised classification must he solved on these, Artificial Neural Networks provide an alternative to more traditional classifier system s for this task. The objective of this paper is to perform a compariso n between several classifiers in a particular problem, the brain matur ation prediction. The data preprocessing/feature extraction process an d the methodology for making the comparison are described. Performance of the methods is evaluated in terms of estimated percentage of corre ctly classified subjects.