| pubmed-article:7729844 | pubmed:abstractText | Quantitative electroencephalographic (EEG) signal analysis has revealed itself as an important diagnostic tool in the last few years. Through the use of signal processing techniques, new quantitative representations of EEG data are obtained. To automate the diagnosis, a problem of supervised classification must be solved on these. Artificial Neural Networks provide an alternative to more traditional classifier systems for this task. The objective of this paper is to perform a comparison between several classifiers in a particular problem, the brain maturation prediction. The data preprocessing/feature extraction process and the methodology for making the comparison are described. Performance of the methods is evaluated in terms of estimated percentage of correctly classified subjects. | lld:pubmed |
