EEG Signals classification using linear and non-linear discriminant methods

Abstract

This article was developed with the particular interest of characterize and study EEG signals as a pattern which in general has a high dimensionality, and has obviously a particular behavior in frequency and time. Here we have developed a wavelet decomposition to reduce a little bit the dimensionality and PCA (Principal Components Analysis) to accurate the result in a better way (only two features representation). After that the EEG signals, with their respective characteristics and representation has been able to train and test some linear and non-linear classifiers such as (Parzen, k-NN, Radial Basis Neural Network, linear and non-linear perceptron and so on.) This evaluation is an analysis of general EEG’s behavior signals with this kind of characterization and classification processes respectively