Lyapunov exponents/probabilistic neural networks for analysis of EEG signals

A new approach based on the implementation of probabilistic neural network (PNN) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the EEG signals. Decision making was performed in two stages: computation of Lyapunov exponents as feature vectors and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of Lyapunov exponents and the PNN. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the EEG signals and the PNN trained on these features achieved high classification accuracies.