Wavelet-package transformation as a preprocessor of EEG waveforms for classification

The results of this paper show that preprocessing of an EEG signal with wavelet packet transformation, both enhances the feature detection capability of a classifier and reduces the input vectors dimensions considerably. The best basis method gave perfect classification with the hold-out method and would be considered to be the best method used in the experiment. It shows that the selection of the packets for the feature vector can be selected with best basis criterions like the minimum entropy criteria. There are few things though that could explain this results. First the results are one shot results, a process like Monte Carlo was not used mainly because of low availability of training samples. The results are not either an average of random selections for the training and test samples, so the way the samples were split up could make a difference. Wavelet packet transformation has shown itself to be a powerful tool in preprocessing of feature vectors for classification. The classifier does not have to be statistical, it could also be a neural network or any other pattern recognition system