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ABSTRACT: 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
Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE;
