Abstract
The study mainly focuses on the analysis of Electroencephalogram, to classify mental tasks by using features based on wavelet transform. We have used the daubechies family wavelets, level 6,to transform obtained signal from EEG signal. Local features can be described well with wavelets that have extent in EEG. This offers improved features to the neural networks obtaining several classified mental tasks. Through several processes, it led us more developed variety mental tasks classification results. We find that the neural networks perform over 75% success resulting with small number of electrodes better than a previous 70% resulting.
