EEG signal denoising based on wavelet transform

It is necessary to increase the feature extraction capability of the classifier by applying wavelet transformation to the EEG signals. In this paper the use of wavelet transformation as a preprocessing tool for EEG signal de-noising is examined and a novel threshold is proposed for wavelet threshold de-noising method. First, hard threshold, soft threshold and the proposed threshold are used for de-noising and decomposing of the EEG data. And then the wavelet coefficient is used as extracted feature set and is fed to a probabilistic neural network classifier to organize the EEG signals into different activities. At last the effectiveness of de-noising with different thresholds is examined by the classification result. It is shown that the novel threshold is better than the last two thresholds for EEG signal de-noising based on wavelet transform.