ANALYSIS OF EEG SIGNALS BY THE CONTINUOUS WAVELET TRANSFORM

Abstract

We propose the analysis of the sleep EEG, the forecasting of epileptic seizures from the EEG signal, and the classification of EEG signal during mental tasks using BCI system. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The mental tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. In BCI system, the architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved 95% classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks, And this research can be reduce doctor's labors and realize quantitative diagnosis of EEG. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

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