Application of neutral network by EEG signal classification

Abstract

Analysis of long-term EEG requires that it is segmented into piece-wise stationary sections and classified. Neural network architecture is introduced for the problem of classification of EEG signals. This paper deals with basic signal classification into two classes. This work is a ground towards creating an algorithm to sleep status analysis. Signal is first worked by signal segmentation and then is used a neural network to classification into two class.