A New Approach for Classification of EEG Signals

Abstract

This study presents a comparative study of the classification accuracy and speed of performance of epileptic Electroensefalogram (EEG) signals using a traditional neural network architecture based on backpropagation training algorithm, and a new neural network. The proposed network is called adaptive neural network with activation function (AAF-NN) in which adjustable parameters, It is used two different activation functions for developed study. One of theese adaptive activation functions is sigmoid function with free parameters and the other one is sum of sinusoidal function with free parameters and sigmoid function with free parameters. The adaptive activation function with free parameters is used in the hidden layer for the proposed structures based on the feed-forward neural network Experimental results have revealed that neural network with adaptive activation function is more suitable for classification EEG signals and training speed is much faster than traditional neural network with fixed sigmoid activation function.