EEG Signal Recognition Based on Wavelet Transform and ACCLN Network

Abstract: The electroencephalogram(EEG) is a record of brain activity. Brain Computer Interface (BCI) technology has become one of the hotspots at present, especially for the identification of EEG characteristic signals. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the raw signal is preprocessed by the wavelet denoising method because the raw EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, The feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and tested by 100 sets of samples in different states. The experimental results show that the accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively. The results show the effectiveness and feasibility of the proposed method.