Research of Classification Methods of EEG Signal Based on Wavelet Packet Transform and LVQ Neural

In order to improve the correct recognition rate of EEG(Electroencephalogram, EEG) signals to meet the needs of Brain-Computer Interface system, this paper put forward a new method of signal recognition which combines wavelet packet decomposition and LVQ neural network. First, using the method of wavelet packet to analyze the signal, and then extract the specific frequency band's energy of wavelet packet as characteristics. Then using the LVQ neural network model to study the distinguishing between the two EEG datas of Motor Imagery. The simulation experiment uses Matlab software to design LVQ neural network model to judge the two kinds of Motor Imagery task. In the process of judgment, respecti -vely to classify the data by using BP neural network and LVQ neural network. Experimental results show that the LVQ neural network can have a higher correct accuracy to recognize the motor imaginary task than BP neural.