Brainwave Classification Based on Wavelet Entropy and Event-Related Desynchronization

For many years brain-computer interfaces research programs have been very popular. The classification algorithm of the EEG signals has attracted much attention. In this paper, an experiment is designed on imaginary left or right hand movements and a new algorithm is proposed to identify different EEG patterns. Butterworth filter has been applied to retrieve the useful signals. Interference noise has been eliminated using a digital filter. The wavelet entropy is treated as one feature in our approach. The adaptive autoregressive model combined with the event-related desynchronization is used in our research to obtain the other feature. Two obtained features reflect time domain and frequency domain features of EEG signal, and will be used by a BP neural network for training. Simulation has been carried out and the results of the simulation show that this approach can restrain noise disturbance and can effectively express mental states. When used in the classification of special events in brain-computer interface, this approach has some advantage. Moreover, it can be used for the online analysis.