An EEG signal recognition algorithm based on sample entropy and BP neural network.

Brain 'Computer Interface (BCI) is an emerging technology for communication between human brain and external devices. The traditional feature extraction method based on time-frequency characteristics cannot reflect the nonlinear characteristics of EEG signals. In order to further improve the accuracy of classification, the pretreatment method of wave threshold denossing is firstly used to improve the signal-to-noise ratio of EEG signals. Then, the feature extraction of the three kinds of imaginary motion EEG signal is carried out by the parameter-sample entropy of nonlinear dynamics, and the nonlinear features of EEG signal are preserved. Among them, the research of Motor-Imagery (MI) EEG has always been the focus of BCI that is a highspeed development field. This paper studies three classifiers including support vector machine, LVQ neural network and BP neural network. The experimental results show that BP neural network has higher recognition rate for classification and recognition of EEG signal. [ABSTRACT FROM AUTHOR]