Brain wave characteristic extraction method based on wavelet translation and BP neural network

Abstract

The invention discloses an extraction method for brain-computer interface system imagination action EEG signal features, in particular to an EEG feature extraction method based on a wavelet transform and a BP neural network. The invention takes the energy change caused by imagination action thinking to be a feature distinguishing the imagination movements of a left hand and a right hand, respectively calculates the point-to-point average power of the entire samplings of the EEG signal obtained from C3 and C4 channels by the left hand and the right hand through the imagination (thereinafter called as C3 and C4 of the left hand and the right hand) within 0 to 9s according to the average power formula. A time window is arranged, a discrete dyadic wavelet transform is made to the data of a section provided with the window, an approximation signal a6 on a sixth size is selected to be taken as a signal feature; a BP neural network is used as a classifier to classify. The method of the invention adopting the wavelet transform and the BP neural network to extract the potential of the imagination movement helps to improve the signal / noise ratio and the identification correction rate of the potential of the imagination action; in addition, the wavelet transform is a linear transform, has a quick calculation speed, and is suitable for on-line analysis.

Description

technical field [0001] The invention relates to a method for extracting features of imaginary action EEG signals in a brain-computer interface (brain-computer interface, BCI) system, in particular to a method for extracting imaginative action EEG features by using discrete wavelet transform and BP neural network. Background technique [0002] Brain-computer interface (brain-computer interface, BCI) refers to a brain-computer (computer or other device) communication system that does not depend on the normal output pathways of the brain (ie, peripheral nerves and muscles). Communication and control methods. BCI system usually consists of four parts: signal acquisition system, signal processing system, pattern recognition system and control device system, as shown in Figure 1. The signal analysis and processing link is the core part of every BCI system, and its function is to convert the input EEG signal into an output signal for controlling external devices. [0003] An imag...

AI Technical Summary

Problems solved by technology

However, using existing technologies, such as superimposed average method, fast Fourier transform method, autoregressive model spectrum estimation, independent component analysis and other methods, to extract characteristic signals from EEG signals containing a lot of noise, there are low signal-to-noise ratios and identification problems. The accuracy is not high, the recognition speed is slow and other shortcomings

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