Electroencephalogram signal recognition method based on spatiotemporal feature weighted convolutional neural network

Abstract

The invention requests to protect an electroencephalogram signal identification method based on a spatiotemporal feature weighted convolutional neural network. The method comprises the following steps: firstly, de-noising a motor imagery electroencephalogram signal by using discrete wavelet transform; designing a spatial-temporal feature weighted convolutional neural network to perform feature extraction on the processed electroencephalogram signal, wherein the convolution operation of the first layer is carried out on the time scale of the motor imagery electroencephalogram signal, and the convolution operation of the second layer is carried out on the channel scale, so that the extracted features include the space-time characteristics of the motor imagery electroencephalogram signal; because the importance degrees of the extracted features are different, a feature weighting module is added into the network, so that the important features are highlighted, and the unimportant featuresare weakened. Characteristics extracted by the model can reflect the characteristics of various motor imagery electroencephalogram signals more effectively, and the recognition accuracy of the motor imagery electroencephalogram signals can be improved.

Description

technical field [0001] The invention belongs to an electroencephalogram signal identification method, in particular to an electroencephalogram signal identification method based on a spatio-temporal feature weighted convolutional neural network. Background technique [0002] Brain-computer interface (BCI) is a technology that converts information recorded directly from the cerebral cortex into computer control commands and is used to control external devices such as wheelchairs and robotic arms. In the BCI system, the feature extraction of EEG signal is one of its important parts. Motor imagery EEG signals are widely used in BCI systems because they are easy to collect and will not cause harm to the human body. However, due to the high-dimensional, nonlinear and non-stationary characteristics of motor imagery EEG signals, modeling them is a very challenging task. [0003] The commonly used motor imagery EEG signal feature extraction methods are: Common Spatial Pattern (CSP...

AI Technical Summary

Problems solved by technology

Convolutional neural network (CNN) is one of the typical representatives, but the features extracted by conventional CNN cannot fully and effectively utilize the effective information in EEG signals.

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