Convolutional neural network training method, electroencephalogram signal recognition method and device and medium

Abstract

The invention discloses a convolutional neural network training method, an electroencephalogram signal recognition method and device and a medium, and the method comprises the steps: executing a plurality of obtaining processes, obtaining an electroencephalogram signal in each obtaining process, and executing the time domain data enhancement and frequency domain data enhancement of the electroencephalogram signal, and training a convolutional neural network by using the enhanced electroencephalogram signal, and the like. The convolutional neural network trained by the method is a multi-input,multi-convolution-scale and multi-convolution-type hybrid convolutional neural network, the sizes of a multi-input convolution layer and a convolution kernel are reasonably designed, and the method has high recognition accuracy; a training set used for training the convolutional neural network is obtained by performing time domain data enhancement and frequency domain data enhancement expansion based on the acquired electroencephalogram signals, so the training data volume of the convolutional neural network can be increased, the over-fitting phenomenon can be reduced, noise interference in the electroencephalogram signals can be effectively coped with, and the recognition effect can be improved. The method is widely applied to the technical field of signal processing.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a convolutional neural network training method, an EEG signal recognition method, a device and a medium. Background technique [0002] The brain-computer interface can convert brain activity into computer control instructions, thereby controlling external devices, and can be widely used in fields such as medicine and industrial control. EEG signals have the advantages of non-invasiveness and high temporal resolution, so they are used as signal sources for EEG interfaces. Applying EEG signals to brain-computer interfaces involves the process of identifying EEG signals, that is, identifying the type or characteristics of EEG signals, and then converting them into computer control instructions. The EEG signal also has the characteristics of non-stationary, nonlinear and random, that is, the characteristics of the EEG signal change with time, so the EEG signal is easily di...

AI Technical Summary

Problems solved by technology

The EEG signal also has the characteristics of non-stationary, nonlinear and random, that is, the characteristics of the EEG signal change with time, so the EEG signal is easily disturbed by noise, which is not good for the recognition of the EEG signal. application is also bad

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