An EEG Signal Classification Method Based on Improved Deep Residual Group Convolutional Network

Abstract

The invention belongs to the field of pattern recognition and EEG signal processing, and relates to an EEG signal classification method based on an improved ResNeXt network; it includes four parts: EEG signal acquisition, preprocessing, feature extraction, and training ResNeXt classification network; training ResNeXt classification network is Refers to: dividing the training set and test set; building an improved ResNeXt EEG signal classification network; training the improved ResNeXt EEG signal classification network; building an improved ResNeXt EEG signal classification network refers to: improving on the basis of ResNeXt, and convolving the groups The middle layer of the convolutional layer of each block module increases the direct connection operation, speeds up the speed of model convergence, reduces the test error of the model, and improves the generalization ability; the invention speeds up the convergence speed of the classification model, compared with the convolutional neural network brain The electrical classification model, the improved ResNeXt classification model is easier to optimize, effectively improves the gradient explosion problem in the deep training model, and can greatly deepen the number of layers of the network while avoiding the degradation of the classification model.

Description

An EEG Signal Classification Method Based on Improved Deep Residual Grouping Convolutional Networks technical field The invention belongs to the field of pattern recognition, relate to EEG pattern classification in brain-computer interface, particularly a kind of basic To improve the classification of EEG signals with deep residual grouped convolutional networks. Background technique In recent years, with the rapid development of neuroscience, information science, computer science and other fields, a kind of New high-tech technology - brain-computer interface technology. Brain-Computer Interface (BCI), yes Also known as "brain port" or "brain-machine fusion perception", it is the use of EEG between human or animal brains and external devices Or other physiological measures of brain activity that establish communication conduits that do not rely on traditional neuromuscular output. brain-computer interface Representing the potential next hardware interface, the fu...

AI Technical Summary

Problems solved by technology

[0005] The technical problem solved by the present invention is to overcome the problem that the existing technology cannot fully utilize the features extracted from the EEG signal and optimize the amount of calculation and feature transfer. On the basis of ResNeXt, a layer of convolutional layer is directly Lian, provided an improved ResNeXt-based EEG signal classification method

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