Parallel convolutional neural network motor imagery electroencephalogram classification method based on spatial-temporal feature fusion

Abstract

The invention provides a parallel convolutional neural network motor imagery electroencephalogram classification method based on spatial-temporal feature fusion. According to the invention, motion imagery electroencephalogram signals are used as research objects, and a novel deep network model-parallel convolutional neural network method is provided for extracting spatial-temporal features of themotion imagery electroencephalogram signals. Different from a traditional electroencephalogram classification algorithm which often discards electroencephalogram spatial feature information, Theta waves (4-8 Hz), alpha waves (8-12 Hz) and beta waves (12-36 Hz) are extracted through fast Fourier transform to generate a 2D electroencephalogram feature map; training of the electroencephalogram feature map is conducted based on a multi-convolutional neural network so as to extract spatial features; in addition, a time convolution neural network is used for parallel training so as to extract time-order features; and finally, the spatial features and the time-order features are fused and classified based on Softmax. Experimental results show that the parallel convolutional neural network has good recognition precision and is superior to other latest classification algorithms.

Description

technical field [0001] The invention belongs to the field of motor imagery EEG classification, in particular to a parallel convolutional neural network motor imagery EEG recognition method based on spatio-temporal feature fusion. Background technique [0002] As a comprehensive reflection of the physiological activities of scalp brain cells, EEG contains a large amount of physiological and disease information. The brain-computer interaction system (BCI) based on EEG signal communication can replace the transmission of brain nerves and muscle tissue as a signal transmission channel, thereby realizing the interaction between the brain and bionic machinery. As an extension of human-computer interaction, BCI has been widely concerned by scholars and researchers in the scientific community. EEG recognition based on motor imagery is a key node in BCI system interaction on interaction with the outside world. Motor imagery is imagined subjectively by the human brain, such as imagi...

AI Technical Summary

Problems solved by technology

However, the previous algorithms, due to the EEG acquisition equipment, only visualized time-series channel data, so most researchers mainly study how to extract EEG features in time series

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