Electroencephalogram signal decoding method based on deep convolutional generative adversarial neural network

Abstract

An electroencephalogram signal decoding method based on a deep convolutional generative adversarial neural network comprises: firstly, converting an electroencephalogram signal into a time-frequency domain image signal from a time sequence signal through short-time Fourier transform; and integrating the convolutional neural classification network and the adversarial generation network, performingdata enhancement on the small sample electroencephalogram data by using the adversarial generation network, putting the enhanced data into the convolutional neural classification network, and performing classification to realize decoding. The structural advantages of the convolutional neural classification network and the adversarial generative network are combined, the defects existing in small sample data processing through the neural network are overcome, the problems existing in the non-stationary and non-linear signal processing process are effectively solved, and the spontaneous electroencephalogram signal decoding precision is improved; and meanwhile, a new solution is provided for electroencephalogram signal data enhancement, and a new thought is provided for reducing the calibration time and improving the generalization ability of the classification model in the actual operation process in the future.

Description

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AI Technical Summary

Benefits of technology

This patented technology uses two networks - one called CWDNN (Convolutionally Neuron Network) that works together with another type of neurons like LSTM or GRU to improve upon traditional methods used by brain researchers such as neurology. These techniques help identify specific types of electrical activity within certain parts of our body better than other areas due to their unique structure. Overall, this innovative approach helps decode more accurate electroencephalography (EEG).

Problems solved by technology

This patented describes two technical methods: 1 ) Deep Neural Network techniques - they use artificial intelligence or machine learning tools trained through training from examples collected during experiments; 2) Computational Fluid Dynamics (CFD)-based approaches such as Simulated Experimentation Environment (SWE), which aim at studying motional phenomena like movement while also analyzing their effects on other parts of the body. These technologies help researchers better interpret electrical activities more accurately than previous analysis methods.

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