Data augmentation method based on RBSAGAN

Abstract

The invention discloses an electroencephalogram signal data augmentation method based on RBSAGAN, and the method comprises the steps: designing Up ResBlock and Down ResBlock network structures, extracting the features under different scale receptive fields through two 1D convolution layers of a trunk and one 1D convolution layer of a branch, and respectively employing a 1D deconvolution layer and an average pooling layer for the enlargement and reduction of data dimensions; and designing a 1D Self-Attention network on the basis of a Self-Attention mechanism. The network structure does not consider the distance between the data at each discrete moment, can directly obtain the global time sequence characteristics by calculating the similarity between the data at each discrete moment in parallel, and is suitable for electroencephalogram signals with rich time sequence information. A discriminator of the RBSAGAN is formed by networks such as Down ResBlock, 1D Selection and the like, and a loss value is output to update parameters of a generator and the discriminator until Nash equilibrium is achieved. The new data generated by the generator and the original data form an augmented data set, and the augmented data set is input into the 1D CNN for classification so as to evaluate the quality of the generated data.

Description

technical field [0001] The present invention relates to the technical field of motor imagery electroencephalography (Motor Imagery Electroencephalography, MI-EEG) data augmentation, and in particular adopts a deep learning (Deep Learning, DL) method to generate motor imagery electroencephalography signals. Specifically involved: Design Up ResBlock and Down ResBlock networks to increase and decrease data dimensions respectively, design a 1D Self-Attention network based on the Self-Attention mechanism for the long-distance dependency problem of longer data, and construct RBSAGAN (ResBlock Self-Attention) through the above-mentioned network -Attention Generative AdversarialNetworks) and used for the generation of EEG signal data, using Convolutional Neural Networks (CNN) to extract features and classify the expanded data set, and evaluate the quality of the generated data. Background technique [0002] Brain computer interface (BCI) is a system that directly provides patients w...

AI Technical Summary

Problems solved by technology

How to make the generated data have the key features contained in EEG is very important, and the existing EEG signal augmentation methods fail to capture the relationship between the data at each discrete moment and the global information, and do not make full use of the timing characteristics of the signal, making the Characteristic ambiguity of the generated EEG signal at long range

Moreover, the traditional multi-layer convolutional network stacking method extracts limited feature information of EEG signals, and there is a problem of feature loss, which leads to unsatisfactory quality of generated EEG signals.

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