Data augmentation method based on RBSAGAN
A technology of data and quantity, applied in the field of using deep learning method to generate EEG signals of motor imagery, can solve the problems of missing features, limited feature information of EEG signals, not making full use of signal timing features, etc. Characteristic information is not comprehensive, and the effect of change is realized
Pending Publication Date: 2021-04-16
BEIJING UNIV OF TECH
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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.
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