EEG Enhancement Algorithm Based on Combination of Effective Attention and GAN
Electroencephalogram(EEG)-based Convolutional Neural Network(CNN)models are widely used to classify and diagnose stroke.However,owing to the small number of stroke patient samples,the category imbalance of the dataset reduces the classification accuracy.Most existing minority-class data enhancement methods utilize Generative Adversarial Network(GAN),and the generation effect is average.Although the quality of data synthesis can be improved by applying dot product attention,storage and computing costs are often high.To address this problem,a progressive data-enhancement algorithm,LESA-CGAN,based on linear effective attention is constructed.First,the algorithm adopts a two-layer autoencoding conditional GAN architecture to extract EEG label features,generate EEG samples,and refine the generation process layer by layer.Second,by introducing a Linear Effective Self-Attention(LESA)module in the encoding part,it enhances the extracted hidden layer features of the EEG labels and reduces the overall computational complexity of the network.The results of the ablation and comparison experiments indicate that under the conditions of a reasonable number of coding layers and proportion of generated data,LESA-CGAN requires fewer computing resources compared with other benchmark methods and achieves a 10％performance improvement in sample generation quality indicators.The EEG feature samples generated in each frequency band are more natural,and the accuracy and sensitivity of patient classification increase to 98.85％and 98.79％,respectively.
万方数据
维普
