基于改进StackCNN网络和集成学习的脑电信号视觉分类算法OA北大核心CSTPCD

Aiming at the limited studies researches on visual classification directly using image-induced EEG sig-nals and low average accuracy of visual classification,a method combining convolutional neural networks(CNN)and ensemble learning was designed to learn the visual feature representation related to EEG signals.By adding the K-max pooling method to the stackCNN network to solve the problem of information loss when extracting EEG features,and combining with Bagging algorithm to enhance the generalization ability of the network,this method was called StackCNN-B.In order to verify the performance of StackCNN-B method in image classification,images were classified using deep residual network regression.The results of ablation experiments and comparative experiments with existing studies showed that the recognition accuracy of this method was high.The average accuracy in learning the visual feature representation of EEG signals was 99.78%,and the average accuracy in image classification was 96.45%.Compared with the most advanced Bi-LSTM-AttGW method,the average accuracy was improved by 0.28 percentage point and 2.97 percentage point.The results verified that EEG signals could effectively decode human brain activities related to visual recognition,proved the advantages of the proposed StackCNN-B model.