EEG Visual Classification Algorithm Based on Improved StackCNN Network andEnsemble Learning

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EEG Visual Classification Algorithm Based on Improved StackCNN Network andEnsemble Learning

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[1]YANG Qing,WANG Yaqun,WEN Dou,et al.EEG Visual Classification Algorithm Based on Improved StackCNN Network andEnsemble Learning[J].Journal of Zhengzhou University (Engineering Science),2024,45(05):69-76.[doi:10.13705/j.issn.1671-6833.2024.02.009]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 05 Page number: 69-76 Column: Public date: 2024-08-08

Title:: EEG Visual Classification Algorithm Based on Improved StackCNN Network andEnsemble Learning

Author(s):: YANG Qing¹; 2; 3; WANG Yaqun¹; 2; 3; WEN Dou¹; 2; 3; WANG Ying¹; 2; 3; WANG Xiangyu¹; 2; 3; 1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Central China Normal University, Wuhan 430079,China; 2. School of Computer,Central China Normal University, Wuhan 430079,China; 3. National Language Resources Monitoring &Research Center for Network Media,Central China Normal University, Wuhan 430079,China

Keywords:: electroencephagram; vision classification; convolutional neural network; Bagging algorithm; ResNet network

CLC:: TP399

DOI:: 10.13705/j.issn.1671-6833.2024.02.009

Abstract:: Abstract: Aiming at the limited studies researches on visual classification directly using image-induced EEG signals 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 theK-max pooling method to the stackCNN network to solve the problem of information loss when extracting EEGfeatures, and combining with Bagging algorithm to enhance the generalization ability of the network, this methodwas called StackCNN-B. In order to verify the performance of StackCNN-B method in image classification, imageswere classified using deep residual network regression. The results of ablation experiments and comparativeexperiments with existing studies showed that the recognition accuracy of this method was high. The averageaccuracy in learning the visual feature representation of EEG signals was 99. 78%, and the average accuracy inimage classification was 96. 45%. Compared with the most advanced Bi-LSTM-AttGW method, the averageaccuracy was improved by 0. 28 percentage point and 2. 97 percentage point. The results verified that EEG signalscould effectively decode human brain activities related to visual recognition, proved the advantages of the proposedStackCNN-B model.

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Last Update: 2024-09-02