Jingjing Li , Ching-Hung Lee , Yanhong Zhou , Tiange Liu , Tzyy-Ping Jung , Xianglong Wan , Dingna Duan , Dong Wen
{"title":"用于跨主体和跨场景分析的新型人工智能驱动脑电图广义分类模型","authors":"Jingjing Li , Ching-Hung Lee , Yanhong Zhou , Tiange Liu , Tzyy-Ping Jung , Xianglong Wan , Dingna Duan , Dong Wen","doi":"10.1016/j.aei.2024.102971","DOIUrl":null,"url":null,"abstract":"<div><div>Artificial intelligence algorithms combined with electroencephalography (EEG) can effectively identify and interpret patterns of brain activity. However, the considerable variability in EEG signals among individuals and the challenges in transferring data and features among different scenarios result in a lack of universality in EEG signal analysis methods. To address these challenges, we introduce a novel AI-driven EEG general classification model called the Deformation Residual Compact Shrinkage Attention Mechanism (D-RCSAM) network. This low-parameter model improves spatial sampling positions using deformable convolution blocks and reduces computational costs while improving generalization performance through depthwise separable residual blocks. We further optimized the soft thresholding function to enhance the model’s nonlinearity and sparse representation, while also improving the loss function. We validated the proposed model on one public dataset and two private datasets, with results demonstrating that the D-RCSAM model effectively integrates both public and private EEG signal features. Visualization and interpretability results show that the D-RCSAM model can handle cross-subject and cross-scene classification tasks, outperforming state-of-the-art models in cognitive task classification. This research offers a new perspective on intelligent, comprehensive analysis across individuals and scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"63 ","pages":"Article 102971"},"PeriodicalIF":8.0000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel AI-driven EEG generalized classification model for cross-subject and cross-scene analysis\",\"authors\":\"Jingjing Li , Ching-Hung Lee , Yanhong Zhou , Tiange Liu , Tzyy-Ping Jung , Xianglong Wan , Dingna Duan , Dong Wen\",\"doi\":\"10.1016/j.aei.2024.102971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Artificial intelligence algorithms combined with electroencephalography (EEG) can effectively identify and interpret patterns of brain activity. However, the considerable variability in EEG signals among individuals and the challenges in transferring data and features among different scenarios result in a lack of universality in EEG signal analysis methods. To address these challenges, we introduce a novel AI-driven EEG general classification model called the Deformation Residual Compact Shrinkage Attention Mechanism (D-RCSAM) network. This low-parameter model improves spatial sampling positions using deformable convolution blocks and reduces computational costs while improving generalization performance through depthwise separable residual blocks. We further optimized the soft thresholding function to enhance the model’s nonlinearity and sparse representation, while also improving the loss function. We validated the proposed model on one public dataset and two private datasets, with results demonstrating that the D-RCSAM model effectively integrates both public and private EEG signal features. Visualization and interpretability results show that the D-RCSAM model can handle cross-subject and cross-scene classification tasks, outperforming state-of-the-art models in cognitive task classification. This research offers a new perspective on intelligent, comprehensive analysis across individuals and scenarios.</div></div>\",\"PeriodicalId\":50941,\"journal\":{\"name\":\"Advanced Engineering Informatics\",\"volume\":\"63 \",\"pages\":\"Article 102971\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Engineering Informatics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1474034624006220\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034624006220","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A novel AI-driven EEG generalized classification model for cross-subject and cross-scene analysis
Artificial intelligence algorithms combined with electroencephalography (EEG) can effectively identify and interpret patterns of brain activity. However, the considerable variability in EEG signals among individuals and the challenges in transferring data and features among different scenarios result in a lack of universality in EEG signal analysis methods. To address these challenges, we introduce a novel AI-driven EEG general classification model called the Deformation Residual Compact Shrinkage Attention Mechanism (D-RCSAM) network. This low-parameter model improves spatial sampling positions using deformable convolution blocks and reduces computational costs while improving generalization performance through depthwise separable residual blocks. We further optimized the soft thresholding function to enhance the model’s nonlinearity and sparse representation, while also improving the loss function. We validated the proposed model on one public dataset and two private datasets, with results demonstrating that the D-RCSAM model effectively integrates both public and private EEG signal features. Visualization and interpretability results show that the D-RCSAM model can handle cross-subject and cross-scene classification tasks, outperforming state-of-the-art models in cognitive task classification. This research offers a new perspective on intelligent, comprehensive analysis across individuals and scenarios.
期刊介绍:
Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.