{"title":"结合事件相关电位和BubbleView测量数字界面认知负荷。","authors":"Shaoyu Wei, Ruiling Zheng, Rui Li, Minghui Shi, Junsong Zhang","doi":"10.1186/s40708-023-00187-7","DOIUrl":null,"url":null,"abstract":"<p><p>Helmet mounted display systems (HMDs) are high-performance display devices for modern aircraft. We propose a novel method combining event-related potentials (ERPs) and BubbleView to measure cognitive load under different HMD interfaces. The distribution of the subjects' attention resources is reflected by analyzing the BubbleView, and the input of the subjects' attention resources on the interface is reflected by analyzing the ERP's P3b and P2 components. The results showed that the HMD interface with more symmetry and a simple layout had less cognitive load, and subjects paid more attention to the upper portion of the interface. Combining the experimental data of ERP and BubbleView, we can obtain a more comprehensive, objective, and reliable HMD interface evaluation result. This approach has significant implications for the design of digital interfaces and can be utilized for the iterative evaluation of HMD interfaces.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"10 1","pages":"8"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984593/pdf/","citationCount":"0","resultStr":"{\"title\":\"Measuring cognitive load of digital interface combining event-related potential and BubbleView.\",\"authors\":\"Shaoyu Wei, Ruiling Zheng, Rui Li, Minghui Shi, Junsong Zhang\",\"doi\":\"10.1186/s40708-023-00187-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Helmet mounted display systems (HMDs) are high-performance display devices for modern aircraft. We propose a novel method combining event-related potentials (ERPs) and BubbleView to measure cognitive load under different HMD interfaces. The distribution of the subjects' attention resources is reflected by analyzing the BubbleView, and the input of the subjects' attention resources on the interface is reflected by analyzing the ERP's P3b and P2 components. The results showed that the HMD interface with more symmetry and a simple layout had less cognitive load, and subjects paid more attention to the upper portion of the interface. Combining the experimental data of ERP and BubbleView, we can obtain a more comprehensive, objective, and reliable HMD interface evaluation result. This approach has significant implications for the design of digital interfaces and can be utilized for the iterative evaluation of HMD interfaces.</p>\",\"PeriodicalId\":37465,\"journal\":{\"name\":\"Brain Informatics\",\"volume\":\"10 1\",\"pages\":\"8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984593/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s40708-023-00187-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40708-023-00187-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
Measuring cognitive load of digital interface combining event-related potential and BubbleView.
Helmet mounted display systems (HMDs) are high-performance display devices for modern aircraft. We propose a novel method combining event-related potentials (ERPs) and BubbleView to measure cognitive load under different HMD interfaces. The distribution of the subjects' attention resources is reflected by analyzing the BubbleView, and the input of the subjects' attention resources on the interface is reflected by analyzing the ERP's P3b and P2 components. The results showed that the HMD interface with more symmetry and a simple layout had less cognitive load, and subjects paid more attention to the upper portion of the interface. Combining the experimental data of ERP and BubbleView, we can obtain a more comprehensive, objective, and reliable HMD interface evaluation result. This approach has significant implications for the design of digital interfaces and can be utilized for the iterative evaluation of HMD interfaces.
期刊介绍：
Brain Informatics is an international, peer-reviewed, interdisciplinary open-access journal published under the brand SpringerOpen, which provides a unique platform for researchers and practitioners to disseminate original research on computational and informatics technologies related to brain. This journal addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics. It also welcomes emerging information technologies and advanced neuro-imaging technologies, such as big data analytics and interactive knowledge discovery related to various large-scale brain studies and their applications. This journal will publish high-quality original research papers, brief reports and critical reviews in all theoretical, technological, clinical and interdisciplinary studies that make up the field of brain informatics and its applications in brain-machine intelligence, brain-inspired intelligent systems, mental health and brain disorders, etc. The scope of papers includes the following five tracks: Track 1: Cognitive and Computational Foundations of Brain Science Track 2: Human Information Processing Systems Track 3: Brain Big Data Analytics, Curation and Management Track 4: Informatics Paradigms for Brain and Mental Health Research Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
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