{"title":"采用信度评分法改进耳- eeg SSVEP-BCI的性能","authors":"Sodai Kondo, Hideyuki Harafuji, Ren Kiuchi, Asahi Saito, Kakeru Tanaka, Wataru Wakayama, Hisaya Tanaka","doi":"10.1007/s10015-025-01025-1","DOIUrl":null,"url":null,"abstract":"<div><p>Steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) are known for high speed, accuracy, and multivalue input. Integrating ear-electroencephalogram (EEG) can make SSVEP-BCI more accessible for everyday use. This study introduces a reliability score to enhance the performance of ear-EEG SSVEP-BCI by dynamically adjusting measurement duration and enabling asynchronous detection. Two analysis methods, learning canonical correlation analysis (LCCA) and task-related component analysis, were evaluated. Using the reliability score, the accuracy for ear-EEG SSVEP-BCI reached <span>\\(100\\)</span>% with an information transfer rate (ITR) of <span>\\(22.36\\pm 3.54\\)</span> bits/min, compared to <span>\\(61.93\\pm 9.22\\)</span>% accuracy and <span>\\(15.32\\pm 4.59\\)</span> bits/min ITR without the reliability score. These findings demonstrate that the reliability score significantly improves ear-EEG SSVEP-BCI performance, suggesting its potential to enhance usability in practical applications. </p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"449 - 457"},"PeriodicalIF":0.8000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01025-1.pdf","citationCount":"0","resultStr":"{\"title\":\"Performance improvement of Ear-EEG SSVEP-BCI using reliability score\",\"authors\":\"Sodai Kondo, Hideyuki Harafuji, Ren Kiuchi, Asahi Saito, Kakeru Tanaka, Wataru Wakayama, Hisaya Tanaka\",\"doi\":\"10.1007/s10015-025-01025-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) are known for high speed, accuracy, and multivalue input. Integrating ear-electroencephalogram (EEG) can make SSVEP-BCI more accessible for everyday use. This study introduces a reliability score to enhance the performance of ear-EEG SSVEP-BCI by dynamically adjusting measurement duration and enabling asynchronous detection. Two analysis methods, learning canonical correlation analysis (LCCA) and task-related component analysis, were evaluated. Using the reliability score, the accuracy for ear-EEG SSVEP-BCI reached <span>\\\\(100\\\\)</span>% with an information transfer rate (ITR) of <span>\\\\(22.36\\\\pm 3.54\\\\)</span> bits/min, compared to <span>\\\\(61.93\\\\pm 9.22\\\\)</span>% accuracy and <span>\\\\(15.32\\\\pm 4.59\\\\)</span> bits/min ITR without the reliability score. These findings demonstrate that the reliability score significantly improves ear-EEG SSVEP-BCI performance, suggesting its potential to enhance usability in practical applications. </p></div>\",\"PeriodicalId\":46050,\"journal\":{\"name\":\"Artificial Life and Robotics\",\"volume\":\"30 3\",\"pages\":\"449 - 457\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10015-025-01025-1.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Life and Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10015-025-01025-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Life and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s10015-025-01025-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
摘要
稳态视觉诱发电位（SSVEP）脑机接口（BCI）以高速、准确和多值输入而闻名。整合耳脑电图（EEG）可以使SSVEP-BCI更易于日常使用。本研究引入信度评分，通过动态调整测量时间和实现异步检测来提高耳-脑SSVEP-BCI的性能。评估了学习典型相关分析（LCCA）和任务相关成分分析（task-related component analysis）两种分析方法。采用信度评分，耳-脑SSVEP-BCI的准确率达到 \(100\)% with an information transfer rate (ITR) of \(22.36\pm 3.54\) bits/min, compared to \(61.93\pm 9.22\)% accuracy and \(15.32\pm 4.59\) bits/min ITR without the reliability score. These findings demonstrate that the reliability score significantly improves ear-EEG SSVEP-BCI performance, suggesting its potential to enhance usability in practical applications.
Performance improvement of Ear-EEG SSVEP-BCI using reliability score
Steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) are known for high speed, accuracy, and multivalue input. Integrating ear-electroencephalogram (EEG) can make SSVEP-BCI more accessible for everyday use. This study introduces a reliability score to enhance the performance of ear-EEG SSVEP-BCI by dynamically adjusting measurement duration and enabling asynchronous detection. Two analysis methods, learning canonical correlation analysis (LCCA) and task-related component analysis, were evaluated. Using the reliability score, the accuracy for ear-EEG SSVEP-BCI reached \(100\)% with an information transfer rate (ITR) of \(22.36\pm 3.54\) bits/min, compared to \(61.93\pm 9.22\)% accuracy and \(15.32\pm 4.59\) bits/min ITR without the reliability score. These findings demonstrate that the reliability score significantly improves ear-EEG SSVEP-BCI performance, suggesting its potential to enhance usability in practical applications.
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