使用个性化刺激频率的高频SSVEP-BCI具有较少的闪烁感

IF 0.8 Q4 ROBOTICS
Sodai Kondo, Hisaya Tanaka
{"title":"使用个性化刺激频率的高频SSVEP-BCI具有较少的闪烁感","authors":"Sodai Kondo,&nbsp;Hisaya Tanaka","doi":"10.1007/s10015-023-00893-9","DOIUrl":null,"url":null,"abstract":"<div><p>The problem of brain–computer interface (BCI) using steady-state visual evoked potential (SSVEP) is a flickering sensation caused by the flashing stimuli used to induce SSVEP. To use of high-frequency flashing stimuli is one of the countermeasures of this problem. This study focused on the relationship between the magnitude of SSVEP components for each subject and proposed a high-frequency (56–70 Hz) SSVEP–BCI that uses only the frequencies at which SSVEP induction was confirmed. For comparison, the accuracy of SSVEP–BCI using learning CCA (LCCA), an extension of canonical correlation analysis (CCA), was 98.61% for the low-frequency (26–40 Hz) SSVEP–BCI for comparison, 62.78% for the high frequency (56–70 Hz) SSVEP–BCI, and 87.19% for the high frequency (56–70 Hz) SSVEP–BCI with personalized stimulus frequency. As a result of comparing with and without personalization using information transfer rate (ITR), non-personalized (normal) and personalized high-frequency SSVEP–BCI ITR were 24.25 bits/min and 29.64 bits/min.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-frequency SSVEP–BCI with less flickering sensation using personalization of stimulus frequency\",\"authors\":\"Sodai Kondo,&nbsp;Hisaya Tanaka\",\"doi\":\"10.1007/s10015-023-00893-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The problem of brain–computer interface (BCI) using steady-state visual evoked potential (SSVEP) is a flickering sensation caused by the flashing stimuli used to induce SSVEP. To use of high-frequency flashing stimuli is one of the countermeasures of this problem. This study focused on the relationship between the magnitude of SSVEP components for each subject and proposed a high-frequency (56–70 Hz) SSVEP–BCI that uses only the frequencies at which SSVEP induction was confirmed. For comparison, the accuracy of SSVEP–BCI using learning CCA (LCCA), an extension of canonical correlation analysis (CCA), was 98.61% for the low-frequency (26–40 Hz) SSVEP–BCI for comparison, 62.78% for the high frequency (56–70 Hz) SSVEP–BCI, and 87.19% for the high frequency (56–70 Hz) SSVEP–BCI with personalized stimulus frequency. As a result of comparing with and without personalization using information transfer rate (ITR), non-personalized (normal) and personalized high-frequency SSVEP–BCI ITR were 24.25 bits/min and 29.64 bits/min.</p></div>\",\"PeriodicalId\":46050,\"journal\":{\"name\":\"Artificial Life and Robotics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2023-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Life and Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10015-023-00893-9\",\"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/s10015-023-00893-9","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

使用稳态视觉诱发电位(SSVEP)的脑机接口(BCI)问题是由用于诱发稳态视觉诱发电位的闪烁刺激引起的闪烁感觉。利用高频闪烁刺激是解决这一问题的对策之一。本研究的重点是每个受试者的SSVEP分量大小之间的关系,并提出了一个高频(56-70 Hz) SSVEP -脑机接口,该接口仅使用SSVEP感应被确认的频率。相比之下,使用典型相关分析(CCA)扩展的学习CCA (LCCA)对低频(26-40 Hz) SSVEP-BCI的准确率为98.61%,高频(56-70 Hz) SSVEP-BCI的准确率为62.78%,个性化刺激频率的高频(56-70 Hz) SSVEP-BCI的准确率为87.19%。采用信息传输率(ITR)进行个性化与非个性化比较,非个性化(正常)与个性化高频SSVEP-BCI ITR分别为24.25 bit /min和29.64 bit /min。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

High-frequency SSVEP–BCI with less flickering sensation using personalization of stimulus frequency

High-frequency SSVEP–BCI with less flickering sensation using personalization of stimulus frequency

The problem of brain–computer interface (BCI) using steady-state visual evoked potential (SSVEP) is a flickering sensation caused by the flashing stimuli used to induce SSVEP. To use of high-frequency flashing stimuli is one of the countermeasures of this problem. This study focused on the relationship between the magnitude of SSVEP components for each subject and proposed a high-frequency (56–70 Hz) SSVEP–BCI that uses only the frequencies at which SSVEP induction was confirmed. For comparison, the accuracy of SSVEP–BCI using learning CCA (LCCA), an extension of canonical correlation analysis (CCA), was 98.61% for the low-frequency (26–40 Hz) SSVEP–BCI for comparison, 62.78% for the high frequency (56–70 Hz) SSVEP–BCI, and 87.19% for the high frequency (56–70 Hz) SSVEP–BCI with personalized stimulus frequency. As a result of comparing with and without personalization using information transfer rate (ITR), non-personalized (normal) and personalized high-frequency SSVEP–BCI ITR were 24.25 bits/min and 29.64 bits/min.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.00
自引率
22.20%
发文量
101
期刊介绍: Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the twenty-first century. This journal covers a broad multidisciplinary field, including areas such as artificial brain research, artificial intelligence, artificial life, artificial living, artificial mind research, brain science, chaos, cognitive science, complexity, computer graphics, evolutionary computations, fuzzy control, genetic algorithms, innovative computations, intelligent control and modelling, micromachines, micro-robot world cup soccer tournament, mobile vehicles, neural networks, neurocomputers, neurocomputing technologies and applications, robotics, robus virtual engineering, and virtual reality. Hardware-oriented submissions are particularly welcome. Publishing body: International Symposium on Artificial Life and RoboticsEditor-in-Chiei: Hiroshi Tanaka Hatanaka R Apartment 101, Hatanaka 8-7A, Ooaza-Hatanaka, Oita city, Oita, Japan 870-0856 ©International Symposium on Artificial Life and Robotics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信