Adaptive CSP with subspace alignment for subject-to-subject transfer in motor imagery brain-computer interfaces

Yi-Ming Jin, Mahta Mousavi, V. D. Sa
{"title":"Adaptive CSP with subspace alignment for subject-to-subject transfer in motor imagery brain-computer interfaces","authors":"Yi-Ming Jin, Mahta Mousavi, V. D. Sa","doi":"10.1109/IWW-BCI.2018.8311494","DOIUrl":null,"url":null,"abstract":"In brain-computer interfaces, adapting a classifier from one user to another is challenging but essential to reduce training time for new users. Common Spatial Patterns (CSP) is a widely used method for learning spatial filters for user specific feature extraction but the performance is degraded when applied to a different user. This paper proposes a novel Adaptive Selective Common Spatial Pattern (ASCSP) method to update the covariance matrix using selected candidates. Subspace alignment is then applied to the extracted features before classification. The proposed method outperforms the standard CSP and adaptive CSP algorithms previously proposed. Visualization of extracted features is provided to demonstrate how subspace alignment contributes to reduce the domain variance between source and target domains.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"4 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Conference on Brain-Computer Interface (BCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWW-BCI.2018.8311494","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

In brain-computer interfaces, adapting a classifier from one user to another is challenging but essential to reduce training time for new users. Common Spatial Patterns (CSP) is a widely used method for learning spatial filters for user specific feature extraction but the performance is degraded when applied to a different user. This paper proposes a novel Adaptive Selective Common Spatial Pattern (ASCSP) method to update the covariance matrix using selected candidates. Subspace alignment is then applied to the extracted features before classification. The proposed method outperforms the standard CSP and adaptive CSP algorithms previously proposed. Visualization of extracted features is provided to demonstrate how subspace alignment contributes to reduce the domain variance between source and target domains.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于子空间对齐的自适应CSP在运动图像脑机接口中的转移
在脑机接口中,将分类器从一个用户调整到另一个用户是具有挑战性的,但对于减少新用户的训练时间至关重要。公共空间模式(Common Spatial Patterns, CSP)是一种广泛应用于特定用户特征提取的空间过滤器学习方法,但当应用于不同的用户时,其性能会下降。本文提出了一种新的自适应选择公共空间模式(ASCSP)方法,利用选择的候选者更新协方差矩阵。然后在分类之前对提取的特征进行子空间对齐。该方法优于已有的标准CSP算法和自适应CSP算法。对提取的特征进行可视化,以演示子空间对齐如何有助于减少源域和目标域之间的域方差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Embodied cognition Design of a brain-controlled robot arm system based on upper-limb movement imagery Applying deep-learning to a top-down SSVEP BMI BCI classification using locally generated CSP features Evaluation of outlier prevalence of density distribution in brain computed tomography: Comparison of kurtosis and quartile statistics
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1