Space-time portraits of brain dynamics

Sang Wan Lee
{"title":"Space-time portraits of brain dynamics","authors":"Sang Wan Lee","doi":"10.1109/IWW-BCI.2016.7457458","DOIUrl":null,"url":null,"abstract":"Recent developments in the application of electroencephalography (EEG) signal-based brain-machine interfaces (BMI) provide support for the capabilities of EEG techniques to account for neural dynamics associated with simple task performance. However, the fundamental question remains as to whether EEG signal patterns relfect information sufficient for dictating underlying cognitive processes. Accurate identification imposes a substantial challenge to computation because such congnitive processes are known to involve a brain-wide correlation in both a spatial and temporal domain. Here we discuss a flexible computational framework for efficiently analyzing dynamics of the whole brain network. The proposed method reduces a heavy computational load by switching between covariance and gram matrices to compute eigenvectors, potentially enabling us to streamline analyses for revealing information pertaining to the present cognitive state.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWW-BCI.2016.7457458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Recent developments in the application of electroencephalography (EEG) signal-based brain-machine interfaces (BMI) provide support for the capabilities of EEG techniques to account for neural dynamics associated with simple task performance. However, the fundamental question remains as to whether EEG signal patterns relfect information sufficient for dictating underlying cognitive processes. Accurate identification imposes a substantial challenge to computation because such congnitive processes are known to involve a brain-wide correlation in both a spatial and temporal domain. Here we discuss a flexible computational framework for efficiently analyzing dynamics of the whole brain network. The proposed method reduces a heavy computational load by switching between covariance and gram matrices to compute eigenvectors, potentially enabling us to streamline analyses for revealing information pertaining to the present cognitive state.
大脑动力学的时空画像
基于脑机接口(BMI)信号的脑电图(EEG)应用的最新进展为EEG技术解释与简单任务性能相关的神经动力学提供了支持。然而,基本的问题仍然是脑电图信号模式是否反映了足够的信息来指示潜在的认知过程。准确的识别给计算带来了巨大的挑战,因为这种认知过程涉及到在空间和时间领域的全脑关联。在这里,我们讨论了一个灵活的计算框架,以有效地分析整个大脑网络的动态。所提出的方法通过在协方差和克矩阵之间切换来计算特征向量,减少了沉重的计算负荷,可能使我们能够简化分析,以揭示与当前认知状态有关的信息。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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