脑电图同步多模态记录的两个常见问题:抖动和延迟

IF 2.4 4区 医学 Q3 NEUROSCIENCES
Seitaro Iwama , Mitsuaki Takemi , Ryo Eguchi , Ryotaro Hirose , Masumi Morishige , Junichi Ushiba
{"title":"脑电图同步多模态记录的两个常见问题:抖动和延迟","authors":"Seitaro Iwama ,&nbsp;Mitsuaki Takemi ,&nbsp;Ryo Eguchi ,&nbsp;Ryotaro Hirose ,&nbsp;Masumi Morishige ,&nbsp;Junichi Ushiba","doi":"10.1016/j.neures.2023.12.003","DOIUrl":null,"url":null,"abstract":"<div><p>Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., muscle activities, eye movement, pupil diameters, or body kinematics data) is ubiquitous in human neuroscience research. However, the precise time alignment of multiple data from heterogeneous sources (i.e., devices) is often arduous due to variable recording parameters of commercially available research devices and complex experimental setups. In this review, we introduced the versatility of a Lab Streaming Layer (LSL)-based application that can overcome two common issues in measuring multimodal data: jitter and latency. We discussed the issues of jitter and latency in multimodal recordings and the benefits of time-synchronization when recording with multiple devices. In addition, a computer simulation was performed to highlight how the millisecond-order jitter readily affects the signal-to-noise ratio of the electrophysiological outcome. Together, we argue that the LSL-based system can be used for research requiring precise time-alignment of datasets. Studies that detect stimulus-induced transient neural responses or test hypotheses regarding temporal relationships of different functional aspects with multimodal data would benefit most from LSL-based systems.</p></div>","PeriodicalId":19146,"journal":{"name":"Neuroscience Research","volume":"203 ","pages":"Pages 1-7"},"PeriodicalIF":2.4000,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0168010223002201/pdfft?md5=1a551f0703704d6ad1d5d6b046a4638c&pid=1-s2.0-S0168010223002201-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Two common issues in synchronized multimodal recordings with EEG: Jitter and latency\",\"authors\":\"Seitaro Iwama ,&nbsp;Mitsuaki Takemi ,&nbsp;Ryo Eguchi ,&nbsp;Ryotaro Hirose ,&nbsp;Masumi Morishige ,&nbsp;Junichi Ushiba\",\"doi\":\"10.1016/j.neures.2023.12.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., muscle activities, eye movement, pupil diameters, or body kinematics data) is ubiquitous in human neuroscience research. However, the precise time alignment of multiple data from heterogeneous sources (i.e., devices) is often arduous due to variable recording parameters of commercially available research devices and complex experimental setups. In this review, we introduced the versatility of a Lab Streaming Layer (LSL)-based application that can overcome two common issues in measuring multimodal data: jitter and latency. We discussed the issues of jitter and latency in multimodal recordings and the benefits of time-synchronization when recording with multiple devices. In addition, a computer simulation was performed to highlight how the millisecond-order jitter readily affects the signal-to-noise ratio of the electrophysiological outcome. Together, we argue that the LSL-based system can be used for research requiring precise time-alignment of datasets. Studies that detect stimulus-induced transient neural responses or test hypotheses regarding temporal relationships of different functional aspects with multimodal data would benefit most from LSL-based systems.</p></div>\",\"PeriodicalId\":19146,\"journal\":{\"name\":\"Neuroscience Research\",\"volume\":\"203 \",\"pages\":\"Pages 1-7\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0168010223002201/pdfft?md5=1a551f0703704d6ad1d5d6b046a4638c&pid=1-s2.0-S0168010223002201-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroscience Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168010223002201\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroscience Research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168010223002201","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

使用脑电图(EEG)和其他生物信号(如肌肉活动、眼球运动、瞳孔直径或身体运动学数据)进行多模态记录在人类神经科学研究中无处不在。然而,由于市售研究设备的记录参数不一,加上实验设置复杂,要对来自不同来源(即设备)的多个数据进行精确的时间配准往往十分困难。在这篇综述中,我们介绍了基于实验室流层(LSL)应用的多功能性,它可以克服测量多模态数据时的两个常见问题:抖动和延迟。我们讨论了多模态记录中的抖动和延迟问题,以及使用多个设备记录时时间同步的好处。此外,我们还进行了计算机模拟,以强调毫秒阶抖动如何轻易影响电生理结果的信噪比。综上所述,我们认为基于 LSL 的系统可用于需要对数据集进行精确时间校准的研究。利用多模态数据检测刺激引起的瞬时神经反应或测试不同功能方面的时间关系假设的研究,将从基于 LSL 的系统中获益匪浅。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two common issues in synchronized multimodal recordings with EEG: Jitter and latency

Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., muscle activities, eye movement, pupil diameters, or body kinematics data) is ubiquitous in human neuroscience research. However, the precise time alignment of multiple data from heterogeneous sources (i.e., devices) is often arduous due to variable recording parameters of commercially available research devices and complex experimental setups. In this review, we introduced the versatility of a Lab Streaming Layer (LSL)-based application that can overcome two common issues in measuring multimodal data: jitter and latency. We discussed the issues of jitter and latency in multimodal recordings and the benefits of time-synchronization when recording with multiple devices. In addition, a computer simulation was performed to highlight how the millisecond-order jitter readily affects the signal-to-noise ratio of the electrophysiological outcome. Together, we argue that the LSL-based system can be used for research requiring precise time-alignment of datasets. Studies that detect stimulus-induced transient neural responses or test hypotheses regarding temporal relationships of different functional aspects with multimodal data would benefit most from LSL-based systems.

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
Neuroscience Research
Neuroscience Research 医学-神经科学
CiteScore
5.60
自引率
3.40%
发文量
136
审稿时长
28 days
期刊介绍: The international journal publishing original full-length research articles, short communications, technical notes, and reviews on all aspects of neuroscience Neuroscience Research is an international journal for high quality articles in all branches of neuroscience, from the molecular to the behavioral levels. The journal is published in collaboration with the Japan Neuroscience Society and is open to all contributors in the world.
×
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