• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于小波的相似性方法提高脑机接口拼写系统中事件相关电位的单次检测能力。

Wavelet-Based Semblance Methods to Enhance the Single-Trial Detection of Event-Related Potentials for a BCI Spelling System.

机构信息

Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Valparaíso, Chile.

Université de Lorraine, CNRS, INRIA, LORIA, F-54000 Nancy, France.

出版信息

Comput Intell Neurosci. 2019 Aug 26;2019:8432953. doi: 10.1155/2019/8432953. eCollection 2019.

DOI:10.1155/2019/8432953
PMID:31534448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6732649/
Abstract

Based on similarity measures in the wavelet domain under a multichannel EEG setting, two new methods are developed for single-trial event-related potential (ERP) detection. The first method, named "multichannel EEG thresholding by similarity" (METS), simultaneously denoises all of the information recorded by the channels. The second approach, named "semblance-based ERP window selection" (SEWS), presents two versions to automatically localize the ERP in time for each subject to reduce the time window to be analysed by removing useless features. We empirically show that when these methods are used independently, they are suitable for ERP denoising and feature extraction. Meanwhile, the combination of both methods obtains better results compared to using them independently. The denoising algorithm was compared with classic thresholding methods based on wavelets and was found to obtain better results, which shows its suitability for ERP processing. The combination of the two algorithms for denoising the signals and selecting the time window has been compared to xDAWN, which is an efficient algorithm to enhance ERPs. We conclude that our wavelet-based semblance method performs better than xDAWN for single-trial detection in the presence of artifacts or noise.

摘要

基于多通道脑电设置下的小波域相似性度量，我们开发了两种新的方法用于单试次事件相关电位（ERP）检测。第一种方法称为“多通道脑电相似度阈值法”（METS），它同时对所有通道记录的信息进行去噪。第二种方法称为“基于相似性的 ERP 窗口选择法”（SEWS），它有两个版本，旨在为每个受试者自动定位 ERP，以通过去除无用特征来减少要分析的时间窗口。我们经验性地表明，当这些方法独立使用时，它们适用于 ERP 去噪和特征提取。同时，两种方法的组合比单独使用它们获得更好的结果。去噪算法与基于小波的经典阈值法进行了比较，结果表明它更适合于 ERP 处理。对这两种算法进行信号去噪和时间窗口选择的组合与 xDAWN 进行了比较，xDAWN 是一种用于增强 ERP 的有效算法。我们得出结论，在存在伪影或噪声的情况下，我们的基于小波的相似性方法在单试次检测方面的表现优于 xDAWN。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/3aa9904375fb/CIN2019-8432953.alg.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/b31927a14660/CIN2019-8432953.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/5c05ea3c9801/CIN2019-8432953.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/f1b06ef2411b/CIN2019-8432953.alg.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/3aa9904375fb/CIN2019-8432953.alg.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/b31927a14660/CIN2019-8432953.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/5c05ea3c9801/CIN2019-8432953.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/f1b06ef2411b/CIN2019-8432953.alg.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cb/6732649/3aa9904375fb/CIN2019-8432953.alg.003.jpg

相似文献

1
Wavelet-Based Semblance Methods to Enhance the Single-Trial Detection of Event-Related Potentials for a BCI Spelling System.基于小波的相似性方法提高脑机接口拼写系统中事件相关电位的单次检测能力。
Comput Intell Neurosci. 2019 Aug 26;2019:8432953. doi: 10.1155/2019/8432953. eCollection 2019.
2
Single-trial evoked potentials study by combining wavelet denoising and principal component analysis methods.基于小波去噪和主成分分析方法的单次诱发电位研究。
J Clin Neurophysiol. 2010 Feb;27(1):17-24. doi: 10.1097/WNP.0b013e3181c9b29a.
3
ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces.利用信号子空间与噪声子空间之间的对比对多通道脑电图数据进行事件相关电位去噪。
J Neurosci Methods. 2009 Jun 15;180(2):340-51. doi: 10.1016/j.jneumeth.2009.03.021. Epub 2009 Mar 31.
4
Automatic denoising of single-trial evoked potentials.单次试验诱发电位的自动去噪
Neuroimage. 2013 Feb 1;66:672-80. doi: 10.1016/j.neuroimage.2012.10.062. Epub 2012 Nov 7.
5
Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.通过共同空间模式和小波滤波用于脑机接口的视觉诱发电位单试验检测
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:2882-5. doi: 10.1109/EMBC.2013.6610142.
6
Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.使用小波域线性混合模型检测单次试验脑电图诱发电位：在错误电位分类中的应用。
J Neural Eng. 2015 Jun;12(3):036013. doi: 10.1088/1741-2560/12/3/036013. Epub 2015 May 14.
7
[Extraction of evoked related potentials by using the combination of independent component analysis and wavelet analysis].[利用独立成分分析与小波分析相结合的方法提取诱发相关电位]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Aug;27(4):741-5.
8
Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques.使用离散小波变换结合霍夫曼编码和机器学习技术的集合对事件相关电位（ERPs）进行单试提取和视觉刺激分类。
J Neuroeng Rehabil. 2023 Jun 2;20(1):70. doi: 10.1186/s12984-023-01179-8.
9
Multiresolution analysis of event-related potentials by wavelet decomposition.基于小波分解的事件相关电位多分辨率分析
Brain Cogn. 1995 Apr;27(3):398-438. doi: 10.1006/brcg.1995.1028.
10
xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.用于增强诱发电位的xDAWN算法：在脑机接口中的应用。
IEEE Trans Biomed Eng. 2009 Aug;56(8):2035-43. doi: 10.1109/TBME.2009.2012869. Epub 2009 Jan 23.

引用本文的文献

1
Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.在线脑机接口实验中减少运动伪影的方法：一项系统综述。
Front Hum Neurosci. 2023 Oct 18;17:1251690. doi: 10.3389/fnhum.2023.1251690. eCollection 2023.

本文引用的文献

1
Best practice for single-trial detection of event-related potentials: Application to brain-computer interfaces.事件相关电位单次试验检测的最佳实践：在脑机接口中的应用。
Int J Psychophysiol. 2017 Jan;111:156-169. doi: 10.1016/j.ijpsycho.2016.07.500. Epub 2016 Jul 22.
2
Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.基于黎曼几何的多用户P300脑机接口单试验分类
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1769-72. doi: 10.1109/EMBC.2015.7318721.
3
Optimization of Single-Trial Detection of Event-Related Potentials Through Artificial Trials.
通过人工试验优化事件相关电位的单次试验检测
IEEE Trans Biomed Eng. 2015 Sep;62(9):2170-6. doi: 10.1109/TBME.2015.2417054. Epub 2015 Mar 25.
4
Single-trial classification of EEG in a visual object task using ICA and machine learning.在视觉物体任务中使用独立成分分析（ICA）和机器学习对脑电图（EEG）进行单试验分类。
J Neurosci Methods. 2014 May 15;228:1-14. doi: 10.1016/j.jneumeth.2014.02.014. Epub 2014 Mar 5.
5
Effects of eye artifact removal methods on single trial P300 detection, a comparative study.眼动伪迹去除方法对单次 P300 检测的影响：一项对比研究。
J Neurosci Methods. 2014 Jan 15;221:41-7. doi: 10.1016/j.jneumeth.2013.08.025. Epub 2013 Sep 18.
6
Automatic denoising of single-trial evoked potentials.单次试验诱发电位的自动去噪
Neuroimage. 2013 Feb 1;66:672-80. doi: 10.1016/j.neuroimage.2012.10.062. Epub 2012 Nov 7.
7
Single-trial analyses: why bother?单次试验分析：为何要费心？
Front Psychol. 2011 Nov 8;2:322. doi: 10.3389/fpsyg.2011.00322. eCollection 2011.
8
Visual modifications on the P300 speller BCI paradigm.对P300拼写器脑机接口范式的视觉修改。
J Neural Eng. 2009 Aug;6(4):046011. doi: 10.1088/1741-2560/6/4/046011. Epub 2009 Jul 15.
9
xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.用于增强诱发电位的xDAWN算法：在脑机接口中的应用。
IEEE Trans Biomed Eng. 2009 Aug;56(8):2035-43. doi: 10.1109/TBME.2009.2012869. Epub 2009 Jan 23.
10
Evaluation of a technique to measure latency jitter in event-related potentials.一种测量事件相关电位中潜伏期抖动技术的评估。
J Neurosci Methods. 2008 Feb 15;168(1):248-55. doi: 10.1016/j.jneumeth.2007.09.031. Epub 2007 Oct 5.