• 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

一种从脑电图中去除眼动伪迹的谱方法。

A spectral method for removing eye movement artifacts from the EEG.

作者信息

Whitton J L, Lue F, Moldofsky H

出版信息

Electroencephalogr Clin Neurophysiol. 1978 Jun;44(6):735-41. doi: 10.1016/0013-4694(78)90208-0.

DOI:10.1016/0013-4694(78)90208-0
PMID:78802
Abstract

A frequency-domain technique for compensating for eye artifacts in mid-line scalp EEG when power spectra are to be calculated is reported. It has been tested in 12 subjects during voluntary and random eye movements. The method which involves calculating the ratio of EEG and EOG spectra adequately controls for low-frequency components in the EEG due to eye artifacts and allows recovery of the dominant peaks in the scalp EEG spectrum.

摘要

本文报道了一种在计算中线头皮脑电图(EEG)功率谱时补偿眼电伪迹的频域技术。该技术已在12名受试者进行自主和随机眼球运动时进行了测试。该方法通过计算EEG和EOG频谱的比值,能够充分控制由于眼电伪迹导致的EEG低频成分,并可恢复头皮EEG频谱中的主峰。

相似文献

1
A spectral method for removing eye movement artifacts from the EEG.一种从脑电图中去除眼动伪迹的谱方法。
Electroencephalogr Clin Neurophysiol. 1978 Jun;44(6):735-41. doi: 10.1016/0013-4694(78)90208-0.
2
Automatic removal of eye-movement and blink artifacts from EEG signals.自动去除 EEG 信号中的眼动和眨眼伪迹。
Brain Topogr. 2010 Mar;23(1):105-14. doi: 10.1007/s10548-009-0131-4. Epub 2009 Dec 29.
3
Online removal of eye movement and blink EEG artifacts using a high-speed eye tracker.使用高速眼动追踪器在线去除眼动和眨眼 EEG 伪迹。
IEEE Trans Biomed Eng. 2012 Aug;59(8):2103-10. doi: 10.1109/TBME.2011.2108295. Epub 2011 Jan 28.
4
Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework.增强基于云的临床应用中的 EEG 数据质量和精度:SLOG 框架评估。
Biomed Phys Eng Express. 2024 Oct 4;10(6). doi: 10.1088/2057-1976/ad7e2d.
5
Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG).利用眼电图(EOG)增强对手部外骨骼的脑机接口(BMI)控制。
J Neuroeng Rehabil. 2014 Dec 16;11:165. doi: 10.1186/1743-0003-11-165.
6
The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain.通过频域回归分析从脑电图中去除眼动伪迹。
Biol Psychol. 1983 Feb-Mar;16(1-2):127-47. doi: 10.1016/0301-0511(83)90059-5.
7
Removal of the ocular artifacts from EEG data using a cascaded spatio-temporal processing.使用级联时空处理从脑电图数据中去除眼部伪迹。
Comput Methods Programs Biomed. 2006 Aug;83(2):95-103. doi: 10.1016/j.cmpb.2006.03.009. Epub 2006 Aug 1.
8
[Procedures for on-line minimizing of eyelid and vertical eye movement artefacts in the EEG].[脑电图中在线最小化眼睑和垂直眼球运动伪迹的程序]
EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb. 1988 Jun;19(2):96-100.
9
Removal of ocular artifacts from the EEG: a comparison between time-domain regression method and adaptive filtering method using simulated data.从脑电图中去除眼部伪迹:使用模拟数据对时域回归方法和自适应滤波方法的比较
Med Biol Eng Comput. 2007 May;45(5):495-503. doi: 10.1007/s11517-007-0179-9. Epub 2007 Mar 16.
10
A model-based objective evaluation of eye movement correction in EEG recordings.脑电图记录中眼动校正的基于模型的客观评估。
IEEE Trans Biomed Eng. 2006 Feb;53(2):246-53. doi: 10.1109/TBME.2005.862533.

引用本文的文献

1
A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications.基于脑电图的情绪识别中深度学习的全面综述:分类、趋势及实际意义
PeerJ Comput Sci. 2024 May 23;10:e2065. doi: 10.7717/peerj-cs.2065. eCollection 2024.
2
A time-frequency denoising method for single-channel event-related EEG.一种用于单通道事件相关脑电信号的时频去噪方法。
Front Neurosci. 2022 Nov 25;16:991136. doi: 10.3389/fnins.2022.991136. eCollection 2022.
3
Removal of Artifacts from EEG Signals: A Review.脑电信号去伪迹:综述。
Sensors (Basel). 2019 Feb 26;19(5):987. doi: 10.3390/s19050987.
4
Keep your opponents close: social context affects EEG and fEMG linkage in a turn-based computer game.与对手保持近距离:社交背景在一款回合制电脑游戏中影响脑电图(EEG)和肌电图(fEMG)的关联。
PLoS One. 2013 Nov 20;8(11):e78795. doi: 10.1371/journal.pone.0078795. eCollection 2013.
5
Mutual-information-based approach for neural connectivity during self-paced finger lifting task.基于互信息的自定步速手指抬起任务中神经连通性的方法。
Hum Brain Mapp. 2008 Mar;29(3):265-80. doi: 10.1002/hbm.20386.
6
Removal of ocular artifacts from the EEG: a comparison between time-domain regression method and adaptive filtering method using simulated data.从脑电图中去除眼部伪迹:使用模拟数据对时域回归方法和自适应滤波方法的比较
Med Biol Eng Comput. 2007 May;45(5):495-503. doi: 10.1007/s11517-007-0179-9. Epub 2007 Mar 16.
7
A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition.一种基于经验模态分解的脑电图同步检测新方法。
J Comput Neurosci. 2007 Aug;23(1):79-111. doi: 10.1007/s10827-007-0020-3. Epub 2007 Feb 2.
8
Removal of ocular artifacts from electro-encephalogram by adaptive filtering.通过自适应滤波去除脑电图中的眼部伪迹。
Med Biol Eng Comput. 2004 May;42(3):407-12. doi: 10.1007/BF02344717.
9
Analysis and visualization of single-trial event-related potentials.单次试验事件相关电位的分析与可视化
Hum Brain Mapp. 2001 Nov;14(3):166-85. doi: 10.1002/hbm.1050.
10
Ocular artifacts in recording EEGs and event-related potentials. II: Source dipoles and source components.脑电图和事件相关电位记录中的眼部伪迹。II:源偶极子和源成分。
Brain Topogr. 1993 Fall;6(1):65-78. doi: 10.1007/BF01234128.