• 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

相似文献

1
A general P300 brain-computer interface presentation paradigm based on performance guided constraints.基于性能引导约束的通用 P300 脑-机接口呈现范式。
Neurosci Lett. 2012 Dec 7;531(2):63-8. doi: 10.1016/j.neulet.2012.08.041. Epub 2012 Aug 29.
2
Pushing the P300-based brain-computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain.将基于P300的脑机接口扩展至超过100次/分钟:将性能引导约束扩展到时间域
J Neural Eng. 2016 Apr;13(2):026024. doi: 10.1088/1741-2560/13/2/026024. Epub 2016 Feb 25.
3
A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.一种新颖的基于 P300 的脑机接口刺激呈现范式:超越行和列。
Clin Neurophysiol. 2010 Jul;121(7):1109-20. doi: 10.1016/j.clinph.2010.01.030. Epub 2010 Mar 26.
4
Suppressing flashes of items surrounding targets during calibration of a P300-based brain-computer interface improves performance.在基于 P300 的脑机接口的校准过程中抑制目标周围项目的闪烁可以提高性能。
J Neural Eng. 2011 Apr;8(2):025024. doi: 10.1088/1741-2560/8/2/025024. Epub 2011 Mar 24.
5
A P300 brain-computer interface based on a modification of the mismatch negativity paradigm.一种基于失配负波范式修正的 P300 脑-机接口。
Int J Neural Syst. 2015 May;25(3):1550011. doi: 10.1142/S0129065715500112. Epub 2015 Feb 26.
6
The P300-based brain-computer interface (BCI): effects of stimulus rate.基于 P300 的脑-机接口(BCI):刺激率的影响。
Clin Neurophysiol. 2011 Apr;122(4):731-7. doi: 10.1016/j.clinph.2010.10.029. Epub 2010 Nov 9.
7
Improving the Cross-Subject Performance of the ERP-Based Brain-Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank.利用快速序列视觉呈现和相关分析排序提高基于事件相关电位的脑机接口的跨主体性能。
Front Hum Neurosci. 2020 Jul 31;14:296. doi: 10.3389/fnhum.2020.00296. eCollection 2020.
8
Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface.基于事件相关电位的脑-机接口的刺激呈现模式优化。
Med Biol Eng Comput. 2011 Feb;49(2):181-91. doi: 10.1007/s11517-010-0689-8. Epub 2010 Oct 2.
9
The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement.P300BCI 不断变化的面貌:涉及面孔、情感和运动的 P300BCI 中刺激变化的比较。
PLoS One. 2012;7(11):e49688. doi: 10.1371/journal.pone.0049688. Epub 2012 Nov 26.
10
Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.基于性能预测的 P300 脑-机接口刺激呈现范式设计优化。
J Neural Eng. 2017 Aug;14(4):046025. doi: 10.1088/1741-2552/aa7525.

引用本文的文献

1
An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling.一种用于在脑机接口中生成最优拼写刺激序列的主动RBSE框架。
IEEE Trans Signal Process. 2017 Oct 15;65(20):5381-5392. doi: 10.1109/TSP.2017.2728500. Epub 2017 Jul 17.
2
Probabilistic Simulation Framework for EEG-Based BCI Design.基于脑电图的脑机接口设计的概率模拟框架
Brain Comput Interfaces (Abingdon). 2016;3(4):171-185. doi: 10.1080/2326263X.2016.1252621. Epub 2016 Dec 5.
3
Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.肌萎缩侧索硬化症患者在家中使用语言模型分类器进行在线脑机接口打字
Brain Comput Interfaces (Abingdon). 2017;4(1-2):114-121. doi: 10.1080/2326263X.2016.1252143. Epub 2016 Nov 15.
4
Performance improvement of ERP-based brain-computer interface via varied geometric patterns.基于 ERP 的脑-机接口通过变化的几何图形进行性能提升。
Med Biol Eng Comput. 2017 Dec;55(12):2245-2256. doi: 10.1007/s11517-017-1671-5. Epub 2017 Jun 28.
5
Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.基于性能预测的 P300 脑-机接口刺激呈现范式设计优化。
J Neural Eng. 2017 Aug;14(4):046025. doi: 10.1088/1741-2552/aa7525.
6
A comparison of stimulus types in online classification of the P300 speller using language models.使用语言模型对P300拼写器进行在线分类时刺激类型的比较。
PLoS One. 2017 Apr 13;12(4):e0175382. doi: 10.1371/journal.pone.0175382. eCollection 2017.
7
Optimizing the Face Paradigm of BCI System by Modified Mismatch Negative Paradigm.基于修正失配负波范式优化脑机接口系统的面部范式
Front Neurosci. 2016 Oct 7;10:444. doi: 10.3389/fnins.2016.00444. eCollection 2016.
8
Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future.第五届国际脑机接口会议研讨会:定义未来
Brain Comput Interfaces (Abingdon). 2014 Jan;1(1):27-49. doi: 10.1080/2326263X.2013.876724.
9
An efficient ERP-based brain-computer interface using random set presentation and face familiarity.一种基于事件相关电位的高效脑机接口,采用随机集呈现和面部熟悉度。
PLoS One. 2014 Nov 10;9(11):e111157. doi: 10.1371/journal.pone.0111157. eCollection 2014.
10
Towards user-friendly spelling with an auditory brain-computer interface: the CharStreamer paradigm.迈向使用听觉脑机接口实现用户友好的拼写:CharStreamer范式。
PLoS One. 2014 Jun 2;9(6):e98322. doi: 10.1371/journal.pone.0098322. eCollection 2014.

本文引用的文献

1
An adaptive P300-based control system.基于自适应 P300 的控制系统。
J Neural Eng. 2011 Jun;8(3):036006. doi: 10.1088/1741-2560/8/3/036006. Epub 2011 Apr 8.
2
Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface.基于事件相关电位的脑-机接口的刺激呈现模式优化。
Med Biol Eng Comput. 2011 Feb;49(2):181-91. doi: 10.1007/s11517-010-0689-8. Epub 2010 Oct 2.
3
A new P300 stimulus presentation pattern for EEG-based spelling systems.一种用于基于脑电图的拼写系统的新型P300刺激呈现模式。
Biomed Tech (Berl). 2010 Aug;55(4):203-10. doi: 10.1515/BMT.2010.029.
4
A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.一种新颖的基于 P300 的脑机接口刺激呈现范式:超越行和列。
Clin Neurophysiol. 2010 Jul;121(7):1109-20. doi: 10.1016/j.clinph.2010.01.030. Epub 2010 Mar 26.
5
A generative model approach for decoding in the visual event-related potential-based brain-computer interface speller.基于视觉事件相关电位的脑机接口拼写器的解码生成模型方法。
J Neural Eng. 2010 Apr;7(2):26003. doi: 10.1088/1741-2560/7/2/026003. Epub 2010 Feb 18.
6
N200-speller using motion-onset visual response.使用运动起始视觉反应的N200拼字器。
Clin Neurophysiol. 2009 Sep;120(9):1658-66. doi: 10.1016/j.clinph.2009.06.026. Epub 2009 Jul 28.
7
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.
8
Visual stimuli for the P300 brain-computer interface: a comparison of white/gray and green/blue flicker matrices.用于P300脑机接口的视觉刺激:白色/灰色与绿色/蓝色闪烁矩阵的比较
Clin Neurophysiol. 2009 Aug;120(8):1562-6. doi: 10.1016/j.clinph.2009.06.002. Epub 2009 Jun 27.
9
How many people are able to control a P300-based brain-computer interface (BCI)?有多少人能够操控基于P300的脑机接口(BCI)?
Neurosci Lett. 2009 Oct 2;462(1):94-8. doi: 10.1016/j.neulet.2009.06.045. Epub 2009 Jun 21.
10
Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.基于视觉P300事件相关电位的脑机接口拼写器中的重叠和不应期效应。
J Neural Eng. 2009 Apr;6(2):026003. doi: 10.1088/1741-2560/6/2/026003. Epub 2009 Mar 2.

基于性能引导约束的通用 P300 脑-机接口呈现范式。

A general P300 brain-computer interface presentation paradigm based on performance guided constraints.

机构信息

Algoma University, Sault Ste. Marie, Ontario P6A 2G4, Canada.

出版信息

Neurosci Lett. 2012 Dec 7;531(2):63-8. doi: 10.1016/j.neulet.2012.08.041. Epub 2012 Aug 29.

DOI:10.1016/j.neulet.2012.08.041
PMID:22960261
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3646331/
Abstract

An electroencephalographic-based brain-computer interface (BCI) can provide a non-muscular method of communication. A general model for P300-based BCI stimulus presentations is introduced--the "m choose n" or C(m (number of flashes per sequence), n (number of flashes per item)) paradigm, which is a universal extension of the previously reported checkerboard paradigm (CBP). C(m,n) captures all possible (unconstrained) ways to flash target items, and then applies constraints to enhance ERP's produced by attended matrix items. We explore a C(36,5) instance of C(m,n) called the "five flash paradigm" (FFP) and compare its performance to the CBP. Eight subjects were tested in each paradigm, counter-balanced. Twelve minutes of calibration data were used as input to a stepwise linear discriminant analysis to derive classification coefficients used for online classification. Accuracy was consistently high for FFP (88%) and CBP (90%); information transfer rate was significantly higher for the FFP (63 bpm) than the CBP (48 bpm). The C(m,n) is a novel and effective general strategy for organizing stimulus groups. Appropriate choices for "m," "n," and specific constraints can improve presentation paradigms by adjusting the parameters in a subject specific manner. This may be especially important for people with neuromuscular disabilities.

摘要

基于脑电图的脑机接口(BCI)可以提供一种非肌肉的通讯方法。引入了一种基于 P300 的 BCI 刺激呈现的通用模型——“m 选 n”或 C(m(每个序列中的闪烁次数),n(每个项目中的闪烁次数))范式,这是先前报道的棋盘格范式(CBP)的通用扩展。C(m,n) 捕获了闪烁目标项目的所有可能(无约束)方式,然后应用约束来增强被注意的矩阵项目产生的 ERP。我们探索了 C(m,n)的一个 C(36,5)实例,称为“五闪烁范式”(FFP),并将其性能与 CBP 进行了比较。每个范式都有 8 名受试者进行测试,平衡了。使用 12 分钟的校准数据作为输入,进行逐步线性判别分析,得出用于在线分类的分类系数。FFP 的准确性始终很高(88%),CBP(90%);FFP 的信息传输率(63 bpm)明显高于 CBP(48 bpm)。C(m,n) 是一种新颖有效的组织刺激组的通用策略。通过以特定于受试者的方式调整参数,可以选择合适的“m”、“n”和特定的约束,从而改善呈现范式。对于患有神经肌肉障碍的人来说,这可能尤为重要。