• 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

迈向自主节奏的脑机通信:在虚拟世界中导航。

Toward self-paced brain-computer communication: navigation through virtual worlds.

作者信息

Scherer Reinhold, Lee Felix, Schlogl Alois, Leeb Robert, Bischof Horst, Pfurtscheller Gert

机构信息

Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Graz 8010, Austria.

出版信息

IEEE Trans Biomed Eng. 2008 Feb;55(2 Pt 1):675-82. doi: 10.1109/TBME.2007.903709.

DOI:10.1109/TBME.2007.903709
PMID:18270004
Abstract

The self-paced control paradigm enables users to operate brain-computer interfaces (BCI) in a more natural way: no longer is the machine in control of the timing and speed of communication, but rather the user is. This is important to enhance the usability, flexibility, and response time of a BCI. In this work, we show how subjects, after performing cue-based feedback training (smiley paradigm), learned to navigate self-paced through the "freeSpace" virtual environment (VE). Similar to computer games, subjects had the task of picking up items by using the following navigation commands: rotate left, rotate right, and move forward ( three classes). Since the self-paced control paradigm allows subjects to make voluntary decisions on time, type, and duration of mental activity, no cues or routing directives were presented. The BCI was based only on three bipolar electroencephalogram channels and operated by motor imagery. Eye movements (electrooculogram) and electromyographic artifacts were reduced and detected online. The results of three able-bodied subjects are reported and problems emerging from self-paced control are discussed.

摘要

自定节奏控制范式使用户能够以更自然的方式操作脑机接口(BCI):不再是机器控制通信的时间和速度,而是用户。这对于提高BCI的可用性、灵活性和响应时间很重要。在这项工作中,我们展示了受试者在进行基于提示的反馈训练(笑脸范式)后,如何学会在“自由空间”虚拟环境(VE)中自定节奏地导航。与电脑游戏类似,受试者的任务是通过使用以下导航命令来拾取物品:向左旋转、向右旋转和向前移动(三类)。由于自定节奏控制范式允许受试者对心理活动的时间、类型和持续时间做出自主决策,因此未呈现任何提示或路线指令。该BCI仅基于三个双极脑电图通道,并通过运动想象进行操作。在线减少并检测眼动(眼电图)和肌电伪迹。报告了三名身体健全受试者的结果,并讨论了自定节奏控制中出现的问题。

相似文献

1
Toward self-paced brain-computer communication: navigation through virtual worlds.迈向自主节奏的脑机通信:在虚拟世界中导航。
IEEE Trans Biomed Eng. 2008 Feb;55(2 Pt 1):675-82. doi: 10.1109/TBME.2007.903709.
2
Continuous EEG classification during motor imagery--simulation of an asynchronous BCI.运动想象期间的连续脑电图分类——异步脑机接口模拟
IEEE Trans Neural Syst Rehabil Eng. 2004 Jun;12(2):258-65. doi: 10.1109/TNSRE.2004.827220.
3
Exploring virtual environments with an EEG-based BCI through motor imagery.通过运动想象,利用基于脑电图的脑机接口探索虚拟环境。
Biomed Tech (Berl). 2005 Apr;50(4):86-91. doi: 10.1515/BMT.2005.012.
4
A two-class brain computer interface to freely navigate through virtual worlds.一种用于在虚拟世界中自由导航的两类脑机接口。
Biomed Tech (Berl). 2009 Jun;54(3):126-33. doi: 10.1515/BMT.2009.014.
5
A fully on-line adaptive BCI.一种全在线自适应脑机接口。
IEEE Trans Biomed Eng. 2006 Jun;53(6):1214-9. doi: 10.1109/TBME.2006.873542.
6
A new discriminative common spatial pattern method for motor imagery brain-computer interfaces.一种新的运动想象脑-机接口的判别式公共空间模式方法。
IEEE Trans Biomed Eng. 2009 Nov;56(11 Pt 2):2730-3. doi: 10.1109/TBME.2009.2026181. Epub 2009 Jul 14.
7
Control of an electrical prosthesis with an SSVEP-based BCI.基于稳态视觉诱发电位的脑机接口对电动假肢的控制
IEEE Trans Biomed Eng. 2008 Jan;55(1):361-4. doi: 10.1109/TBME.2007.897815.
8
Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment.脑机通信:探索虚拟公寓的动机、目标及影响
IEEE Trans Neural Syst Rehabil Eng. 2007 Dec;15(4):473-82. doi: 10.1109/TNSRE.2007.906956.
9
Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces.基于脑电图的脑机接口的在线自适应判别分析研究。
IEEE Trans Biomed Eng. 2007 Mar;54(3):550-6. doi: 10.1109/TBME.2006.888836.
10
Self-paced operation of an SSVEP-Based orthosis with and without an imagery-based "brain switch:" a feasibility study towards a hybrid BCI.基于 SSVEP 的矫形器的自主操作,有无基于想象的“脑开关”:混合 BCI 的可行性研究。
IEEE Trans Neural Syst Rehabil Eng. 2010 Aug;18(4):409-14. doi: 10.1109/TNSRE.2010.2040837. Epub 2010 Feb 8.

引用本文的文献

1
Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.非侵入式脑机接口:现状与趋势
IEEE Rev Biomed Eng. 2025;18:26-49. doi: 10.1109/RBME.2024.3449790. Epub 2025 Jan 28.
2
Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing.通过应用基于在线 EEG 的框架来解码运动意图、轨迹和错误处理,从而实现对脊髓损伤患者的运动控制解锁。
Sci Rep. 2024 Feb 27;14(1):4714. doi: 10.1038/s41598-024-55413-x.
3
Bayesian learning from multi-way EEG feedback for robot navigation and target identification.
基于多通道 EEG 反馈的贝叶斯学习在机器人导航和目标识别中的应用。
Sci Rep. 2023 Oct 7;13(1):16925. doi: 10.1038/s41598-023-44077-8.
4
EOG-Based Human-Computer Interface: 2000-2020 Review.基于眼电图的人机界面:2000-2020 年回顾。
Sensors (Basel). 2022 Jun 29;22(13):4914. doi: 10.3390/s22134914.
5
Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.感受你的触及范围:一个基于脑电图的框架,用于持续检测目标导向运动和错误处理,以控制基于动觉反馈的人工手臂。
Front Hum Neurosci. 2022 Mar 11;16:841312. doi: 10.3389/fnhum.2022.841312. eCollection 2022.
6
Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation.探索用于中风后运动康复的自定步速可具身化神经反馈
Front Hum Neurosci. 2020 Jan 20;13:461. doi: 10.3389/fnhum.2019.00461. eCollection 2019.
7
Comparison between covert sound-production task (sound-imagery) vs. motor-imagery for onset detection in real-life online self-paced BCIs.掩蔽发声任务(声音意象)与运动意象在真实生活中在线自我调节脑机接口启动检测中的比较。
J Neuroeng Rehabil. 2020 Feb 7;17(1):14. doi: 10.1186/s12984-020-0651-4.
8
Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG.使用脑电图估计交互式虚拟现实环境中的认知工作量。
Front Hum Neurosci. 2019 Nov 14;13:401. doi: 10.3389/fnhum.2019.00401. eCollection 2019.
9
A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface.用于在脑机接口上识别心理任务中的意向性的分层架构。
PLoS One. 2019 Jun 18;14(6):e0218181. doi: 10.1371/journal.pone.0218181. eCollection 2019.
10
Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface.利用神经网络和小波分析对生物电信号进行特征提取,以实现对人机接口的远程控制。
Sensors (Basel). 2019 Apr 24;19(8):1923. doi: 10.3390/s19081923.