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自主式(异步)脑机接口控制虚拟环境中的轮椅:一项四肢瘫痪患者的案例研究。

Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic.

机构信息

Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.

出版信息

Comput Intell Neurosci. 2007;2007:79642. doi: 10.1155/2007/79642.

DOI:10.1155/2007/79642
PMID:18368142
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2272302/
Abstract

The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to "go" from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

摘要

本研究旨在首次证明,脑电波可以被四肢瘫痪患者用于控制虚拟现实(VR)中的轮椅运动。在这个案例研究中,脊髓损伤(SCI)患者通过想象瘫痪的脚部运动,能够在脑电图(EEG)中产生β波爆发。这些β波被用于基于单个双极 EEG 记录的自定步速(异步)脑机接口(BCI)控制。受试者被置于一个虚拟街道中,里面有化身。任务是从一个化身走到街道尽头的另一个化身,但在每个化身前停下来与他们交谈。平均而言,参与者能够以 90%的成功率成功完成这个异步实验,单跑最高可达 100%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59a/2272302/09977f30dff5/CIN2007-79642.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59a/2272302/e5157d62a705/CIN2007-79642.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59a/2272302/af7935daee6a/CIN2007-79642.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59a/2272302/09977f30dff5/CIN2007-79642.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59a/2272302/e5157d62a705/CIN2007-79642.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59a/2272302/af7935daee6a/CIN2007-79642.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59a/2272302/09977f30dff5/CIN2007-79642.003.jpg

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本文引用的文献

1
The self-paced graz brain-computer interface: methods and applications.自主式 Graz 脑-机接口:方法与应用。
Comput Intell Neurosci. 2007;2007:79826. doi: 10.1155/2007/79826.
2
Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation.用于控制神经假体的脑机接口:从同步操作模式到异步操作模式
Biomed Tech (Berl). 2006 Jul;51(2):57-63. doi: 10.1515/BMT.2006.011.
3
15 years of BCI research at Graz University of Technology: current projects.格拉茨工业大学15年的脑机接口研究:当前项目
脑控与用户为中心的智能轮椅:可行性研究。
Sensors (Basel). 2024 May 9;24(10):3000. doi: 10.3390/s24103000.
4
Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.基于深度学习解码的无创脑机接口连续跟踪
PNAS Nexus. 2024 Apr 30;3(4):pgae145. doi: 10.1093/pnasnexus/pgae145. eCollection 2024 Apr.
5
Effectiveness of the Combined Use of a Brain-Machine Interface System and Virtual Reality as a Therapeutic Approach in Patients with Spinal Cord Injury: A Systematic Review.脑机接口系统与虚拟现实联合应用作为脊髓损伤患者治疗方法的有效性:一项系统综述。
Healthcare (Basel). 2023 Dec 17;11(24):3189. doi: 10.3390/healthcare11243189.
6
Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance.基于 EEG 感测的视觉和振动触觉引导的运动想象分类。
Sensors (Basel). 2023 May 25;23(11):5064. doi: 10.3390/s23115064.
7
Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton.通过虚拟现实训练增强的脑机接口,用于控制下肢外骨骼
iScience. 2023 Apr 15;26(5):106675. doi: 10.1016/j.isci.2023.106675. eCollection 2023 May 19.
8
Detection of motor imagery based on short-term entropy of time-frequency representations.基于时频表示的短期熵的运动想象检测。
Biomed Eng Online. 2023 May 4;22(1):41. doi: 10.1186/s12938-023-01102-1.
9
Machine learning in biosignals processing for mental health: A narrative review.用于心理健康的生物信号处理中的机器学习:一项叙述性综述。
Front Psychol. 2023 Jan 13;13:1066317. doi: 10.3389/fpsyg.2022.1066317. eCollection 2022.
10
Learning to control a BMI-driven wheelchair for people with severe tetraplegia.为严重四肢瘫痪患者学习控制由体重指数驱动的轮椅。
iScience. 2022 Nov 18;25(12):105418. doi: 10.1016/j.isci.2022.105418. eCollection 2022 Dec 22.
IEEE Trans Neural Syst Rehabil Eng. 2006 Jun;14(2):205-10. doi: 10.1109/TNSRE.2006.875528.
4
Walking from thought.从思考中走出。
Brain Res. 2006 Feb 3;1071(1):145-52. doi: 10.1016/j.brainres.2005.11.083. Epub 2006 Jan 10.
5
Virtual reality in brain damage rehabilitation: review.虚拟现实在脑损伤康复中的应用:综述
Cyberpsychol Behav. 2005 Jun;8(3):241-62; discussion 263-71. doi: 10.1089/cpb.2005.8.241.
6
Virtual environments for motor rehabilitation: review.用于运动康复的虚拟环境:综述
Cyberpsychol Behav. 2005 Jun;8(3):187-211; discussion 212-9. doi: 10.1089/cpb.2005.8.187.
7
EEG-based neuroprosthesis control: a step towards clinical practice.基于脑电图的神经假体控制:迈向临床实践的一步。
Neurosci Lett. 2005;382(1-2):169-74. doi: 10.1016/j.neulet.2005.03.021. Epub 2005 Apr 2.
8
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.
9
Noninvasive brain-actuated control of a mobile robot by human EEG.通过人类脑电图对移动机器人进行无创脑驱动控制。
IEEE Trans Biomed Eng. 2004 Jun;51(6):1026-33. doi: 10.1109/TBME.2004.827086.
10
Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch.用于异步控制应用的脑机接口设计:对LF-ASD异步脑开关的改进
IEEE Trans Biomed Eng. 2004 Jun;51(6):985-92. doi: 10.1109/TBME.2004.827078.