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利用非侵入式脑机接口模拟计算机鼠标控制

Emulation of computer mouse control with a noninvasive brain-computer interface.

作者信息

McFarland Dennis J, Krusienski Dean J, Sarnacki William A, Wolpaw Jonathan R

机构信息

Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health and State University of New York, Albany, NY 12201, USA.

出版信息

J Neural Eng. 2008 Jun;5(2):101-10. doi: 10.1088/1741-2560/5/2/001. Epub 2008 Mar 5.

DOI:10.1088/1741-2560/5/2/001
PMID:18367779
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2757111/
Abstract

Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.

摘要

脑机接口(BCI)技术能够为严重瘫痪者提供非肌肉方式的通信与控制。BCI可采用非侵入性或侵入性技术来记录传达用户指令的脑信号。尽管非侵入性BCI用于简单应用,但人们常常认为只有使用植入大脑电极的侵入性BCI才能对机械臂或神经假体进行多维顺序控制。本研究表明,一种利用头皮记录的脑电图(EEG)活动和自适应算法的非侵入性BCI能够为包括脊髓损伤患者在内的人群提供二维光标移动和目标选择功能。在电脑屏幕周边呈现多个目标,其中一个被指定为正确目标。用户的任务是利用脑电图将光标从屏幕中心移至正确目标,然后使用额外的脑电图特征来选择目标。如果光标到达错误目标,用户会被指示不要选择它。因此,该任务模拟了鼠标操作的关键特征。结果表明,严重运动功能障碍者能够利用脑信号进行顺序多维运动和选择。

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本文引用的文献

1
Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.沃兹沃思中心的脑机接口信号处理:μ波和感觉运动β节律。
Prog Brain Res. 2006;159:411-9. doi: 10.1016/S0079-6123(06)59026-0.
2
A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.一种P300事件相关电位脑机接口(BCI):矩阵大小和刺激间隔对性能的影响。
Biol Psychol. 2006 Oct;73(3):242-52. doi: 10.1016/j.biopsycho.2006.04.007. Epub 2006 Jul 24.
3
Neuronal ensemble control of prosthetic devices by a human with tetraplegia.
头戴式辅助鼠标控制器对上肢残疾人士的可接受性:使用技术接受模型的实证研究。
PLoS One. 2023 Oct 31;18(10):e0293608. doi: 10.1371/journal.pone.0293608. eCollection 2023.
4
Brain-computer interface: trend, challenges, and threats.脑机接口:趋势、挑战与威胁。
Brain Inform. 2023 Aug 4;10(1):20. doi: 10.1186/s40708-023-00199-3.
5
Real-Time Navigation in Google Street View Using a Motor Imagery-Based BCI.基于运动想象的脑机接口在谷歌街景中的实时导航
Sensors (Basel). 2023 Feb 3;23(3):1704. doi: 10.3390/s23031704.
6
Low-Cost Human-Machine Interface for Computer Control with Facial Landmark Detection and Voice Commands.基于面部地标检测和语音命令的低成本人机界面,用于计算机控制。
Sensors (Basel). 2022 Nov 29;22(23):9279. doi: 10.3390/s22239279.
7
Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.参加残疾人 Cybathlon 锦标赛:对患有四肢瘫痪的 cybathlete 进行长期的运动想象脑机接口训练。
J Neuroeng Rehabil. 2022 Sep 6;19(1):95. doi: 10.1186/s12984-022-01073-9.
8
A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking.一种利用 EEG 和鼠标追踪技术评估成人大脑中动态二元选择的资源。
Sci Data. 2022 Jul 16;9(1):416. doi: 10.1038/s41597-022-01538-5.
9
Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future.用于机器人控制的非侵入式人机接口方法和应用:过去、现在和未来。
Comput Intell Neurosci. 2022 Jun 8;2022:1635672. doi: 10.1155/2022/1635672. eCollection 2022.
10
Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.基于生物启发式机器学习方法的脑-机接口开发中脑电图信号的分类。
Comput Intell Neurosci. 2022 Feb 25;2022:4487254. doi: 10.1155/2022/4487254. eCollection 2022.
四肢瘫痪患者对假肢装置的神经元集群控制
Nature. 2006 Jul 13;442(7099):164-71. doi: 10.1038/nature04970.
4
BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.2005年脑机接口会议——脑机接口信号处理研讨会:特征提取与转换
IEEE Trans Neural Syst Rehabil Eng. 2006 Jun;14(2):135-8. doi: 10.1109/TNSRE.2006.875637.
5
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.
6
EMG activity in selected target muscles during imagery rising on tiptoes in healthy adults and poststroke hemiparetic patients.健康成年人和中风后偏瘫患者在想象踮脚尖起身过程中选定目标肌肉的肌电图活动。
J Mot Behav. 2005 Nov;37(6):475-83. doi: 10.3200/JMBR.37.6.475-483.
7
Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance.基于感觉运动节律的脑机接口(BCI):通过回归进行特征选择可提高性能。
IEEE Trans Neural Syst Rehabil Eng. 2005 Sep;13(3):372-9. doi: 10.1109/TNSRE.2005.848627.
8
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.
9
A brain-computer interface using electrocorticographic signals in humans.一种利用人类皮质脑电图信号的脑机接口。
J Neural Eng. 2004 Jun;1(2):63-71. doi: 10.1088/1741-2560/1/2/001. Epub 2004 Jun 14.
10
Brain-computer interface (BCI) operation: signal and noise during early training sessions.脑机接口(BCI)操作:早期训练阶段的信号与噪声
Clin Neurophysiol. 2005 Jan;116(1):56-62. doi: 10.1016/j.clinph.2004.07.004.