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An MEG-based brain-computer interface (BCI). ​

文献信息 ​

DOI10.1016/j.neuroimage.2007.03.019
PMID17475511
期刊NeuroImage
影响因子4.5
JCR 分区Q1
发表年份2007
被引次数98
关键词脑机接口, 脑电图, 磁脑电图, 运动皮层, 信号处理
文献类型Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
ISSN1053-8119
页码581-93
期号36(3)
作者Jürgen Mellinger, Gerwin Schalk, Christoph Braun, Hubert Preissl, Wolfgang Rosenstiel, Niels Birbaumer, Andrea Kübler

一句话小结 ​

本研究探讨了一种基于磁脑电图（MEG）的脑机接口（BCI），通过自愿的μ波和β波节律的幅度调制实现沟通，显示出在用户训练中具有高效性。研究结果表明，MEG相较于传统的脑电图（EEG）可显著提高沟通速度，为失去肌肉控制的患者提供了更有效的交流方式。

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脑机接口 · 脑电图 · 磁脑电图 · 运动皮层 · 信号处理

摘要 ​

脑机接口（BCI）通过单纯的脑活动实现意图的传递，而无需依赖肌肉。因此，BCI可能为那些失去所有自愿肌肉控制的患者提供了唯一的沟通方式。许多最新研究表明，基于脑电图（EEG）的BCI能够使健康个体和重度瘫痪者进行沟通。尽管这种方法安全且费用低廉，但沟通速度较慢。与EEG相比，磁脑电图（MEG）提供了更高时空分辨率的信号，因此可以用来探讨这些改进的信号特性是否能提高BCI的沟通速度。在本研究中，我们探讨了一种基于MEG的BCI的实用性，该BCI利用自愿的传感运动μ波和β波节律的幅度调制。为了提高信噪比，我们提出了一种简单的空间过滤方法，该方法考虑了MEG中信号传播的几何特性，并提供了能够处理在基于MEG的BCI中特定遇到的伪影的方法。作为示例，六名参与者通过肢体运动的想象，成功接受了二元决策的沟通训练，采用反馈范式。参与者在32分钟的反馈训练内实现了显著的μ波节律自我控制。对于三个参与者的子组，我们将幅度调制信号的来源定位到运动皮层。我们的结果表明，基于MEG的BCI在用户训练方面是可行且高效的。

英文摘要 ​

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

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主要研究问题 ​

  1. MEG与EEG在脑-机接口应用中的优势和劣势分别是什么？
  2. 如何进一步提高MEG-based BCI的通信速度和准确性？
  3. 除了运动想象，是否有其他类型的脑活动可以用于MEG-based BCI？
  4. 在MEG-based BCI的研究中，如何有效处理噪声和伪影？
  5. 未来MEG-based BCI在临床应用中的潜在发展方向是什么？

核心洞察 ​

研究背景和目的 ​

脑机接口（BCI）是一种允许通过脑活动进行意图传达的技术，特别适用于失去自主肌肉控制的患者，如渐冻症或严重的运动障碍患者。尽管基于脑电图（EEG）的BCI已经取得了一定的成功，但其通信速度较慢。因此，本研究旨在探索基于磁脑电图（MEG）的BCI，利用其更高的时空分辨率，是否能提高通信速度和效率。

主要方法/材料/实验设计 ​

本研究涉及六名健康志愿者，采用以下步骤进行实验：

  1. 参与者筛选：所有参与者均为健康成年人，首次接触BCI技术。
  2. 实验设置：参与者坐在屏幕前，进行手脚运动的想象训练。
  3. MEG记录：在磁屏蔽室中使用151个传感器的全头MEG系统进行数据采集。
  4. 实时反馈系统：使用BCI2000系统，提供基于μ和β波幅度的实时反馈。
  5. 训练过程：参与者通过想象运动来控制光标，反馈时间为6.7秒的试验周期。

以下是技术路线的流程图：

Mermaid diagram

关键结果和发现 ​

  • 所有参与者在反馈训练中成功实现了μ波的自我控制，训练时间平均为32分钟。
  • 四名参与者在第一次训练中达到了90%以上的准确率。
  • 通过相位去相关分析（PDA），成功定位了信号源，表明μ波的起源位于运动皮层。

主要结论/意义/创新性 ​

本研究证明了基于MEG的BCI的可行性和有效性，参与者在短时间内能够掌握控制技能。相较于传统的EEG，MEG在时空分辨率上具有优势，能够更快地进行信号处理和反馈。这一研究为未来BCI技术的发展提供了新的思路，特别是在改善通信速度和用户体验方面。

研究局限性和未来方向 ​

  • 参与者数量较少，结果的普遍性需进一步验证。
  • 在不同训练会话中，参与者的头部位置变化影响了信号质量，未来研究应考虑如何在实时环境中更好地控制头部位置。
  • 建议未来的研究探索更复杂的空间滤波方法，以提高信号的信噪比和分类准确性。
研究局限性未来方向
参与者数量有限增加样本量，验证结果的普遍性
头部位置控制不佳研究实时头部定位技术
信号处理方法简单探索更复杂的信号处理技术

参考文献 ​

  1. Mu and beta rhythm topographies during motor imagery and actual movements. - D J McFarland;L A Miner;T M Vaughan;J R Wolpaw - Brain topography (2000)
  2. Brain-computer interface research at the Wadsworth Center. - J R Wolpaw;D J McFarland;T M Vaughan - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2000)
  3. Brain oscillations control hand orthosis in a tetraplegic. - G Pfurtscheller;C Guger;G Müller;G Krausz;C Neuper - Neuroscience letters (2000)
  4. Spatial filter approach for comparison of the forward and inverse problems of electroencephalography and magnetoencephalography. - L A Bradshaw;R S Wijesinghe;J P Wikswo - Annals of biomedical engineering (2001)
  5. Brain-computer interfaces for communication and control. - Jonathan R Wolpaw;Niels Birbaumer;Dennis J McFarland;Gert Pfurtscheller;Theresa M Vaughan - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2002)
  6. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. - Nikolaus Weiskopf;Ralf Veit;Michael Erb;Klaus Mathiak;Wolfgang Grodd;Rainer Goebel;Niels Birbaumer - NeuroImage (2003)
  7. How many people are able to operate an EEG-based brain-computer interface (BCI)? - C Guger;G Edlinger;W Harkam;I Niedermayer;G Pfurtscheller - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2003)
  8. 'Thought'--control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. - Gert Pfurtscheller;Gernot R Müller;Jörg Pfurtscheller;Hans Jürgen Gerner;Rüdiger Rupp - Neuroscience letters (2003)
  9. BCI2000: a general-purpose brain-computer interface (BCI) system. - Gerwin Schalk;Dennis J McFarland;Thilo Hinterberger;Niels Birbaumer;Jonathan R Wolpaw - IEEE transactions on bio-medical engineering (2004)
  10. Localization of realistic cortical activity in MEG using current multipoles. - K Jerbi;S Baillet;J C Mosher;G Nolte;L Garnero;R M Leahy - NeuroImage (2004)

引用本文的文献 ​

  1. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. - Ethan Buch;Cornelia Weber;Leonardo G Cohen;Christoph Braun;Michael A Dimyan;Tyler Ard;Jurgen Mellinger;Andrea Caria;Surjo Soekadar;Alissa Fourkas;Niels Birbaumer - Stroke (2008)
  2. Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals. - Harsha Battapady;Peter Lin;Tom Holroyd;Mark Hallett;Xuedong Chen;Ding-Yu Fei;Ou Bai - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2009)
  3. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. - Wei Wang;Jennifer L Collinger;Monica A Perez;Elizabeth C Tyler-Kabara;Leonardo G Cohen;Niels Birbaumer;Steven W Brose;Andrew B Schwartz;Michael L Boninger;Douglas J Weber - Physical medicine and rehabilitation clinics of North America (2010)
  4. Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. - Trent J Bradberry;Rodolphe J Gentili;José L Contreras-Vidal - The Journal of neuroscience : the official journal of the Society for Neuroscience (2010)
  5. Corticospinal beta-band synchronization entails rhythmic gain modulation. - Gijs van Elswijk;Femke Maij;Jan-Mathijs Schoffelen;Sebastiaan Overeem;Dick F Stegeman;Pascal Fries - The Journal of neuroscience : the official journal of the Society for Neuroscience (2010)
  6. A procedure for measuring latencies in brain-computer interfaces. - J Adam Wilson;Jürgen Mellinger;Gerwin Schalk;Justin Williams - IEEE transactions on bio-medical engineering (2010)
  7. Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. - Joseph N Mak;Jonathan R Wolpaw - IEEE reviews in biomedical engineering (2009)
  8. Decoding and cortical source localization for intended movement direction with MEG. - Wei Wang;Gustavo P Sudre;Yang Xu;Robert E Kass;Jennifer L Collinger;Alan D Degenhart;Anto I Bagic;Douglas J Weber - Journal of neurophysiology (2010)
  9. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. - J D R Millán;R Rupp;G R Müller-Putz;R Murray-Smith;C Giugliemma;M Tangermann;C Vidaurre;F Cincotti;A Kübler;R Leeb;C Neuper;K-R Müller;D Mattia - Frontiers in neuroscience (2010)
  10. rtMEG: a real-time software interface for magnetoencephalography. - Gustavo Sudre;Lauri Parkkonen;Elizabeth Bock;Sylvain Baillet;Wei Wang;Douglas J Weber - Computational intelligence and neuroscience (2011)

... (88 更多 篇文献)

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