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文献精选 > 高引权威"脑机接口"文献

Brain-computer interfaces in medicine. ​

文献信息 ​

DOI10.1016/j.mayocp.2011.12.008
PMID22325364
期刊Mayo Clinic proceedings
影响因子6.7
JCR 分区Q1
发表年份2012
被引次数125
关键词脑-计算机接口, 神经肌肉障碍, 信号采集, 康复, 医疗应用
文献类型Journal Article, Review
ISSN0025-6196
页码268-79
期号87(3)
作者Jerry J Shih, Dean J Krusienski, Jonathan R Wolpaw

一句话小结 ​

脑-计算机接口（BCI）旨在通过获取和分析脑信号来替代或恢复因神经肌肉疾病导致的功能障碍，研究发现其在控制设备如假肢和轮椅方面具有潜力，并在中风康复中发挥重要作用。随着技术进步，BCI的未来发展将依赖于信号采集硬件的便捷性、安全性、系统在实际应用中的有效性以及性能的可靠性，以实现更自然的肌肉功能替代。

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脑-计算机接口 · 神经肌肉障碍 · 信号采集 · 康复 · 医疗应用

摘要 ​

脑-计算机接口（BCI）获取脑信号，对其进行分析，并将其转换为命令，这些命令被传递给输出设备以执行所需的动作。BCI不使用正常的神经肌肉输出通路。BCI的主要目标是替代或恢复因神经肌肉疾病（如肌萎缩侧索硬化症、脑瘫、中风或脊髓损伤）而残疾人士的有用功能。从最初的基于脑电图的拼写演示和单神经元控制设备的研究开始，研究人员逐渐使用脑电图、皮层内、皮层电图以及其他脑信号实现对光标、机器人手臂、假肢、轮椅和其他设备的越来越复杂的控制。脑-计算机接口在中风后的康复和其他疾病中也可能发挥重要作用。未来，它们可能增强外科医生或其他医疗专业人员的表现。脑-计算机接口技术是一个快速增长的研究与开发领域，令科学家、工程师、临床医生以及大众感到兴奋。其未来的成就将依赖于三个关键领域的进展。脑-计算机接口需要便捷、便携、安全，并且能够在各种环境中正常工作的信号采集硬件。脑-计算机接口系统需要在长期研究中验证其在重度残疾人士实际使用中的效果，并必须实施有效且可行的模型以广泛传播。最后，BCI的日常和瞬时性能可靠性必须提高，以使其接近自然肌肉功能的可靠性。

英文摘要 ​

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.

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主要研究问题 ​

  1. 在脑机接口的研究中，哪些新兴技术可能会对信号采集硬件的便携性和安全性产生重大影响？
  2. 脑机接口在康复治疗中的应用效果如何？是否有成功的案例可以分享？
  3. 目前脑机接口的临床验证进展如何？在长期使用中的效果和挑战是什么？
  4. 除了神经肌肉疾病，脑机接口在其他医疗领域（如精神疾病或认知障碍）的潜在应用有哪些？
  5. 如何评估脑机接口在医疗环境中的可靠性，特别是在与自然肌肉功能的比较中？

核心洞察 ​

研究背景和目的 ​

脑-计算机接口（BCI）是一种通过获取脑信号、分析并将其转化为命令，从而控制外部设备的计算机系统。BCI的主要目标是为因神经肌肉障碍（如肌萎缩侧索硬化症、脑瘫、中风或脊髓损伤）而失去功能的人群提供替代或恢复有用的功能。本文旨在为医学界提供BCI的概述，探讨其发展历程、应用、面临的挑战及未来前景。

主要方法/材料/实验设计 ​

BCI系统由四个主要组成部分构成：

  1. 信号获取：通过电极记录脑信号。
  2. 特征提取：分析信号以识别与用户意图相关的特征。
  3. 特征翻译：将提取的特征转化为设备控制命令。
  4. 设备输出：执行相应的动作，并向用户提供反馈。

以下是BCI系统的技术路线图：

Mermaid diagram

关键结果和发现 ​

  • BCI的研究表明，通过不同的脑信号（如EEG、ECoG和微电极阵列）可以实现对计算机光标、机器人手臂、假肢和轮椅的控制。
  • 目前，BCI技术在实验室取得了显著成就，但实际应用仍处于初期阶段，主要集中在低自由度的连续运动控制和离散选择上。
  • 研究表明，BCI在中风后康复和其他神经损伤的治疗中具有潜在应用价值。

主要结论/意义/创新性 ​

BCI技术有望在未来为重度残疾人士提供有效的沟通和控制手段，改善其生活质量。当前BCI的研究和开发需要解决三个关键问题：信号获取硬件的便携性和安全性、BCI的长期验证和有效传播、以及BCI性能的可靠性。

研究局限性和未来方向 ​

  • 目前BCI技术的应用大多局限于实验室环境，尚未广泛推广至家庭使用，面临用户群体小、商业化吸引力不足等挑战。
  • 未来研究需聚焦于提高BCI的可靠性，使其在真实环境中的表现接近自然肌肉功能，确保用户能够独立使用。
  • 此外，BCI系统的多学科团队合作和长期临床研究将是实现其临床应用的关键。
关键领域当前挑战未来方向
信号获取便携性、安全性、稳定性开发干电极、便携式设备
验证与传播长期有效性、用户适用性进行多中心临床试验
可靠性当前性能不足增强用户和BCI之间的适应性

BCI的未来取决于技术进步、临床验证和商业化策略的有效实施，期望在不久的将来为更广泛的人群提供服务。

参考文献 ​

  1. Cognitive control signals for neural prosthetics. - S Musallam;B D Corneil;B Greger;H Scherberger;R A Andersen - Science (New York, N.Y.) (2004)
  2. An efficient P300-based brain-computer interface for disabled subjects. - Ulrich Hoffmann;Jean-Marc Vesin;Touradj Ebrahimi;Karin Diserens - Journal of neuroscience methods (2008)
  3. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. - Kai J Miller;Gerwin Schalk;Eberhard E Fetz;Marcel den Nijs;Jeffrey G Ojemann;Rajesh P N Rao - Proceedings of the National Academy of Sciences of the United States of America (2010)
  4. Brain-computer interfaces based on the steady-state visual-evoked response. - M Middendorf;G McMillan;G Calhoun;K S Jones - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2000)
  5. Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. - Simon P Kelly;Edmund C Lalor;Richard B Reilly;John J Foxe - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2005)
  6. Multichannel EEG-based brain-computer communication. - J R Wolpaw;D J McFarland - Electroencephalography and clinical neurophysiology (1994)
  7. Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG). - Peter Brunner;Anthony L Ritaccio;Joseph F Emrich;Horst Bischof;Gerwin Schalk - Frontiers in neuroscience (2011)
  8. Brain-computer interfaces in neurological rehabilitation. - Janis J Daly;Jonathan R Wolpaw - The Lancet. Neurology (2008)
  9. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. - Christian Enzinger;Stefan Ropele;Franz Fazekas;Marisa Loitfelder;Faton Gorani;Thomas Seifert;Gudrun Reiter;Christa Neuper;Gert Pfurtscheller;Gernot Müller-Putz - Experimental brain research (2008)
  10. Neurophysiologic correlates of fMRI in human motor cortex. - Dora Hermes;Kai J Miller;Mariska J Vansteensel;Erik J Aarnoutse;Frans S S Leijten;Nick F Ramsey - Human brain mapping (2012)

引用本文的文献 ​

  1. Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects. - Olesya A Mokienko;Alexander V Chervyakov;Sofia N Kulikova;Pavel D Bobrov;Liudmila A Chernikova;Alexander A Frolov;Mikhail A Piradov - Frontiers in computational neuroscience (2013)
  2. Low-latency multi-threaded processing of neuronal signals for brain-computer interfaces. - Jörg Fischer;Tomislav Milekovic;Gerhard Schneider;Carsten Mehring - Frontiers in neuroengineering (2014)
  3. Empirical models of scalp-EEG responses using non-concurrent intracranial responses. - Komalpreet Kaur;Jerry J Shih;Dean J Krusienski - Journal of neural engineering (2014)
  4. Classification of four-class motor imagery employing single-channel electroencephalography. - Sheng Ge;Ruimin Wang;Dongchuan Yu - PloS one (2014)
  5. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. - Kai Keng Ang;Cuntai Guan;Kok Soon Phua;Chuanchu Wang;Longjiang Zhou;Ka Yin Tang;Gopal J Ephraim Joseph;Christopher Wee Keong Kuah;Karen Sui Geok Chua - Frontiers in neuroengineering (2014)
  6. Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: toward a next generation closed-loop neural prosthesis. - Peter J Grahn;Grant W Mallory;B Michael Berry;Jan T Hachmann;Darlene A Lobel;J Luis Lujan - Frontiers in neuroscience (2014)
  7. Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury. - Rüdiger Rupp - Frontiers in neuroengineering (2014)
  8. "Messing with the mind": evolutionary challenges to human brain augmentation. - Arthur Saniotis;Maciej Henneberg;Jaliya Kumaratilake;James P Grantham - Frontiers in systems neuroscience (2014)
  9. Non-invasive control interfaces for intention detection in active movement-assistive devices. - Joan Lobo-Prat;Peter N Kooren;Arno H A Stienen;Just L Herder;Bart F J M Koopman;Peter H Veltink - Journal of neuroengineering and rehabilitation (2014)
  10. Future think: cautiously optimistic about brain augmentation using tissue engineering and machine interface. - E Paul Zehr - Frontiers in systems neuroscience (2015)

... (115 更多 篇文献)

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