文献精选 > 高引权威"脑机接口"文献

Brain-computer interfaces for communication and control.

文献信息

DOI	10.1016/s1388-2457(02)00057-3
PMID	12048038
期刊	Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
影响因子	3.6
JCR 分区	Q1
发表年份	2002
被引次数	1105
关键词	脑机接口, 电生理信号, 沟通技术, 神经假体, 用户适应
文献类型	Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, P.H.S., Review
ISSN	1388-2457
页码	767-91
期号	113(6)
作者	Jonathan R Wolpaw, Niels Birbaumer, Dennis J McFarland, Gert Pfurtscheller, Theresa M Vaughan

一句话小结

本研究探讨了脑机接口（BCI）在为严重神经肌肉疾病患者提供沟通和控制能力方面的潜力，强调了通过生理电信号实时提取用户意图的必要性。研究表明，跨学科的合作和算法优化是提升BCI信息传输速率和应用范围的关键，未来有望为运动障碍者及其他用户开辟新的控制和沟通方式。

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脑机接口 · 电生理信号 · 沟通技术 · 神经假体 · 用户适应

摘要

多年来，人们一直推测，脑电图活动或其他生理电测量脑功能的手段可能为向外部世界发送信息和指令提供一种新的非肌肉通道——即脑机接口（BCI）。在过去的15年中，生产性脑机接口研究项目不断涌现。受益于对脑功能的新认识、强大且低成本计算设备的出现，以及对残疾人士需求和潜力的日益关注，这些项目专注于为患有严重神经肌肉疾病（如肌萎缩侧索硬化症、脑干中风和脊髓损伤）的人开发新的辅助沟通和控制技术。其直接目标是为这些可能完全瘫痪或“锁定”的用户提供基本的沟通能力，以便他们能够向护理人员表达自己的愿望，甚至操作文字处理程序或神经假肢。

现今的脑机接口通过多种不同的生理电信号来确定用户的意图。这些信号包括从头皮记录的慢皮层电位、P300电位以及μ波或β波节律，以及由植入电极记录的皮层神经元活动。这些信号实时转换为操作计算机显示器或其他设备的指令。成功操作要求用户在这些信号中编码指令，而脑机接口则从信号中提取指令。因此，用户和脑机接口系统需在初始阶段及持续过程中相互适应，以确保稳定的性能。目前的脑机接口的最大信息传输速率可达10-25比特/分钟。这一有限的容量对那些因严重残疾而无法使用传统辅助沟通方法的人来说是有价值的。与此同时，许多脑机接口技术的潜在应用，如神经假肢控制，可能需要更高的信息传输速率。

未来的进展将依赖于以下几个方面的认识：脑机接口的研究和开发是一个跨学科的问题，涉及神经生物学、心理学、工程学、数学和计算机科学；识别用户能够最佳控制的信号，无论是诱发电位、自发节律还是神经元放电率，这些信号独立于传统运动输出通路的活动；开发帮助用户获得和维持这种控制的训练方法；确定将这些信号转换为设备指令的最佳算法；关注识别和消除诸如肌电图和眼电图活动等伪影；采用精确和客观的程序来评估脑机接口的性能；认识到对脑机接口性能进行长期与短期评估的必要性；识别适当的脑机接口应用，并适当地匹配应用与用户；关注影响用户接受辅助技术的因素，包括使用的便利性、外观美观，以及提供对用户最重要的沟通和控制能力。

脑机接口技术的发展还将受益于对同行评审研究出版物的更大重视，以及避免媒体的夸张和常常误导的关注，因为这种关注往往会在公众中产生不切实际的期望，并在其他研究者中引发怀疑。通过充分认识并有效应对上述所有问题，脑机接口系统最终可能为运动障碍者提供重要的新沟通和控制选择，同时也可能为没有残疾的人提供一个补充的控制通道或在特殊情况下有用的控制通道。

英文摘要

For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.

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主要研究问题

1. 除了严重的神经肌肉疾病，BCI技术是否有可能应用于其他类型的残疾人群？
2. 在BCI系统的设计中，如何有效识别和消除电生理信号中的伪影，以提高系统的准确性？
3. 对于BCI用户，怎样的训练方法可以帮助他们更好地控制信号以实现有效的通信？
4. BCI技术的未来发展中，如何平衡技术复杂性与用户接受度之间的关系？
5. 目前BCI系统的信息传输速率限制，是否会影响其在商业应用中的推广和使用？

核心洞察

1. 研究背景和目的

脑机接口（BCI）是一种新兴技术，能够通过电生理信号，如脑电图（EEG），为严重神经肌肉障碍患者提供非肌肉的交流和控制通道。随着对脑功能理解的深入、低成本计算设备的出现，以及对残疾人士需求的日益关注，BCI的研发在过去15年中取得了显著进展。本研究旨在开发新型的增强通信和控制技术，以帮助如肌萎缩侧索硬化症、脑干中风和脊髓损伤等严重残疾患者，提供基本的沟通能力。

2. 主要方法和发现

当前的BCI系统利用多种电生理信号（如慢皮质电位、P300电位及μ或β节律）从用户脑部获取意图，并将这些信号实时转换为计算机命令。用户需要通过特定的脑电信号编码命令，而BCI系统则需不断适应用户，保证性能的稳定。尽管现有BCI的最大信息传输速率为10-25比特/分钟，但对于一些严重残疾患者而言，这一限制仍然是可用的。研究强调了跨学科合作的重要性，包括神经生物学、心理学、工程学、数学和计算机科学，并指出了训练用户有效控制信号、开发最佳信号转化算法以及消除伪影等技术挑战。

3. 核心结论

BCI技术的未来进展依赖于多方面的努力，包括：确认用户能够独立控制的信号类型、开发有效的用户培训方法、完善信号到设备命令的算法、以及精准评估BCI性能的程序。此外，用户接受度影响因素（如易用性和功能性）也需得到重视。随着这些问题的有效解决，BCI系统有潜力为运动障碍患者提供重要的新沟通和控制选择，并可能为无障碍人士提供额外的控制通道。

4. 研究意义和影响

本研究为BCI技术的发展提供了全面的视角，强调了其在医疗和日常生活中的广泛应用潜力。通过深入探讨BCI的技术挑战和用户需求，研究不仅为未来的技术研发提供了理论基础，也为相关政策的制定和实践的推广奠定了基础。BCI的成功开发将有助于改善残疾人群的生活质量，同时也为非残障用户提供了新的交互方式，具有深远的社会和经济影响。

引用本文的文献

1. Predictors of successful self control during brain-computer communication. - N Neumann;N Birbaumer - Journal of neurology, neurosurgery, and psychiatry (2003)
2. Basic advances and new avenues in therapy of spinal cord injury. - Bruce H Dobkin;Leif A Havton - Annual review of medicine (2004)
3. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. - Jonathan R Wolpaw;Dennis J McFarland - Proceedings of the National Academy of Sciences of the United States of America (2004)
4. Motor imagery classification by means of source analysis for brain-computer interface applications. - Lei Qin;Lei Ding;Bin He - Journal of neural engineering (2004)
5. Motor-related cortical dynamics to intact movements in tetraplegics as revealed by high-resolution EEG. - Donatella Mattia;Febo Cincotti;Marco Mattiocco;Giorgio Scivoletto;Maria Grazia Marciani;Fabio Babiloni - Human brain mapping (2006)
6. Encoding of movement direction in different frequency ranges of motor cortical local field potentials. - Jörn Rickert;Simone Cardoso de Oliveira;Eilon Vaadia;Ad Aertsen;Stefan Rotter;Carsten Mehring - The Journal of neuroscience : the official journal of the Society for Neuroscience (2005)
7. An enhanced time-frequency-spatial approach for motor imagery classification. - Nobuyuki Yamawaki;Christopher Wilke;Zhongming Liu;Bin He - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2006)
8. The beat goes on: rhythmic modulation of cortical potentials by imagined tapping. - Allen Osman;Robert Albert;K Richard Ridderinkhof;Guido Band;Maurits van der Molen - Journal of experimental psychology. Human perception and performance (2006)
9. Adaptive feature extraction for EEG signal classification. - Shiliang Sun;Changshui Zhang - Medical & biological engineering & computing (2006)
10. Study of discriminant analysis applied to motor imagery bipolar data. - Carmen Vidaurre;Reinhold Scherer;Rafael Cabeza;Alois Schlögl;Gert Pfurtscheller - Medical & biological engineering & computing (2007)

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