Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Wolpaw, Jonathan R.; McFarland, Dennis J.

doi:10.1073/pnas.0403504101

Cited by 1,299 publications

(1,021 citation statements)

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“…The present data show that the computer-controlled induction of hand ownership alters -at least partly -the same fronto-parietal oscillations that can be used in non-invasive brain computer interfaces. These approaches have utilized motor imagery for motor control via online exploitation of spectral features from the mu-and beta-bands (8-26 Hz) over sensorimotor cortex (Lebedev and Nicolelis, 2006;Millán et al, 2010;Pfurtscheller and Neuper, 2001;Pfurtscheller et al, 1997b;Wolpaw and McFarland, 2004). Based on the present findings, we argue that automatized illusory hand ownership may be used to guide or improve control of external devices including robotic arms using non-invasive brain computer interface technology as well as to control prosthetic arms that are interfaced with the peripheral nervous system (Marasco et al, 2011;Navarro et al, 2005).…”

Section: Shared Spectral and Anatomical Mechanisms Between Motor Imagmentioning

confidence: 59%

Shared electrophysiology mechanisms of body ownership and motor imagery

Evans

Blanke

2013

NeuroImage

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Although we feel, see, and experience our hands as our own (body or hand ownership), recent research has shown that illusory hand ownership can be induced for fake or virtual hands and may be useful for neuroprosthetics and brain-computer interfaces. Despite the vast amount of behavioral data on illusory hand ownership, neuroimaging studies are rare, in particular electrophysiological studies. Thus, while the neural systems underlying hand ownership are relatively well described, the spectral signatures of body ownership as measured by electroencephalography (EEG) remain elusive. Here we induced illusory hand ownership in an automated, computer-controlled manner using virtual reality while recording 64-channel EEG and found that illusory hand ownership is reflected by a body-specific modulation in the mu-band over fronto-parietal cortex. In a second experiment in the same subjects, we then show that mu as well as beta-band activity in highly similar fronto-parietal regions was also modulated during a motor imagery task often used in paradigms employing non-invasive brain-computer interface technology. These data provide insights into the electrophysiological brain mechanisms of illusory hand ownership and their strongly overlapping mechanisms with motor imagery in fronto-parietal cortex. They also highlight the potential of combining high-resolution EEG with virtual reality setups and automatized stimulation protocols for systematic, reproducible stimulus presentation in cognitive neuroscience, and may inform the design of non-invasive brain-computer interfaces.

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Section: Shared Spectral and Anatomical Mechanisms Between Motor Imagmentioning

confidence: 59%

Shared electrophysiology mechanisms of body ownership and motor imagery

Evans

Blanke

2013

NeuroImage

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“…Other BCI systems depend on brain activity recorded non-invasively from the surface of the scalp using electroencephalography (EEG). EEG-based BCIs can be operated by modulations of EEG rhythmic activity located over scalp sensorimotor areas that are induced by motor imagery tasks [11]; these modulations can be used to control a cursor on a computer screen [12] or a prosthetic device for limited hand movements [13] [14]. Thus, it has become conceivable to extend the communication between disabled individuals and the external environment from mere symbolic interaction (e.g.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive brain–computer interface system: Towards its application as assistive technology

Cincotti

Mattia

Aloise

et al. 2008

Brain Research Bulletin

275

147

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The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain Computer Interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options.Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time.Correspondence should be addressed to: Febo Cincotti, PhD, Fondazione Santa Lucia, IRCCS, Via Ardeatina, 306, 00179, Rome, Italy, Phone: +39-06-51501466, Fax: +39-06-51501465, f.cincotti@hsantalucia.it. * These authors equally contributed to the paper Conflict of Interest Declaration: I had full access to all the data in the study and I take responsibility for the integrity of the data and the accuracy of the data analysis. I also state that all authors have read and approved submission of the manuscript; the manuscript contains original material that has not been published and has not being considered for publication elsewhere.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI. NIH Public Access

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“…6 In the late 1970s, Kuhlman 7 showed that the mu rhythm can be enhanced by EEG feedback training. Starting from this information, Wolpaw et al [8][9][10] trained volunteers to control sensorimotor rhythm amplitudes and use them to move a cursor on a computer screen accurately in 1 or 2 dimensions. By 2006, a microelectrode array was implanted in the primary motor cortex of a young man with complete tetraplegia after a C3-C4 cervical injury.…”

Section: Milestones In Bci Developmentmentioning

confidence: 99%

Brain-Computer Interfaces in Medicine

Shih

Krusienski

Wolpaw

2012

Mayo Clinic Proceedings

Self Cite

562

279

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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. U ntil recently, the dream of being able to control one's environment through thoughts had been in the realm of science fiction. However, the advance of technology has brought a new reality: Today, humans can use the electrical signals from brain activity to interact with, influence, or change their environments. The emerging field of brain-computer interface (BCI) technology may allow individuals unable to speak and/or use their limbs to once again communicate or operate assistive devices for walking and manipulating objects. Brain-computer interface research is an area of high public awareness. Videos on YouTube as well as news reports in the lay media indicate intense curiosity and interest in a field that hopefully one day soon will dramatically improve the lives of many disabled persons affected by a number of different disease processes.This review seeks to provide the general medical community with an introduction to BCIs. We define BCI and then review some of the seminal discoveries in this rapidly emerging field, the brain signals used by BCIs, the essential components of a BCI system, current BCI systems, and the key issues now engaging researchers. Challenges are inherent in translating any new technology to practical and useful clinical applications, and BCIs are no exception. We discuss the potent...

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Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Cited by 1,299 publications

References 31 publications

Shared electrophysiology mechanisms of body ownership and motor imagery

Shared electrophysiology mechanisms of body ownership and motor imagery

Non-invasive brain–computer interface system: Towards its application as assistive technology

Brain-Computer Interfaces in Medicine

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