Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface

Kübler, Andrea; Nijboer, Femke; Mellinger, Jürgen; Vaughan, Theresa M.; Pawelzik, H.; Schalk, Gerwin; McFarland, Dennis J.; Birbaumer, Niels; Wolpaw, Jonathan R.

doi:10.1212/01.wnl.0000158616.43002.6d

Cited by 484 publications

(339 citation statements)

References 9 publications

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“…All patients displayed brain signal modulations over the expected centro-parietal scalp positions. This confirms findings in [5][12] [14] and extends them to other neurological disorders (DMD and SMA). Our study is thus additional evidence that people with severely disabling neuromuscular or neurological disorders can acquire and maintain control over detectable aspects of brain signals, and use this control to drive output devices.…”

Section: Discussionsupporting

confidence: 89%

“…[5][12] [14]. Although the improvement of quality-of-life brought by such an interface is expected to be relevant only for those patients who are not able to perform any voluntarily controlled movement, the advances in the BCI field are expected to increase the performance of this communication channel, thus making it effective for a broader population of individuals.…”

Section: Discussionmentioning

confidence: 99%

“…Brain Computer Interface (BCI) technology "gives their users communication and control channels that do not depend on the brain's normal output channels of peripheral nerves and muscles." [3], and can allow completely paralyzed individuals to communicate with the surrounding environment [4] [5]. A BCI detects activation patterns in the brain that correspond to the subject's intent.…”

Section: Introductionmentioning

confidence: 99%

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

Cincotti

Mattia

Aloise

et al. 2008

Brain Research Bulletin

272

150

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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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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Cincotti

Mattia

Aloise

et al. 2008

Brain Research Bulletin

272

150

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“…Noninvasive BCIs use electroencephalographic (EEG) activity recorded from the scalp (Farwell and Donchin, 1988;Wolpaw et al, 1991;Sutter, 1992;McFarland et al, 1993McFarland et al, , 2008Pfurtscheller et al, 1993Pfurtscheller et al, , 2000McFarland, 1994, 2004;Birbaumer et al, 1999;Millán et al, 2004;Kübler et al, 2005;Blankertz et al, 2006;Vaughan et al, 2006;Müller et al, 2008). Although EEG-based BCIs support higher performance than often assumed, the acquisition of high levels of brain-based control typically requires extensive user training, and BCI performance can also be variable.…”

Section: Translating Microscale Neural Interface Technologies For CLImentioning

confidence: 99%

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Kipke¹,

Shain²,

Buzsáki³

et al. 2008

J. Neurosci.

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258

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IntroductionTechnological advances in neural interfaces are providing increasingly more powerful "toolkits" of designs, materials, components, and integrated devices for establishing high-fidelity chronic neural interfaces. For a broad class of neuroscience studies, the primary requirements of these interfaces include recording and/or stimulating from a number of discretely sampled volumes at requisite spatial resolutions for specific periods of time. Translational and clinical applications present additional requirements for safety, usability, reliability, patient acceptance, and cost effectiveness. Innovative solutions result from the constructive tension between ever-increasing application requirements and incorporation of technological advances into usable devices. The purpose of this minireview is to present snapshots of the current state-of-the-art in chronic, microscale neural interfaces by highlighting several leading neuroscience applications and discussing their implications for next-generation interface devices.

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“…et al, 1998) or attempted (Kauhanen et al, 2004). Unfortunately, not all users show ERD in motor imagery, although it is possible to train healthy subjects (Guger et al, 2003) as well as patients with ALS (Kübler et al, 2005) to control their SMR such that the recorded activity becomes more classifiable. When present, ERD can be detected relatively easily and is therefore used in the majority of BCI studies.…”

Section: Neurological Phenomena Of Imagined Movementmentioning

confidence: 99%

Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals

Hill

Lal

Schröder³

et al. 2006

Lecture Notes in Computer Science

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We present the results from three motor-imagery-based Brain-Computer Interface experiments. Brain signals were recorded from 8 untrained subjects using EEG, 4 using ECoG and 10 using MEG. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in the clinical application of a BCI, so we aim to obtain the best possible classification performance in a short time. Accordingly, the burden of adaptation is on the side of the computer rather than the user, so we must adopt a machine-learning approach to the analysis. We introduce the required machine-learning vocabulary and concepts, and then present quantitative results that focus on two main issues. The first is the effect of the number of trials-how long does the recording session need to be? We find that good performance could be achieved, on average, after the first 200 trials in EEG, 75-100 trials in MEG, or 25-50 trials in ECoG. The second issue is the effect of spatial filtering-we compare the performance of the original sensor signals with that of the outputs of Independent Component Analysis and the Common Spatial Pattern algorithm, in each of the three sensor types. We find that spatial filtering does not help in MEG, helps a little in ECoG, and improves performance a great deal in EEG. The unsupervised ICA algorithm performed at least as well as the supervised CSP algorithm in all cases-the latter suffered from poor generalization performance due to overfitting in ECoG and MEG, although this could be alleviated by reducing the number of sensors used as input to the algorithm.

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Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface

Cited by 484 publications

References 9 publications

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

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

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals

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