A spelling device for the paralysed

Birbaumer, Niels; Ghanayim, Nimr; Hinterberger, Thilo; Iversen, Iver H.; Kotchoubey, Boris; Kübler, Andrea; Perelmouter, J.; Taub, Edward; Flor, Herta

doi:10.1038/18581

Cited by 1,375 publications

(831 citation statements)

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“…In contrast, a brain-computer interface (BCI) is a technology that utilizes neurophysiological signals directly from the brain to control external devices, bypassing the natural muscular output [1]. Currently, BCI systems based on electroencephalography (EEG) can provide severely motordisabled people with a new output channel to voluntarily control applications for communication and environmental control [2,3,4,5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

The auditory P300-based single-switch brain–computer interface: Paradigm transition from healthy subjects to minimally conscious patients

Pokorny

Klobassa

Pichler

et al. 2013

Artificial Intelligence in Medicine

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Objective: Within this work an auditory P300 brain-computer interface (BCI) based on tone stream segregation, which allows for binary decisions, was developed and evaluated. Materials and methods: Two tone streams consisting of short beep tones with infrequently appearing deviant tones at random positions were used as stimuli. This paradigm was evaluated in 10 healthy subjects and applied to 12 patients in a minimally conscious state (MCS) at clinics in Graz, Würzburg, Rome, and Liège. A stepwise linear discriminant analysis (SWLDA) classifier with 10 × 10 cross-validation was used to detect the presence of any P300 and to investigate attentional modulation of the P300 amplitude. Results:The results for healthy subjects were promising and most classification results were better than random. However, for MCS patients only a small number of classification results were above chance level and none of the results were sufficient for communication purposes. Nevertheless, signs of consciousness were detected in most patients, not on a single-trial basis, but after averaging of corresponding data segments and computing significant differences. These significant results, however, strongly varied across sessions and conditions. Conclusion: This work shows the transition of a paradigm from healthy subjects to MCS patients. Promising results with healthy subjects are, however, no guarantee of good results with patients. Therefore, more investigations are required before any definite conclusions about the usability of this paradigm for MCS patients can be drawn. Nevertheless, this paradigm might offer an opportunity to support bedside clinical assessment of MCS patients and eventually, to provide them with a means of communication.

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Section: Introductionmentioning

confidence: 99%

The auditory P300-based single-switch brain–computer interface: Paradigm transition from healthy subjects to minimally conscious patients

Pokorny

Klobassa

Pichler

et al. 2013

Artificial Intelligence in Medicine

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“…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%

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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“…Numerous studies over the past two decades indicate that scalp-recorded EEG activity can be the basis for non-muscular communication and control systems, commonly called brain-computer interfaces (BCIs) (Birbaumer et al, 1999;Farwell and Donchin, 1988;Pfurtscheller et al, 1993;Wolpaw et al, 1991). EEG-based communication systems measure specific features of EEG activity and use the results as control signals.…”

Section: Introductionmentioning

confidence: 99%

Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis

McFarland¹,

2008

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People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders; and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain-computer interface (BCI) operation. Increasing model order and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.

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A spelling device for the paralysed

Cited by 1,375 publications

References 0 publications

The auditory P300-based single-switch brain–computer interface: Paradigm transition from healthy subjects to minimally conscious patients

The auditory P300-based single-switch brain–computer interface: Paradigm transition from healthy subjects to minimally conscious patients

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

Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis

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