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      Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

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          Abstract

          The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to “go” from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

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          Report of the committee on methods of clinical examination in electroencephalography

          (1958)
          Article 0 comments Cited 296 times – based on 0 reviews     Review now
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            Virtual environments for motor rehabilitation: review.

            Maureen K. Holden (2005)
            In this paper, the current "state of the art" for virtual reality (VR) applications in the field of motor rehabilitation is reviewed. The paper begins with a brief overview of available equipment options. Next, a discussion of the scientific rationale for use of VR in motor rehabilitation is provided. Finally, the major portion of the paper describes the various VR systems that have been developed for use with patients, and the results of clinical studies reported to date in the literature. Areas covered include stroke rehabilitation (upper and lower extremity training, spatial and perceptual-motor training), acquired brain injury, Parkinson's disease, orthopedic rehabilitation, balance training, wheelchair mobility and functional activities of daily living training, and the newly developing field of telerehabilitation. Four major findings emerge from these studies: (1) people with disabilities appear capable of motor learning within virtual environments; (2) movements learned by people with disabilities in VR transfer to real world equivalent motor tasks in most cases, and in some cases even generalize to other untrained tasks; (3) in the few studies (n = 5) that have compared motor learning in real versus virtual environments, some advantage for VR training has been found in all cases; and (4) no occurrences of cybersickness in impaired populations have been reported to date in experiments where VR has been used to train motor abilities.
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              Noninvasive brain-actuated control of a mobile robot by human EEG.

              Wulfram Gerstner, Frédéric Renkens, Josep Mouriño … (2004)
              Brain activity recorded noninvasively is sufficient to control a mobile robot if advanced robotics is used in combination with asynchronous electroencephalogram (EEG) analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only, using an EEG-based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a performance ratio of 0.74.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Intell Neurosci
                Journal ID (publisher-id): CIN
                Title: Computational Intelligence and Neuroscience
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1687-5265
                ISSN (Electronic): 1687-5273
                Publication date (Print): 2007
                Publication date (Electronic): 10 September 2007
                Volume: 2007
                Electronic Location Identifier: 79642
                Affiliations
                1Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria
                2Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
                3Sammy Ofer School of Communications, The Interdisciplinary Center, P.O. Box 167, Herzliya 08010, Israel
                4Catalan Institute of Research and Advanced Studies (ICREA), Polytechnic University of Catalunya, 08010 Barcelona, Spain
                Author notes

                Recommended by Andrzej Cichocki

                Article
                DOI: 10.1155/2007/79642
                PMC ID: 2272302
                PubMed ID: 18368142
                SO-VID: ecd4f530-3c4b-402b-9d03-3e5b5a407e4f
                Copyright © Copyright © 2007 Robert Leeb et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 18 February 2007
                Date accepted : 17 July 2007
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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