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  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., & Grosse-Wentrup, M. (2011). Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. Journal of Neural Engineering, 8(3): 036005, pp. 1-12. doi:10.1088/1741-2560/8/3/036005.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB52-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B580-1
Genre: Journal Article

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 Creators:
Gomez Rodriguez, M1, 2, Author              
Peters, J1, 2, Author              
Hill, J1, 2, Author              
Schölkopf, B1, 2, Author              
Gharabaghi, A, Author
Grosse-Wentrup, M1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The combination of brain–computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.

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 Dates: 2011-06
 Publication Status: Published in print
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 Identifiers: DOI: 10.1088/1741-2560/8/3/036005
BibTex Citekey: GomezRodriguezPHSGG2011_2
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Title: Journal of Neural Engineering
Source Genre: Journal
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Publ. Info: Bristol : Institute of Physics Publishing
Pages: - Volume / Issue: 8 (3) Sequence Number: 036005 Start / End Page: 1 - 12 Identifier: ISSN: 1741-2552
CoNE: https://pure.mpg.de/cone/journals/resource/17412552