PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Closing the sensorimotor loop: haptic feedback helps decoding of motor imagery
Manuel Gomez-Rodriguez, Jan Peters, Jeremy Hill, Bernhard Schölkopf, Alireza Gharabaghi and Moritz Grosse-Wentrup
Journal of Neural Engineering 2010.

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 article, 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 on-line 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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:7831
Deposited By:Moritz Grosse-Wentrup
Deposited On:17 March 2011