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What’s Your next move? Detecting movement intention for stroke rehabilitation


Zimmermann, Raphael; Marchal-Crespo, L; Lambercy, O; Fluet, M C; Metzger, J C; Edelmann, J; Brand, J; Eng, K; Riener, R; Wolf, M; Gassert, R (2013). What’s Your next move? Detecting movement intention for stroke rehabilitation. In: Guger, C; Allsion, B Z; Edlinger, G. Brain-Computer Interface Research: A State-of-the-Art Summary. Berlin: Springer, 23-37.

Abstract

BCIs have recently been identified as a method to promote restorative neuroplastic changes in patients with severe motor impairment, such as after a stroke. In this chapter, we describe a novel therapeutic strategy for hand rehabilitation making use of this method. The approach consists of recording brain activity in cortical motor areas by means of near-infrared spectroscopy, and complementing the cortical signals with physiological data acquired simultaneously. By combining these signals, we aim at detecting the intention to move using a multi-modal classification algorithm. The classifier output then triggers assistance from a robotic device, in order to execute the movement and provide sensory stimulation at the
level of the hand as response to the detected motor intention. Furthermore, the cortical data can be used to control audiovisual feedback, which provides a context and a motivating training environment. It is expected that closing the sensorimotor loop with such a brain-body-robot interface will promote europlasticity in sensorimotor networks and support the recovery process.

Abstract

BCIs have recently been identified as a method to promote restorative neuroplastic changes in patients with severe motor impairment, such as after a stroke. In this chapter, we describe a novel therapeutic strategy for hand rehabilitation making use of this method. The approach consists of recording brain activity in cortical motor areas by means of near-infrared spectroscopy, and complementing the cortical signals with physiological data acquired simultaneously. By combining these signals, we aim at detecting the intention to move using a multi-modal classification algorithm. The classifier output then triggers assistance from a robotic device, in order to execute the movement and provide sensory stimulation at the
level of the hand as response to the detected motor intention. Furthermore, the cortical data can be used to control audiovisual feedback, which provides a context and a motivating training environment. It is expected that closing the sensorimotor loop with such a brain-body-robot interface will promote europlasticity in sensorimotor networks and support the recovery process.

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Additional indexing

Item Type:Book Section, not refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Neonatology
04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2013
Deposited On:23 Jan 2014 12:20
Last Modified:05 Apr 2016 17:28
Publisher:Springer
Series Name:SpringerBriefs in Electrical and Computer Engineering
ISBN:978-3-642-36083-1
Publisher DOI:https://doi.org/10.1007/978-3-642-36083-1_4

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