Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke.

Autor: Bhagat NA; Dept. of Electrical & Computer Engineering, University of Houston, Houston, TX 77004 USA. (; fax: 713-743-4444; nabhagat@uh.edu., French J; Dept. of Mechanical Engineering, Rice University, Houston, TX 77005 USA. jaf12@rice.edu., Venkatakrishnan A; Dept. of Electrical & Computer Engineering, University of Houston, Houston, TX 77004 USA. (; fax: 713-743-4444; avenkatakrishnan@uh.edu., Yozbatiran N; Institute for Rehabilitation Research (TIRR) and University of Texas Health Sciences Center, Houston, TX USA, nuray.yozbatiran@uth.tmc.edu., Francisco GE; Institute for Rehabilitation Research (TIRR) and University of Texas Health Sciences Center, Houston, TX USA gerard.e.francisco@uth.tmc.edu., O'Malley MK; Dept. of Mechanical Engineering, Rice University, Houston, TX 77005 USA. omalleym@rice.edu., Contreras-Vidal JL; Dept. of Electrical & Computer Engineering, University of Houston, Houston, TX 77004 USA. (; fax: 713-743-4444; jlcontreras-vidal@uh.edu.
Jazyk: angličtina
Zdroj: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2014; Vol. 2014, pp. 4127-4130.
DOI: 10.1109/EMBC.2014.6944532
Abstrakt: Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions and parameters for these novel therapeutic systems are still unknown. Here, we present preliminary findings demonstrating successful movement intent detection from scalp electroencephalography (EEG) during robotic rehabilitation using the MAHI Exo-II in an individual with hemiparesis following stroke. These findings have strong clinical implications for the development of closed-loop brain-machine interfaces to robotic rehabilitation systems.
Databáze: MEDLINE
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