TY - GEN
T1 - Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke
AU - Bhagat, Nikunj A.
AU - French, James
AU - Venkatakrishnan, Anusha
AU - Yozbatiran, Nuray
AU - Francisco, Gerard E.
AU - O'Malley, Marcia K.
AU - Contreras-Vidal, Jose L.
N1 - Publisher Copyright:
© 2014 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2014/11/2
Y1 - 2014/11/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84929494213&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929494213&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2014.6944532
DO - 10.1109/EMBC.2014.6944532
M3 - Conference contribution
C2 - 25570900
AN - SCOPUS:84929494213
VL - 2014
T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
SP - 4127
EP - 4130
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Y2 - 26 August 2014 through 30 August 2014
ER -