title: Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke.
abstract: 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.
authors.affiliation: Dept. of Electrical & Computer Engineering, University of Houston, Houston, TX 77004 USA. (; fax: 713-743-4444; nabhagat@uh.edu.
authors.forename: Nikunj A
authors.initials: NA
authors.lastname: Bhagat
authors.affiliation: Dept. of Mechanical Engineering, Rice University, Houston, TX 77005 USA. jaf12@rice.edu.
authors.forename: James
authors.initials: J
authors.lastname: French
authors.affiliation: Dept. of Electrical & Computer Engineering, University of Houston, Houston, TX 77004 USA. (; fax: 713-743-4444; avenkatakrishnan@uh.edu.
authors.forename: Anusha
authors.initials: A
authors.lastname: Venkatakrishnan
authors.affiliation: Institute for Rehabilitation Research (TIRR) and University of Texas Health Sciences Center, Houston, TX USA, nuray.yozbatiran@uth.tmc.edu.
authors.forename: Nuray
authors.initials: N
authors.lastname: Yozbatiran
authors.affiliation: Institute for Rehabilitation Research (TIRR) and University of Texas Health Sciences Center, Houston, TX USA gerard.e.francisco@uth.tmc.edu.
authors.forename: Gerard E
authors.initials: GE
authors.lastname: Francisco
authors.affiliation: Dept. of Mechanical Engineering, Rice University, Houston, TX 77005 USA. omalleym@rice.edu.
authors.forename: Marcia K
authors.initials: MK
authors.lastname: O'Malley
authors.affiliation: Dept. of Electrical & Computer Engineering, University of Houston, Houston, TX 77004 USA. (; fax: 713-743-4444; jlcontreras-vidal@uh.edu.
authors.forename: Jose L
authors.initials: JL
authors.lastname: Contreras-Vidal
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement; D010291:Paresis / Q000503:physiopathology / Q000534:rehabilitation; D012371:Robotics; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000503:physiopathology; D000071939:Stroke Rehabilitation; D060388:Support Vector Machine; D034941:Upper Extremity / Q000503:physiopathology; D055815:Young Adult