recoveriX: A new BCI-based technology for persons with stroke

Abstract

Brain-computer interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help monitor compliance and provide closed-loop feedback during therapy. We describe recoveriX and present initial results from one patient.

Behavioral Outcomes Following Brain–Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial

“…However, innovative therapeutic techniques show some efficacy generating functional motor recovery beyond the traditional rehabilitation windows ( Cramer and Nudo, 2010 ; Ang et al, 2015 ; Irimia et al, 2016 ). Brain–computer interfaces (BCIs), a novel rehabilitation tool, have shown proof of concept for rehabilitating volitional movements in stroke survivors ( Muralidharan et al, 2011 ; Song et al, 2014 , 2015 ; Young et al, 2014a , b , c , d , 2015 ; Irimia et al, 2016 ). In this growing area of research, developing technologies demonstrate promising potential for treating hemiparesis in a clinically viable and efficient manner and they may offer an avenue to increased autonomy for patients reducing their cost and burden of care.…”

“…There is currently considerable variability in design and efficacy of BCI therapies as well as little consensus with respect to proper arrangement, administration, and dosing ( Muralidharan et al, 2011 ; Ang and Guan, 2013 ; Young et al, 2014a ; Ang et al, 2015 ; Irimia et al, 2016 ; Remsik et al, 2016 ; Bundy et al, 2017 ; Dodd et al, 2017 ). Although acute stroke care has improved morbidity outcomes significantly, current treatments for persistent UE motor impairment resulting from stroke offer only limited restoration of UE motor function the further from stroke a survivor progresses ( Wolf et al, 2006 , 2010 ; Dromerick et al, 2009 ; Benjamin et al, 2017 ; Stinear et al, 2017 ).…”

“…Although acute stroke care has improved morbidity outcomes significantly, current treatments for persistent UE motor impairment resulting from stroke offer only limited restoration of UE motor function the further from stroke a survivor progresses ( Wolf et al, 2006 , 2010 ; Dromerick et al, 2009 ; Benjamin et al, 2017 ; Stinear et al, 2017 ). Evidence suggests both acute and chronic stroke patients respond to various neuro-rehabilitative BCI therapy strategies and can achieve clinically significant changes in measures of UE impairment ( Young et al, 2014c ; Irimia et al, 2016 ; Remsik et al, 2016 ). Furthermore, recent research also suggests that BCI therapy targeted at motor recovery may provide benefits in other brain regions outside of only the motor network ( Mohanty et al, 2018 ).…”

Evaluation of Changes in the Motor Network Following BCI Therapy Based on Graph Theory Analysis

“…In the absence of effective traditional rehabilitation therapy for chronic stroke survivors, novel therapeutic techniques show success in generating some functional motor recovery beyond traditional rehabilitation window (Cramer and Nudo, 2010 ; Ang et al, 2015 ; Irimia et al, 2016 ).…”

“…Brain-computer interface (BCI) therapy is being used in non-traditional therapies for stroke rehabilitation. An increasing number of studies indicate that with different neuro-rehabilitative BCI therapy strategies, both acute and chronic stroke patients can achieve significant changes in behavioral measures [such as the Action Research Arm Test (ARAT), and the Nine-Hole Peg Test (9-HPT)] of persistent upper extremity (UE) impairment (Young et al, 2014a , b ; Irimia et al, 2016 ; Remsik et al, 2016 ). One such strategies that was applied in the ongoing clinical trial [(NCT02098265) interventional, non-invasive closed-loop electroencephalography (EEG) based BCI therapy for the restoration of distal UE motor function in stroke survivors Song et al, 2014a , 2015 ; Young et al, 2014a , b , c , d , 2015 ; Remsik et al, 2016 ] is to use electroencephalography (EEG) to detect neural activity.…”