Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke

Abstract

The objective of this study was to investigate the efficacy of an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA) score 10–50), recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 min per session. The BCI-HK group received 1 h of BCI coupled with HK intervention, and the HK group received 1 h of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 min of therapist-assisted arm mobilization. The SAT group received 1.5 h of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12, and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.

Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

“…The mean age of the participants ranged from 49.3 ± 12.5 to 67.1 ± 5.51 years. Six studies targeted chronic patients,39, 41, 59, 60, 61, 62 whereas the remaining three studies targeted,42, 63, 64 patients in the subacute phase, with a mean time from stroke approximately ranging from 2 to 4.5 months. In eight out of the nine studies, the BCI relied on the detection of ERD of SMR related to motor imagery.…”

“…The nature of the control group differed across studies: sham‐feedback triggered orthosis movement at random instances in four studies,39, 59, 60, 63 one study used conventional therapy,62 one study used robot‐assisted training,41 one study used NMES,64 and one study used motor imagery 42. Ang et al61 reported results of two different control groups: robot only and conventional therapy only. Whenever available, we used the results of control groups undergoing conventional therapy.…”

Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface

“…The hypothesis is that the afferent signal generated artificially induces central nervous system plasticity because it is in causal association with the cortical activity associated with the intention to move. Several research groups have recently provided evidence that this type of BCI leads to functional improvements in upper limb or hand function (Ang et al 2010;Broetz et al 2010;Cincotti et al 2012;Daly et al 2009;Kasashima-Shindo et al 2015;Li et al 2014;Mukaino et al 2014;Pichiorri et al 2015;Ramos-Murguialday et al 2013;, although others have found no changes (Ang et al 2014;Buch et al 2008) and data are lacking for the use of such a BCI for altering lower limb function (Teo and Chew 2014). These studies report evidence for neuroplasticity, typically inferred from an improved performance of the BCI (Buch et al 2008;Li et al 2014), from alterations in the amplitude (Broetz et al 2010;Cincotti et al 2012;Li et al 2014;Pichiorri et al 2015) or latency ) of the extracted electroencephalographic (EEG) signal, or by functional magnetic resonance imaging (fMRI) .…”

Interaction of BCI with the underlying neurological conditions in patients: pros and cons

“…The issue presents 4 reviews Pineda et al, 2014;Priftis, 2014;Rupp, 2014) and 8 experimental studies (Ang et al, 2014;Daly et al, 2014;Ono et al, 2014;Song et al, 2014;Xu et al, 2014;Young et al, 2014a,b,c). It covers studies on five different patient groups: stroke (Ang et al, 2014;Ono et al, 2014;Song et al, 2014;Young et al, 2014a,b,c), spinal cord injury (SCI) (Rupp, 2014;Xu et al, 2014), autism Pineda et al, 2014), cerebral palsy (CP) (Daly et al, 2014) and amyotrophic lateral sclerosis (ALS) (Priftis, 2014). Three different types of BCI are presented: motor imagery, P300 and neurofeedback (operant conditioning).…”

“…Several experimental studies in this special issue present BCI applications to improve and restore CNP functions (Ang et al, 2014;Ono et al, 2014;Young et al, 2014a,b) while some present basic research papers looking into the effect of BCI training on the cortical activity (Song et al, 2014;Young et al, 2014b,c) or exploring EEG signature characteristic for a certain patient group, such as SCI or CP (Daly et al, 2014;Xu et al, 2014).…”