Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation.

Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants' movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p < 0.05) and marginally correlated with FMA-UE (ρ = 0.63, p = 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.

Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial.

This paper presents the preliminary findings of a multi-year clinical study evaluating the effectiveness of adding a brain-machine interface (BMI) to the MAHI-Exo II, a robotic upper limb exoskeleton, for elbow flexion/extension rehabilitation in chronic stroke survivors. The BMI was used to trigger robot motion when movement intention was detected from subjects' neural signals, thus requiring that subjects be mentally engaged during robotic therapy. The first six subjects to complete the program have shown improvements in both Fugl-Meyer Upper-Extremity scores as well as in kinematic movement quality measures that relate to movement planning, coordination, and control. These results are encouraging and suggest that increasing subject engagement during therapy through the addition of an intent-detecting BMI enhances the effectiveness of standard robotic rehabilitation.

Quantifying learned non-use after stroke using unilateral and bilateral steering tasks.

Learned non-use (LNU) is common after stroke and manifests when persons with stroke spontaneously use their stronger less-impaired arm despite residual functional abilities in the impaired arm. This tendency of under utilizing the impaired arm slows down the re-acquisition of bilateral coordination on activities of daily living. We wanted to examine whether this behavior could be studied and quantified using the TheraDrive system, a low-cost, mechatronic/robotic stroke rehabilitation system which uses a commercial force-feedback steering wheel along with custom games and unilateral and bilateral steering tasks for therapy and assessment. We attempt to quantify the role of the impaired arm in bilateral tracking with one and two-wheeled modes of the TheraDrive. Our results indicate that impaired arm use, arm bias and learned non-use behaviors may best be detected in decoupled bilateral tracking tasks.

Feasibility study of TheraDrive: a low-cost game-based environment for the delivery of upper arm stroke therapy.

Rising healthcare costs combined with an increase in the number of people living with disabilities due to stroke have created a need for affordable stroke therapy that can be administered in both home and clinical environments. Studies show that robot and computer-assisted devices are promising tools for rehabilitating persons with impairment and disabilities due to stroke. Studies also have shown that highly motivating therapy produces neuromotor relearning that aids the rehabilitative process. Combining these concepts, this paper discusses TheraDrive, a simple, but novel robotic system for more motivating stroke therapy. We conducted two feasibility studies. The paper discusses these studies. Findings demonstrate the ability of the system to grade therapy and the sensitivity of its metrics to the level of motor function in the impaired arm. In addition, findings confirm the ability of the system to administer fun therapy leading to improved motor performance on steering tasks. However, further work is needed to improve the system's ability to increase motor function in the impaired arm.