Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial

Abstract

Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p < 0.01) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p < 0.01). Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.

Brain‐machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review

“…No new articles were retrieved by using reference and author tracking. After quality assessment, nine studies were included because they had a score of 6 or more points and therefore considered to have sufficient methodological quality (Ang et al, ; Ang, Chua, et al, ; Ang, Guan, et al,, ; Frolov et al, ; Jang, Kim, & Lee, ; Kim, Kim, & Lee, ; Li et al, ; Mihara et al, ; Ramos‐Murguialday et al, ; Table ). For a summary of all study characteristics, see Table .…”

“…In total, 233 participants were included in this systematic review, the majority Asian, with an average age ranging from 49.3 ± 12.5 to 67.1 ± 5.95 years, with time poststroke ranging from 1.7 to 71 months. Five studies involved patients moderately impaired (scoring between 26 and 50 out of 66 in FMA) to severely impaired (scoring below 25 in FMA; Luft, McCombe‐Waller, et al, ), whereas four other studies referred to severely impaired patients (Frolov et al, ; Jang et al, ; Li et al, ; Ramos‐Murguialday et al, ). Overall, men‐to‐women ratio of 1.99:1, left‐to‐right side ratio of 1:1, and only four patients with bilateral lesions were included.…”

“…The number of subjects randomized to the BCI training ranged from 10 to 74. Dropout rates varied from 3.8% to 17.4% not only in BCI but also in control groups, and the main reasons were due to pain (Ang, Chua, et al, ), seizures (Ang et al, ), allergy to electrode pads (Li et al, ), and discharge (Frolov et al, ; Jang et al, ; Kim et al, ).…”

“…One study combined BCI with robotic feedback neurorehabilitation compared with standard robotic rehabilitation (Ang, Chua, et al, ), whereas another included BCI Haptic Knob robotic feedback compared with Haptic Knob robot or standard arm rehabilitation (Ang et al, ). Moreover, three used sham intervention, two studies compared real neurofeedback with sham‐EEG‐based MI‐BCI orthosis (Frolov et al, ; Ramos‐Murguialday et al, ) whereas another performed the intervention combined with previous transcranial direct current stimulation (tDCS) in the experimental group or combined with sham tDCS intervention in the control group (Ang, Guan, et al, ). The number of trials was the same between the experimental and control groups in three studies (Ang et al, ; Ang, Guan, et al, ; Frolov et al, ); however, the fourth did not match (Ang, Chua, et al, ).…”

“…Moreover, three used sham intervention, two studies compared real neurofeedback with sham‐EEG‐based MI‐BCI orthosis (Frolov et al, ; Ramos‐Murguialday et al, ) whereas another performed the intervention combined with previous transcranial direct current stimulation (tDCS) in the experimental group or combined with sham tDCS intervention in the control group (Ang, Guan, et al, ). The number of trials was the same between the experimental and control groups in three studies (Ang et al, ; Ang, Guan, et al, ; Frolov et al, ); however, the fourth did not match (Ang, Chua, et al, ). Finally, three other studies used EEG‐based MI‐BCI combined with functional electrical stimulation (FES) and occupational therapy (Jang et al, ; Kim et al, ) or physical therapy, exercise therapy, and acupuncture therapy (Li et al, ) comparatively with the respective therapies alone (see Table ).…”