Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke

Abstract

Objective: The central-to-peripheral voluntary motor effort (VME) in physical practice of the paretic limb is a dominant force for driving functional neuroplasticity on motor restoration post-stroke. However, current rehabilitation robots isolated the central and peripheral involvements in the control design, resulting in limited rehabilitation effectiveness. The purpose of this study was to design a corticomuscular coherence (CMC) and electromyography (EMG)-driven (CMC-EMG-driven) system with central-and-peripheral integrated representation of VME for wrist-hand rehabilitation after stroke. Approach: The CMC-EMG-driven control was developed in a neuromuscular electrical stimulation (NMES)-robot system, i.e., CMC-EMG-driven NMES-robot system, to instruct and assist the wrist-hand extension and flexion in persons after stroke. A pilot single-group trial of 20 training sessions was conducted with the developed system to assess the feasibility for wrist-hand practice on the chronic strokes (n=16). The rehabilitation effectiveness was evaluated through clinical assessments, CMC, and EMG activation levels. Main results: The trigger success rate and laterality index (LI) of CMC were significantly increased in wrist-hand extension across training sessions (p<0.05). After the training, significant improvements in the target wrist-hand joints and suppressed compensation from the proximal shoulder-elbow joints were observed through the clinical scores and EMG activation levels (p<0.05). The central-to-peripheral VME distribution across upper extremity (UE) muscles was also significantly improved, as revealed by the CMC values (p<0.05). Significance: Precise wrist-hand rehabilitation was achieved by the developed system, presenting suppressed cortical and muscular compensation from the contralesional hemisphere and the proximal UE, and improved distribution of the central-and-peripheral VME on UE muscles.

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients

“…Meanwhile almost no false negative detections were obtained. The classification accuracy achieved in the present work is higher than that reported in the two available studies using CMC for online control of robotic orthosis in a rehabilitation context ( Chowdhury et al, 2019 ; Guo et al, 2022 ). Comparable performances were obtained in Chowdhury et al (2019) when an approach based on statistical correlation is used instead of the classical CMC algorithm.…”

“…Regarding the timing achieved in the classification decision with the parameters selected by this analysis, CMC features were able to provide a fast classification in stroke patients which ensures not only to exploit and train central-to-peripheral communication ( Guo et al, 2022 ), but also to send ecological feedback to the patient right after the onset of the movement attempt (a feedback that is congruent in timing and content with the exercise setting), favoring an effective motor re-learning. Comparing classification speed with previous works on rehabilitative BCIs during movement attempts, we obtained comparable ( Mrachacz-Kersting and Aliakbaryhosseinabadi, 2018 ) or better ( Biasiucci et al, 2018 , 3.5–5 s to deliver feedback) results with respect to EEG-based approaches.…”

“…Such devices usually combine the EEG and EMG feature in the classification stage, meaning that each feature (brain and muscular) is calculated separately and combined sequentially or simultaneously using a balanced weight or Bayesian fusion approach to better control the assistive device ( Leeb et al, 2011 ; Lalitharatne et al, 2013 ). Only recently, h-BCIs (EEG-EMG) have been studied specifically for post-stroke upper limb rehabilitation ( Chowdhury et al, 2019 ; Guo et al, 2022 ).…”

Enhancement of EEG–EMG coupling detection using corticomuscular coherence with spatial–temporal optimization

“…With the understanding of basic mechanisms related to stroke recovery, it has been a trend to combine different types of neurorehabilitation techniques. Based on the differences in strategies of robotic control, it has been divided into the central-intention-driven and peripheral-effort-driven paradigms [4][5][6].…”

Vibrotactile enhancement in hand rehabilitation has a reinforcing effect on sensorimotor brain activities

“…They found that bilateral cortical activation was prevalent in both motor tasks, with higher activation in the contralateral compared to the ipsilateral primary motor cortex (Yang et al, 2020). Laterality index (LI) was often used to quantify the phenomenon of hemispheric asymmetry and for understanding 10.3389/fnins.2022.935827 interhemispheric patterns of action and functional changes in the brain (Belfatto et al, 2018;Lemée et al, 2020;Guo et al, 2022). Delorme used the LI to the assess changes in the hemispheric balance between cortical sensorimotor areas in stroke patients (Delorme et al, 2019).…”