Quantitative EEG for Predicting Upper Limb Motor Recovery in Chronic Stroke Robot-Assisted Rehabilitation

2017 ◽  
Vol 25 (7) ◽  
pp. 1058-1067 ◽  
Author(s):  
Paula Trujillo ◽  
Alfonso Mastropietro ◽  
Alessandro Scano ◽  
Andrea Chiavenna ◽  
Simona Mrakic-Sposta ◽  
...  
Keyword(s):  
Upper Limb ◽  
Quantitative Eeg ◽  
Chronic Stroke ◽  
Motor Recovery ◽  
Robot Assisted

Upper limb robot-assisted therapy in subacute and chronic stroke patients using an innovative end-effector haptic device: A pilot study

2018 ◽  
Vol 42 (1) ◽  
pp. 43-52 ◽  
Author(s):  
S. Mazzoleni ◽  
E. Battini ◽  
R. Crecchi ◽  
P. Dario ◽  
F. Posteraro
Keyword(s):  
Pilot Study ◽  
Upper Limb ◽  
Chronic Stroke ◽  
Haptic Device ◽  
Stroke Patients ◽  
Robot Assisted ◽  
End Effector

Effects of proximal and distal robot-assisted upper limb rehabilitation on chronic stroke recovery

2013 ◽  
Vol 33 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Stefano Mazzoleni ◽  
Patrizio Sale ◽  
Marco Franceschini ◽  
Samuele Bigazzi ◽  
Maria Chiara Carrozza ◽  
...  
Keyword(s):  
Upper Limb ◽  
Chronic Stroke ◽  
Stroke Recovery ◽  
Robot Assisted ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

scholarly journals Quantification of Upper Limb Motor Recovery and EEG Power Changes after Robot-Assisted Bilateral Arm Training in Chronic Stroke Patients: A Prospective Pilot Study

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Marialuisa Gandolfi ◽  
Emanuela Formaggio ◽  
Christian Geroin ◽  
Silvia Francesca Storti ◽  
Ilaria Boscolo Galazzo ◽  
...  
Keyword(s):  
Upper Limb ◽  
Chronic Stroke ◽  
Active Movement ◽  
Stroke Patients ◽  
Robot Assisted ◽  
Sensorimotor Network ◽  
Upper Limb Function ◽  
Eeg Power ◽  
Beta Frequency ◽  
Bilateral Arm Training

Background. Bilateral arm training (BAT) has shown promise in expediting progress toward upper limb recovery in chronic stroke patients, but its neural correlates are poorly understood.Objective. To evaluate changes in upper limb function and EEG power after a robot-assisted BAT in chronic stroke patients.Methods. In a within-subject design, seven right-handed chronic stroke patients with upper limb paresis received 21 sessions (3 days/week) of the robot-assisted BAT. The outcomes were changes in score on the upper limb section of the Fugl-Meyer assessment (FM), Motricity Index (MI), and Modified Ashworth Scale (MAS) evaluated at the baseline (T0), posttraining (T1), and 1-month follow-up (T2). Event-related desynchronization/synchronization were calculated in the upper alpha and the beta frequency ranges.Results. Significant improvement in all outcomes was measured over the course of the study. Changes in FM were significant at T2, and in MAS at T1and T2. After training, desynchronization on the ipsilesional sensorimotor areas increased during passive and active movement, as compared with T0.Conclusions. A repetitive robotic-assisted BAT program may improve upper limb motor function and reduce spasticity in the chronically impaired paretic arm. Effects on spasticity were associated with EEG changes over the ipsilesional sensorimotor network.

scholarly journals A Data-Driven Investigation on Surface Electromyography Based Clinical Assessment in Chronic Stroke

2021 ◽  
Vol 15 ◽  
Author(s):  
Fuqiang Ye ◽  
Bibo Yang ◽  
Chingyi Nam ◽  
Yunong Xie ◽  
Fei Chen ◽  
...  
Keyword(s):  
Upper Limb ◽  
Surface Electromyography ◽  
Pearson Correlation ◽  
Chronic Stroke ◽  
Data Driven ◽  
Robot Assisted ◽  
Automated Assessment ◽  
Zero Crossing ◽  
Clinical Scales ◽  
Before And After

Background: Surface electromyography (sEMG) based robot-assisted rehabilitation systems have been adopted for chronic stroke survivors to regain upper limb motor function. However, the evaluation of rehabilitation effects during robot-assisted intervention relies on traditional manual assessments. This study aimed to develop a novel sEMG data-driven model for automated assessment.Method: A data-driven model based on a three-layer backpropagation neural network (BPNN) was constructed to map sEMG data to two widely used clinical scales, i.e., the Fugl–Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS). Twenty-nine stroke participants were recruited in a 20-session sEMG-driven robot-assisted upper limb rehabilitation, which consisted of hand reaching and withdrawing tasks. The sEMG signals from four muscles in the paretic upper limbs, i.e., biceps brachii (BIC), triceps brachii (TRI), flexor digitorum (FD), and extensor digitorum (ED), were recorded before and after the intervention. Meanwhile, the corresponding clinical scales of FMA and MAS were measured manually by a blinded assessor. The sEMG features including Mean Absolute Value (MAV), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), and Wavelength (WL) were adopted as the inputs to the data-driven model. The mapped clinical scores from the data-driven model were compared with the manual scores by Pearson correlation.Results: The BPNN, with 15 nodes in the hidden layer and sEMG features, i.e., MAV, ZC, SSC, and RMS, as the inputs to the model, was established to achieve the best mapping performance with significant correlations (r > 0.9, P < 0.001), according to the FMA. Significant correlations were also obtained between the mapped and manual FMA subscores, i.e., FMA-wrist/hand and FMA-shoulder/elbow, before and after the intervention (r > 0.9, P < 0.001). Significant correlations (P < 0.001) between the mapped and manual scores of MASs were achieved, with the correlation coefficients r = 0.91 at the fingers, 0.88 at the wrist, and 0.91 at the elbow after the intervention.Conclusion: An sEMG data-driven BPNN model was successfully developed. It could evaluate upper limb motor functions in chronic stroke and have potential application in automated assessment in post-stroke rehabilitation, once validated with large sample sizes.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT02117089.

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