Posts Tagged brain-machine interface (BMI)

[Abstract] Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up

Background. Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients.

Objective. Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention.

Methods. A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2).

Results. The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P = .015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions.

Conclusions. BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.

via Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up – Ander Ramos-Murguialday, Marco R. Curado, Doris Broetz, Özge Yilmaz, Fabricio L. Brasil, Giulia Liberati, Eliana Garcia-Cossio, Woosang Cho, Andrea Caria, Leonardo G. Cohen, Niels Birbaumer, 2019

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[Abstract] Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up

Background. Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients.

Objective. Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention.

Methods. A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2).

Results. The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P = .015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions.

Conclusions. BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.

 

via Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up – Ander Ramos-Murguialday, Marco R. Curado, Doris Broetz, Özge Yilmaz, Fabricio L. Brasil, Giulia Liberati, Eliana Garcia-Cossio, Woosang Cho, Andrea Caria, Leonardo G. Cohen, Niels Birbaumer, 2019

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