scholarly journals A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3754 ◽  
Author(s):  
Octavio Marin-Pardo ◽  
Christopher M. Laine ◽  
Miranda Rennie ◽  
Kaori L. Ito ◽  
James Finley ◽  
...  

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain–computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain–computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12–30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle–computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.

Author(s):  
Daniel Perez-Marcos ◽  
Odile Chevalley ◽  
Thomas Schmidlin ◽  
Gangadhar Garipelli ◽  
Andrea Serino ◽  
...  

2018 ◽  
Vol 9 ◽  
Author(s):  
Ana L. Faria ◽  
Mónica S. Cameirão ◽  
Joana F. Couras ◽  
Joana R. O. Aguiar ◽  
Gabriel M. Costa ◽  
...  

e-Neuroforum ◽  
2015 ◽  
Vol 21 (4) ◽  
Author(s):  
Niels Birbaumer ◽  
Ujwal Chaudhary

AbstractBrain-computer interfaces (BCI) use neuroelectric and metabolic brain activity to activate peripheral devices and computers without mediation of the motor system. In order to activate the BCI patients have to learn a certain amount of brain control. Self-regulation of brain activity was found to follow the principles of skill learning and instrumental conditioning. This review focuses on the clinical application of brain-computer interfaces in paralyzed patients with locked-in syndrome and completely locked-in syndrome (CLIS). It was shown that electroencephalogram (EEG)-based brain-computer interfaces allow selection of letters and words in a computer menu with different types of EEG signals. However, in patients with CLIS without any muscular control, particularly of eye movements, classical EEG-based brain-computer interfaces were not successful. Even after implantation of electrodes in the human brain, CLIS patients were unable to communicate. We developed a theoretical model explaining this fundamental deficit in instrumental learning of brain control and voluntary communication: patients in complete paralysis extinguish goal-directed responseoriented thinking and intentions. Therefore, a reflexive classical conditioning procedure was developed and metabolic brain signals measured with near infrared spectroscopy were used in CLIS patients to answer simple questions with a “yes” or “no”-brain response. The data collected so far are promising and show that for the first time CLIS patients communicate with such a BCI system using metabolic brain signals and simple reflexive learning tasks. Finally, brain machine interfaces and rehabilitation in chronic stroke are described demonstrating in chronic stroke patients without any residual upper limb movement a surprising recovery of motor function on the motor level as well as on the brain level. After extensive combined BCI training with behaviorally oriented physiotherapy, significant improvement in motor function was shown in this previously intractable paralysis. In conclusion, clinical application of brain machine interfaces in well-defined and circumscribed neurological disorders have demonstrated surprisingly positive effects. The application of BCIs to psychiatric and clinical-psychological problems, however, at present did not result in substantial improvement of complex behavioral disorders.


2016 ◽  
Vol 39 (2) ◽  
pp. 261-272 ◽  
Author(s):  
Malene Pedersen ◽  
Tina H. Bundgaard ◽  
Peter Zeeman ◽  
Jørgen R. Jørgensen ◽  
Peter M.B. Sørensen ◽  
...  

2020 ◽  
Vol 14 ◽  
Author(s):  
Felix Putze ◽  
Athanasios Vourvopoulos ◽  
Anatole Lécuyer ◽  
Dean Krusienski ◽  
Sergi Bermúdez i Badia ◽  
...  

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