Posts Tagged brain-machine interfaces

[WEB PAGE] Upper arm rehabilitation after severe stroke: where are we? – Physics World

10 Sep 2019 Andrea Rampin 
EEG cap

Stroke is the second leading cause of death worldwide and the third cause of induced disability, according to estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study. Treatments based on constraint-induced movement therapy, occupational practice, virtual reality and brain stimulation can work well for patients with mild impairment of upper limb movement, but they are not as effective for those burdened by severe disability. Therefore, novel individualized approaches are needed for this patient group.

Martina Coscia from the Wyss Center for Bio and Neuroengineering in Geneva, and colleagues from several other Swiss institutes, have published a review paper summarizing the most advanced techniques in use today for treatment of severe, chronic stroke patients. The researchers describe techniques being developed for upper limb motor rehabilitation: from robotics and muscular electrical stimulation, to brain stimulation and brain–computer/machine interfaces (Brain 10.1093/brain/awz181).

Robot-aided rehabilitation approaches include movement-assisting exoskeletons and end-effector devices, which enable upper arm movement by stimulating the peripheral nervous system. These techniques can also trigger reorganization of the impaired peripheral nervous system and encourage rehabilitation of the damaged somatosensory system. Several studies have reported the efficiency of robot-aided rehabilitation, alone or in combination with other techniques, in the treatment of upper limb motor impairment. One study that included severely impaired individuals also demonstrated encouraging results.

Muscular electrical stimulation can help improve the connection of motor neurons to the spinal cord and the motor cortex. Researchers have also demonstrated that application of electrical stimuli to the muscles provides positive effects on the neurons responsible for sensory signal transduction to the brain, thereby improving the motion control loop function. By modulating motor neurons’ sensitivity, muscular electrical stimulation inhibits the muscle spasms observed in other treatments.

More recently, therapies have moved on from the simple use of currents to harnessing coordinated stimuli to orchestrate more complex, task-related movements. Although this particular set of techniques didn’t show a particular advantage over physiotherapy in long-term studies of patients with mild upper limb impairment, it did seem to have a stronger effect for chronic severe patients.

Stimulating the brain

Brain stimulation, meanwhile, stimulates cortical neurons in order to improve their ability to form new connections within the affected neural network. Brain stimulation techniques can be divided into two branches – electrical and magnetic – both of which can activate or inhibit neural activity, depending on the polarity and intensity of the stimulus.

Transcranial magnetic stimulation

Researchers have achieved encouraging results using both techniques. In particular, magnetic field-triggered inhibition of the contralesional hemisphere (the hemisphere that was not affected by the stroke) activity yielded positive results. Magnetic, low-frequency stimulation of the contralesional hemisphere also proved encouraging – improving the reach to grasp ability of patients, although only for small objects. Excitingly, some studies suggest that coupling contralesional cortex inhibition with magnetic stimulation of the chronically affected area could achieve effective results.

Within these techniques, one promising approach is invasive brain stimulation, in which a device is surgically implanted in a superficial region of the brain. Such techniques allow for more sustained and spatially-oriented stimulation of the desired brain regions. The Everest trial used such methods and showed significant improvement for a larger percentage of patients after 24 weeks, compared with standard rehabilitation protocols.

Another promising recent development is non-invasive deep-brain stimulation, achieved by temporally interfering electric fields. The authors envision that a deeper understanding of the complex mechanisms involved in the brain’s reactions to magnetic and electrical stimulation will provide an important assistance in clinical application of these techniques.

The final category, brain–computer or brain–machine interfaces (BCIs or BMIs), exploit electroencephalogram (EEG) patterns to trigger feedback or an action output from an external device. Devices that produce feedback are used to train the patient to recruit the correct zone of the brain and help reorganize its interconnections. These techniques have only recently transitioned to the clinic; however, early results and observations are promising. For example, a BCI technique coupled with muscular electrical stimulation restored patients’ ability to extend their fingers.

In recent years, researchers have also tested combinations of the techniques described above. For example, combinations of robotics and muscular electrical stimulation have shown encouraging results, especially when more than one articulation was targeted by the treatment. Combining brain stimulation with muscular electrical stimulation and robotics has proved more effective in severe than in moderate cases. Also, coupling of muscular electrical stimulation with magnetic inhibitory brain stimulation provided better results than either individual technique. Interestingly, addition of electrical brain stimulation to a BCI system coupled with a robotic motor feedback enhanced the outcome, helping to achieve adaptive brain remodelling at the expense of inappropriate reorganization.

Coscia and co-authors highlight that all the techniques studied share a range of limitations that should be addressed, such as small sample size, limited understanding of the underlying mechanisms, lack of treatment personalization and minimal attention to the training task, which they note is often of limited importance for daily life. Addressing these limitations might be key to improving the clinical outcome for patients with severe stroke-induced upper limb paralysis treated with neurotechnology-aided interventions. Moreover, the authors plan to begin a clinical trial to test the use of a novel personalized therapy approach that will include a combination of the described techniques.


via Upper arm rehabilitation after severe stroke: where are we? – Physics World

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[WEB SITE] Research work in Neurotechnology directs efforts towards treating chronic stroke

Scientific work undertaken at Wyss Center for Bio and Neuroengineering in Geneva, Switzerland has developed a rehabilitation arm in order to improve recovery during severe chronic strokes in patients.

Stroke is regarded as one of the major health problems among people today. A common symptom observed among cases of stroke is the long-term impairment of upper arm function. This results in complications in daily life chores and hampers the quality of life.

The Neurotechnology includes a host of therapies, like robotics, brain stimulation, brain-machine interfaces, etc. According to experts, these will in return be fruitful in treating patients, centering on their individual needs. Moreover, the new study also sheds light on longitudinal clinical studies in order to understand the rehabilitation benefits of individual therapies. Furthermore, the study also focuses on various combinations of complementary therapies used over a period of time.

“Our findings show that neurotechnology-aided upper limb rehabilitation is promising for severe chronic stroke patients. However, we also found that the ‘one size fits all’ approach doesn’t lead to the best outcome. We suggest a move towards a personalized combination of neurotechnology-based stroke rehabilitation therapies, ideally in a home-based environment where prolonged therapy is more feasible than in a clinic. We believe that by sequentially introducing stroke therapies according to individual progress, we could allow patients to continue their recovery beyond what is possible today,” says Dr. Martina Coscia, lead author and Staff Engineer at Wyss Center.

As per experts, rehabilitation therapies show the best results within the first three months after the incidence of stroke. After the first three months, the scope of natural recovery is limited and patients are considered chronic, commonly observed scenario, especially among patients who are severely affected.

For the study, authors reportedly compared data from 64 cases of clinical studies based on upper limb neurotechnology treatments among stroke patients. The findings mainly centered on brain stimulation, electrical stimulation of muscles, and brain-computer interfaces, in addition to a combination of these.

Further reports suggest the team is directing efforts towards undertaking clinical traits in order to test the results. For the trial, experimental design such as robotics, functional electrical stimulation, brain-computer interfaces is used to monitor the after-effects of treatment in individual patients. Scientists believe to use a combination of neurotechnological and new personalized therapies in order to improve recovery among patients. The study published in the journal Brain alleges that the trial will begin in Switzerland in summer 2019.

via Research work in Neurotechnology directs efforts towards treating chronic stroke – Xaralite

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[BOOK] Emerging Therapies in Neurorehabilitation II – Βιβλία Google

José L. PonsRafael RayaJosé González
Springer30 Οκτ 2015 – 318 σελίδες

This book reports on the latest technological and clinical advances in the field of neurorehabilitation. It is, however, much more than a conventional survey of the state-of-the-art in neurorehabilitation technologies and therapies. It was written on the basis of a week of lively discussions between PhD students and leading research experts during the Summer School on Neurorehabilitation (SSNR2014), held September 15-19 in Baiona, Spain. Its unconventional format makes it a perfect guide for all PhD students, researchers and professionals interested in gaining a multidisciplinary perspective on current and future neurorehabilitation scenarios. The book addresses various aspects of neurorehabilitation research and practice, including a selection of common impairments affecting CNS function, such as stroke and spinal cord injury, as well as cutting-edge rehabilitation and diagnostics technologies, including robotics, neuroprosthetics, brain-machine interfaces and neuromodulation.

via Emerging Therapies in Neurorehabilitation II – Βιβλία Google

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[REVIEW] Robotic Devices and Brain Machine Interfaces for Hand Rehabilitation Post-stroke: Current State and Future Potentials – Full Text PDF


This paper reviews the current state of the art in robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain machine interfaces (BMI). The main focus is on the technical specifications required for these devices to achieve their goals. From the literature reviewed, it is clear that these rehabilitation devices can increase the functionality of the human hand post-stroke. However, there are still several challenges to be overcome before they can be fully deployed. Further clinical trials are needed to ensure that substantial improvement can be made in limb functionality for stroke survivors, particularly as part of a programme of frequent at-home high-intensity training over an extended period.

This review serves the purpose of providing valuable insights into robotics rehabilitation techniques in particular for those that could explore the synergy between BMI and the novel area of soft robotics.


Strokes are a global issue affecting people of all ethnicities, genders and ages [1]; approximately 20 million people per year worldwide suffer a stroke [2, 3]. Five million of those patients remain severely handicapped and dependent on assistance in daily life [4]. Once a stroke has occurred the patient may be left with mild to severe disabilities, depending on the type and severity of the stroke. This paper will focus on the primary issues experienced which are the clawing of the hand and stiffening of the wrist. In recent years, several new forms of rehabilitation have been proposed using robot-aided therapy. This work reviews the current state-ofthe-art robotic devices and brain-machine interfaces (BMI) for post-stroke hand rehabilitation, analysing current challenges, highlighting the future potential and addressing any inherent ethical issues.[…]

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[ARTICLE] Brain-machine Interface in Robot-assisted Neurorehabilitation for Patients with Stroke and Upper Extremity Weakness – the Therapeutic Turning Point – Full Text HTML


Activity and participation after stroke can be increased by neurorehabilitation of upper extremity. As the technology advances, a robot-assisted restorative therapy with/without a brain-machine interface (BMI) is suggested as a promising therapeutic option. Understanding the therapeutic point of view about robots and BMIs can be linked to the patient-oriented usability of the devices. The therapeutic turning point concept of robot-assisted rehabilitation with BMIs, basics of robotics for stroke and upper extremity weakness and consequent neuroplasticity/motor recovery are reviewed.


  • Robot with BMI therapy for arm after stroke has closed feedback and more chance of neural plasticity.
  • Understanding of the new rehabilitation technologies such as robot with BMI therapy for arm after stroke shall give the therapeutic turning point.


Stroke is a sudden neurologic deficit caused by disturbance of vascular supply to the brain by ending up ischemic/hemorrhagic lesions on it. A large proportion of disease burden of stroke can be explained by the loss of motor function causing decreased activity of daily living and participation restriction for the patient. Affected brain regions in stroke, especially in sensorimotor areas, could show various kinds of motor deficits such as weakness, incoordination and changes of muscle tone. For the execution of activity of daily living, those motor deficits need to be properly intervened, which would be the reason why we claim an intensive neurorehabilitation for the recovery of functions during long survival period after stroke [1, 2, 3].

In Merriam-Webster (; accessed on 26 August 2016), one simple definition of robot is a machine that do the work of a person and that works automatically or is controlled by a computer. Some kinds of a robot can move human body parts, and the purpose of the robot can be the neurorehabilitation and the improvement of the function of that body parts.

Robot-assisted arm rehabilitation can give the patient repetitive, controlled motion of upper extremity without exhaustion of therapist. Level of difficulty for the training task can be adjusted according to the status of the patient [4]. Through robot-assisted upper extremity training of movement, neural plasticity and motor recovery can be facilitated [5].

The motivation for the use of devices and the study of psychological stability would be important in terms of efficacy in all kinds of therapies, including robot therapy. Closed feedback during the robot therapy can elevate the patient’s emersion to the task and increase the motivation. Among many methods of closed feedback, brain-machine interface (BMI) system can give the direct and immediate feedback to the patient [5]. The BMI is a system that picks up the brain signal, by extracting a useful characteristic, and develop some logics to control other devices using that characteristic, which ideally congruent with the patient’s intention [6]. Many logics of current BMI are used for controlling robots. Through such robot-assisted rehabilitation with BMI system, closed feedback from patient’s immediate, not preprogrammed, intention can be completed. Even though robotic devices or BMIs do not give any haptic sensory feedback, visual or proprioceptive observation of the robotic arm to perform the intended movement will give the BMI-controlling patients more appropriate feedback. In this review, non-invasive electroencephalogram based BMI combined with robot-assisted rehabilitation technique is considered.

Continue —> KoreaMed Synapse

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[Article] Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions – Full Text PDF/HTML/ePUB


Background: As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.

Methods: In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.

Results: Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively).

Conclusions: The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.

Full Text HTML —>  JNER | Full text | Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions

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[ARTICLE] Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation – Full Text HTML


Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients.

Recent engineering and technological advances such as brain–machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neurorehabilitation, to accelerate functional recovery and improve QOL.

This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation.

Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation.

Full Text HTML–> Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation – Springer.

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[ARTICLE] Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates

Abstract (provisional)


Brain-machine interfaces (BMI) have recently been integrated within motor rehabilitation therapies by actively involving the central nervous system (CNS) within the exercises. For instance, the online decoding of intention of motion of a limb from pre-movement EEG correlates is being used to convert passive rehabilitation strategies into active ones mediated by robotics. As early stages of upper limb motor rehabilitation usually focus on analytic single-joint mobilizations, this paper investigates the feasibility of building BMI decoders for these specific types of movements.


Two different experiments were performed within this study. For the first one, six healthy subjects performed seven self-initiated upper-limb analytic movements, involving from proximal to distal articulations. For the second experiment, three spinal cord injury patients performed two of the previously studied movements with their healthy elbow and paralyzed wrist. In both cases EEG neural correlates such as the event-related desynchronization (ERD) and movement related cortical potentials (MRCP) were analyzed, as well as the accuracies of continuous decoders built using the pre-movement features of these correlates (i.e., the intention of motion was decoded before movement onset).


The studied movements could be decoded in both healthy subjects and patients. For healthy subjects there were significant differences in the EEG correlates and decoding accuracies, dependent on the moving joint. Percentages of correctly anticipated trials ranged from 75% to 40% (with chance level being around 20%), with better performances for proximal than for distal movements. For the movements studied for the SCI patients the accuracies were similar to the ones of the healthy subjects.


This paper shows how it is possible to build continuous decoders to detect movement intention from EEG correlates for seven different upper-limb analytic movements. Furthermore we report differences in accuracies among movements, which might have an impact on the design of the rehabilitation technologies that will integrate this new type of information. The applicability of the decoders was shown in a clinical population, with similar performances between healthy subjects and patients.

The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.

via JNER | Abstract | Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates.

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[ARTICLE] Abstract – Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke

Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions and parameters for these novel therapeutic systems are still unknown. Here, we present preliminary findings demonstrating successful movement intent detection from scalp electroencephalography (EEG) during robotic rehabilitation using the MAHI Exo-II in an individual with hemiparesis following stroke. These findings have strong clinical implications for the development of closed-loop brain-machine interfaces to robotic rehabilitation systems.

via IEEE Xplore Abstract – Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for str….

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