Posts Tagged Brain–machine interface

[WEB SITE] FDA Approves MindMotion GO, Mobile Neurorehabilitation Product

The US Food and Drug Administration (FDA) has granted clearance to MindMotion GO, a portable neurorehabilitation product, for launch in the United States.

MindMotion GO utilizes technology that is designed to be used by patients with mild to lightly severe neurological impairments, as well as in the recovery phase of rehabilitation. Produced by the Swiss neurogaming company MindMaze, the mobile rehabilitation product is an outpatient addition to its MindMotion PRO, which received FDA approval in May 2017.

The PRO version differs from the recently approved MindMotion GO in that it is intended for use in patients with severe impairments as well as in early hospital care—in an inpatient setting—with therapeutic activities able to take place within 4 days after a neurological incident.

“Now that both MindMotion products have FDA clearance, MindMaze delivers a full spectrum of neuro-care solutions for both inpatient and outpatient recovery for patients in the United States,” said Tej Tadi, PhD, the CEO and founder of MindMaze, in a statement. “Our unique capability to safely and securely acquire data through our platform is essential for patient recovery and performance, and positions MindMaze as a powerhouse for the future of brain-machine interfaces. Beyond healthcare, this will enable powerful AI-based applications. We are working on a range of brain-tech initiatives at MindMaze to build the infrastructure for innovations to improve patients’ quality of life.”

The mobile MindMotion GO allows for real-time audio and visual feedback, aiding physicians in the assessment of progress and tailoring of therapy to their individual patient’s performance, according to MindMaze. Additionally, it enables the patients to see their progress as well. The set-up and calibration can be done in less than 5 minutes, so patients can begin rehabilitation sessions while physicians facilitate case management.

The program is equipped with a variety of gamified engaging activities which cover motor and task functions and includes a 3D virtual environment. As a result, early findings have suggested that both patient engagement and adherence to therapy have been amplified. Thus far, MindMotion GO has been trialed with upward of 300 patients across therapy centers in the UK, Italy, Germany, and Switzerland.

Neurological impairments are the main cause of long-term disability in the United States, with a recent study estimating direct and indirect costs associated with neurological diseases cost roughly $800 billion annually. For stroke alone, there are almost 800,000 cases each year, with direct annual costs estimated at $22.8 billion.

MindMaze’s Continuum of Care seeks to support earlier, and ongoing, intervention to enable by healthcare providers in the United States to have access to a cost-effective solution for improving neurorehabilitation results.

Even more resources pertaining to stroke prevention and care can be found on MD Magazine‘s new sister site, NeurologyLive.

via FDA Approves MindMotion GO, Mobile Neurorehabilitation Product | MD Magazine

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[Abstract + References] Design of Isometric and Isotonic Soft Hand for Rehabilitation Combining with Noninvasive Brain Machine Interface

Abstract

Comparing with the traditional way for hand rehabilitation, such as simple trainers and artificial rigid auxiliary, this paper presents an isometric and isotonic soft hand for rehabilitation supported by the soft robots theory which aims to satisfy the more comprehensive rehabilitation requirements. Salient features of the device are the ability to achieve higher and controllable stiffness for both isometric and isotonic contraction. Then we analyze the active control for isometric and isotonic movement through electroencephalograph (EEG) signal. This paper focuses on three issues. The first is using silicon rubber to build a soft finger which can continuously stretch and bend to fit the basic action of the fingers. The second is changing stiffness of the finger through the coordination between variable stiffness cavity and actuating cavity. The last is to classify different EEG states based on isometric and isotonic contraction using common spatial pattern feature extraction (CSP) methods and support vector machine classification methods (SVM). On this basis, an EEG-based manipulator control system was set up.

 

I. Introduction

In recent years, stroke has became one of the major health problems which significantly affect the daily life of the elderly, and hand rehabilitation is introduced as an auxiliary treatment. Though various kinds of mechanical devices for hand rehabilitation have been developed, some deficiencies still exist in the current rigid rehabilitation hand, such as the degrees of freedom is not enough, complexity, unsafe status, overweight, being uncomfortable, unfitness and so on. Therefore, with the growth of aging population, it is highly needed to develop some new devices to satisfy the comprehensive rehabilitation requirements. Meanwhile, inspired by the mollusks in nature, soft robot is made of soft materials that can withstand large strains. It is a new type of continuum robot with high flexibility and environmental adaptability. The soft robot has a broad application prospects in military detection techniques, such as instance search, rescue, medical application and other fields.

References

1. J Zhang, H Wang, J Tang et al., “Modeling and design of a soft pneumatic finger for hand rehabilitation [C]”, IEEE International Conference on Information and Automation, pp. 2460-2465, 2015.

2. H Godaba, J Li, Y Wang et al., “A Soft Jellyfish Robot Driven by a Dielectric Elastomer Actuator [J]”, IEEE Robotics & Automation Letters, vol. 1, no. 2, pp. 624-631, 2016.

3. Y Yang, Y. Chen, “Novel design and 3D printing of variable stiffness robotic fingers based on shape memory polymer [C]”, IEEE International Conference on Biomedical Robotics and Biomechatronics, pp. 195-200, 2016.

4. M Wehner, R L Truby, D J Fitzgerald et al., “An integrated design and fabrication strategy for entirely soft autonomous robots [J]”, Nature, vol. 536, no. 7617, pp. 451, 2016.

5. P Polygerinos, Z Wang, K C Galloway et al., “Soft robotic glove for combined assistance and at-home rehabilitation [J]”, Robotics & Autonomous Systems, vol. 73, no. C, pp. 135-143, 2014.

6. M Tian, Y Xiao, X Wang et al., “Design and Experimental Research of Pneumatic Soft Humanoid Robot Hand [M]/ /” in Robot Intelligence Technology and Applications 4. Springer International Publishing, 2017.

7. K Y Hong, J H Lim, F Nasrallah et al., “A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness [C]”, IEEE International Conference on Robotics and Automation, pp. 4967-4972, 2015.

8. J.R Wolpaw, N Birbaumer, WJ Heetderks, DJ Mcfarland, PH Peckham, G Schalk et al., “Brain-computer interface technology: a review of thefirst international meeting”, IEEE Transactions on Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society, vol. 8, no. 2, pp. 164, 2000.

9. C Ethier, ER Oby, MJ Bauman, LE. Miller, “Restoration of grasp following paralysis through brain-controlled stimulation of muscles”, Nature, vol. 485, no. 7398, pp. 368, 2012.

10. C JL, B W, D JE, W W, T EC, W DJ et al., “High-performance neuroprosthetic control by an individual with tetraplegia”, Lancet, vol. 381, no. 9866, pp. 557-564, 2013.

11. UA Qidwai, M. Shakir, Fuzzy Classification-Based Control of Wheelchair Using EEG Data to Assist People with Disabilities, vol. 7666, pp. 458-467, 2012.

12. UA Qidwai, M. Shakir, Fuzzy Classification-Based Control of Wheelchair Using EEG Data to Assist People with Disabilities, vol. 7666, pp. 458-467, 2012.

13. D Broetz, C Braun, C Weber, S.R Soekadar, A Caria, N. Birbaumer, “Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report”, Neurorehabilitation & Neural Repair, vol. 24, no. 7, pp. 674, 2010.

14. BH. Dobkin, “Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation”, Journal of Physiology, vol. 579, no. Pt 3, pp. 637, 2007.

15. S.R Soekadar, N Birbaumer, LG. Cohen, Brain-Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma, Japan:Springer, 2011.

16. LR Hochberg, B Daniel, J Beata, NY Masse, JD Simeral, V Joern et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm”, Nature, vol. 485, no. 7398, pp. 372-375, 2013.

17. S R Soekadar, M Witkowski, C Gómez et al., Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia [J], vol. 1, no. 1, pp. eaag3296, 2016.

18. L B. Rosenberg, Force feedback interface having isotonic and isometric functionality: CA US 5825308 A [P], 1998.

19. L B. Rosenberg, Isotonic-isometric haptic feedback interface: US US71 02541 [P], 2006.

20. J T Gwin, D P. Ferris, “An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions [J]”, Journal of NeuroEngineering and Rehabilitation, vol. 9, no. 1, pp. 35, 9 2012-06-09.

21. S Bouisset, F Goubel, B. Maton, “[Isometric isotonic contraction and anisotonic isometric contraction: an electromyographic comparison] [J]”, Electromyography & Clinical Neurophysiology, vol. 13, no. 5, pp. 525, 1973.

 

via Design of Isometric and Isotonic Soft Hand for Rehabilitation Combining with Noninvasive Brain Machine Interface – IEEE Conference Publication

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[VIDEO] Brain-Machine Interfaces for Restoration of Motor Function and Communication – NIH VideoCast

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Jaimie Henderson, M.D. is director of the Stanford program in Stereotactic and Functional Neurosurgery, and co-director (with Prof. Krishna Shenoy, PhD) of the Stanford Neural Prosthetics Translational Laboratory (NPTL). His research interests encompass several areas of stereotactic and functional neurosurgery, including frameless stereotactic approaches for therapy delivery to deep brain nuclei; mechanisms of action of deep brain stimulation; cortical physiology and its relationship to normal and pathological movement; neural prostheses; and the development of novel neuromodulatory techniques for the treatment of neurological diseases. During his residency in the early 1990’s, Dr. Henderson was intimately involved with the development of the new field of image-guided surgery. This innovative technology has revolutionized the practice of neurosurgery, allowing for safer and more effective operations with reduced operating time. Dr. Henderson remains one of the world’s foremost experts on the application of image-guided surgical techniques to functional neurosurgical procedures such as the placement of deep brain stimulators for movement disorders, epilepsy, pain, and psychiatric diseases. His work with NPTL focuses on the creation of clinically viable interfaces between the human brain and prosthetic devices to assist people with severe neurological disability.

NIH Neuroscience Series Seminar
For more information go to https://neuroscience.nih.gov/neuroseries/Home.aspx

via NIH VideoCast – Brain-Machine Interfaces for Restoration of Motor Function and Communication

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[Review] Review of devices used in neuromuscular electrical stimulation for stroke rehabilitation – PDF

Abstract

Neuromuscular electrical stimulation (NMES), specifically functional electrical stimulation (FES) that compensates for voluntary motion, and therapeutic electrical stimulation (TES) aimed at muscle strengthening and recovery from paralysis are widely used in stroke rehabilitation. The electrical stimulation of muscle contraction should be synchronized with intended motion to restore paralysis. Therefore, NMES devices, which monitor electromyogram (EMG) or electroencephalogram (EEG) changes with motor intention and use them as a trigger, have been developed. Devices that modify the current intensity of NMES, based on EMG or EEG, have also been proposed. Given the diversity in devices and stimulation methods of NMES, the aim of the current review was to introduce some commercial FES and TES devices and application methods, which depend on the condition of the patient with stroke, including the degree of paralysis.

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

Abstract

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.

Introduction

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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[BOOK] Progress in Motor Control: Theories and Translations – Google Books

ΕξώφυλλοThis single volume brings together both theoretical developments in the field of motor control and their translation into such fields as movement disorders, motor rehabilitation, robotics, prosthetics, brain-machine interface, and skill learning. Motor control has established itself as an area of scientific research characterized by a multi-disciplinary approach. Its goal is to promote cooperation and mutual understanding among researchers addressing different aspects of the complex phenomenon of motor coordination. Topics covered include recent theoretical advances from various fields, the neurophysiology of complex natural movements, the equilibrium-point hypothesis, motor learning of skilled behaviors, the effects of age, brain injury, or systemic disorders such as Parkinson’s Disease, and brain-computer interfaces.

Source: Progress in Motor Control: Theories and Translations – Google Books

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[Abstract] Brain-computer interfaces for communication and rehabilitation : Nature Reviews Neurology

Abstract

Brain–computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

Figures

  1. General framework of brain-computer interface (BCI) systems.
    Figure 1
  2. Use of a brain-computer interface in severe chronic stroke.
    Figure 2

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Source: Brain-computer interfaces for communication and rehabilitation : Nature Reviews Neurology : Nature Research

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[ARTICLE] Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients – Full Text HTML

Abstract

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3–13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.

Introduction

Spinal Cord Injury (SCI) rehabilitation remains a major clinical challenge, especially in cases involving chronic complete injury. Clinical studies using body weight support systems1,2, robotic assistance1,2,3,4, and functional electrostimulation of the leg5,6 have proposed potential solutions for assisting SCI patients in walking7,8. Yet, none of these approaches have generated any consistent clinical improvement in neurological functions, namely somatosensory (tactile, proprioceptive, pain, and temperature) perception and voluntary motor control, below the level of the spinal cord lesion.

Since the first experimental demonstrations in rats9, monkeys10,11, and the subsequent clinical reports in humans12,13,14, brain-machine interfaces (BMIs) have emerged as potential options to restore mobility in patients who are severely paralyzed as a result of spinal cord injuries (SCIs) or neurodegenerative disorders15. However, to our knowledge, no study has suggested that long-term training associating BMI-based paradigms and physical training could trigger neurological recovery, particularly in patients clinically diagnosed as having a complete SCI. Yet, in 60–80% of these “complete” SCI patients, neurophysiological assessments16,17 and post-mortem anatomical18 studies have indicated the existence of a number of viable axons crossing the level of the SCI. This led some authors to refer to these patients as having a “discomplete” SCI17 and predict that these remaining axons could mediate some degree of neurological recovery.

For the past few years, our multidisciplinary team has been engaged in a project to implement a multi-stage neurorehabilitation protocol – the Walk Again Neurorehabilitation (WA-NR) – in chronic SCI patients. This protocol included the intensive employment of immersive virtual-reality environments, combining training on non-invasive brain-control of virtual avatar bodies with rich visual and tactile feedback, and the use of closed-loop BMI platforms in conjunction with lower limb robotic actuators, such as a commercially available robotic walker (Lokomat, Hocoma AG, Volketswil, Switzerland), and a brain-controlled robotic exoskeleton, custom-designed specifically for the execution of this project.

Originally, our central goal was to explore how much such a long-term BMI-based protocol could help SCI patients regain their ability to walk autonomously using our brain-controlled exoskeleton. Among other innovations, this device provides tactile feedback to subjects through the combination of multiple force-sensors, applied to key locations of the exoskeleton, such as the plantar surface of the feet, and a multi-channel haptic display, applied to the patient’s forearm skin surface.

Unexpectedly, at the end of the first 12 months of training with the WA-NR protocol, a comprehensive neurological examination revealed that all of our eight patients had experienced a significant clinical improvement in their ability to perceive somatic sensations and exert voluntary motor control in dermatomes located below the original SCI. EEG analysis revealed clear signs of cortical functional plasticity, at the level of the primary somatosensory and motor cortical areas, during the same period. These findings suggest, for the first time, that long-term exposure to BMI-based protocols enriched with tactile feedback and combined with robotic gait training may induce cortical and subcortical plasticity capable of triggering partial neurological recovery even in patients originally diagnosed with a chronic complete spinal cord injury.

Methods

Eight paraplegic patients, suffering from chronic (>1 year) spinal cord injury (SCI, seven complete and one incomplete, see Fig. 1A, Supplementary Methods Inclusion/exclusion Criteria), were followed by a multidisciplinary rehabilitation team, comprised of clinical staff, engineers, neuroscientists, and roboticists, during the 12 months of 2014. Our clinical protocol, which we named the Walk Again Neurorehabilitation (WA-NR), was approved by both a local ethics committee (Associação de Assistência à Criança Deficiente, Sao Paulo, Sao Paulo, Brazil #364.027) and the Brazilian federal government ethics committee (CONEP, CAAE: 13165913.1.0000.0085). All research activities were carried out in accordance with the guidelines and regulations of the Associação de Assistência à Criança Deficiente and CONEP. Each participant signed written informed consent before enrolling in the study. The central goal of this study was to investigate the clinical impact of the WA-NR, which consisted of the integration between traditional physical rehabilitation and the use of multiple brain-machine interface paradigms (BMI). This protocol included six components: (1) an immersive virtual reality environment in which a seated patient employed his/her brain activity, recorded via a 16-channel EEG, to control the movements of a human body avatar, while receiving visuo-tactile feedback; (2) identical interaction with the same virtual environment and BMI protocol while patients were upright, supported by a stand-in-table device; (3) training on a robotic body weight support (BWS) gait system on a treadmill (Lokomat, Hocoma AG, Switzerland); (4) training with a BWS gait system fixed on an overground track (ZeroG, Aretech LLC., Ashburn, VA); (5) training with a brain-controlled robotic BWS gait system on a treadmill; and (6) gait training with a brain-controlled, sensorized 12 degrees of freedom robotic exoskeleton (seeSupplementary Material).

(A) Cumulated number of hours and sessions for all patients over 12 months. We report cumulated hours for the following activities: classic physiotherapy activities (e.g. strengthening/stretching), gait-BMI-based neurorehabilitation, one-to-one consultations with a psychologist, periodic measurements for research purposes and routine medical monitoring (vital signs, etc.). (B) Neurorehabilitation training paradigm and corresponding cumulated number of hours for all patients: 1) Brain controlled 3D avatar with tactile feedback when patient is seated on a wheelchair or 2) in an orthostatic position on a stand-in-table, 3) Gait training using a robotic body weight support (BWS) system on a treadmill (LokomatPro, Hocoma), 4) Gait training using an overground BWS system (ZeroG, Aretech). 5–6) Brain controlled robotic gait training integrated with the sensory support of the tactile feedback at gait devices (BWS system on a treadmill or the exoskeleton). (C) Material used for the clinical sensory assessment of dermatomes in the trunk and lower limbs: to evaluate pain sensitivity, examiner used a pin-prick in random positions of the body segments. Nylon monofilaments applying forces ranging between 300 to 0.2 grams on the skin, were used to evaluate patients’ sensitivity for crude to fine touch. Dry cotton and alcohol swabs were used to assess respectively warm and cold sensation. Vibration test was done using a diapason on patients’ legs bone surface. Deep pressure was assessed with an adapted plicometer in every dermatome.

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[Abstract] Brain–machine interfaces for rehabilitation of poststroke hemiplegia

Abstract

Noninvasive brain–machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis.

However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma.

In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback.

Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization.

Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.

 

Source: Brain–machine interfaces for rehabilitation of poststroke hemiplegia

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[Abstract] Brain–machine interfaces for rehabilitation of poststroke hemiplegia

Abstract

Noninvasive brain–machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis. However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma. In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback. Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization. Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.

 

Source: Brain–machine interfaces for rehabilitation of poststroke hemiplegia

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