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


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.


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


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

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


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


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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[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


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.


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


  1. Wyrwicka, W. & Sterman, M. B. Instrumental conditioning of sensorimotor cortex EEG spindles in the waking cat. Physiol. Behav. 3, 703707 (1968).
  2. Kamiya, J. in Altered states of consciousness. (ed Tart, C.) 519529 (New York: Wiley, 1969).
  3. Fetz, E. E. & Baker, M. A. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J. Neurophysiol. 36, 179204 (1973).
  4. Vidal, J.-J. Toward direct brain-computer communication. Annu. Rev. Biophys. Bioeng. 2, 157180 (1973).
    The first paper describing a brain computer interface and the hypothetical learning mechanisms involved.
  5. Sterman, M. B., Wyrwicka, W. & Roth, S. Electrophysiological correlates and neural substrates of alimentary behavior in the cat. Ann. NY Acad. Sci. 157, 723739 (1969).
  6. Sterman, M. & Friar, L. Suppression of seizures in epileptic Following on sensorimotor EEG feedback training. Electroencephalogr. Clin. Neurophysiol. 33, 8995 (1972).
  7. Lubar, J. F. & Shouse, M. N. EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR) – A preliminary report. Biofeedback Self Regul. 1, 293306 (1976).
  8. Sterman, M. B. & Macdonald, L. R. Effects of central cortical EEG feedback training on incidence of poorly controlled seizures. Epilepsia 19, 207222 (1978).
  9. Chapin, J. K., Moxon, K. A., Markowitz, R. S. & Nicolelis, M. A. L. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2, 664670 (1999).
  10. Donoghue, J. P. Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5, 10851088 (2002).
  11. Nicolelis, M. A. L. Actions from thoughts. Nature 409, 403407 (2001).
  12. Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 10981101 (2008).
  13. Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct Cortical Control of 3D Neuroprosthetic Devices. Sci. 296, 18291832 (2002).
  14. Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. & Shenoy, K. V. A high-performance brain-computer interface. Nature 442, 195198 (2006).
  15. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361365 (2000).
  16. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. Brain-machine interface: Instant neural control of a movement signal. Nature 416, 141142 (2002).
  17. Carmena, J. M. et al. Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol. 1, e2 (2003).
    This paper provides the most advanced and detailed neurophysiological analysis of the neuronal mechanisms behind brain–computer interface control of complex movements.

  18. Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164171 (2006).
  19. Donoghue, J. P., Nurmikko, A., Black, M. & Hochberg, L. R. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J. Physiol. 579, 603611 (2007).
  20. Birbaumer, N., Ramos Murguialday, A., Weber, C. & Montoya, P. Chapter 8 neurofeedback and brain-computer Interface: clinical applications. Int. Rev. Neurobiol. 86, 107117 (2009).
  21. Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H. & Kaiser, J. Neurofeedback treatment for attention-deficit / hyperactivity disorder in children: a comparison with methylphenidate. Appl. Psychophysiol. Biofeedback 28, 112 (2003).
  22. Monastra, V. J. et al. Electroencephalographic biofeedback in the treatment of attention-deficit / hyperactivity disorder. J. Neurother. 9, 534 (2006).
  23. Kotchoubey, B. et al. Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 42, 406416 (2001).
  24. Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 11421145 (2015).
  25. Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372375 (2012).
    The first paper describing multidemensional movement control of an arm–hand robotic device using an implanted microelectrode array in the primary motor cortex of a paralyzed patient.

  26. Jarosiewicz, B. et al. Virtual typing by people with tetraplegia using a stabilized, self-calibrating intracortical brain-computer interface. IEEE BRAIN Gd. Challenges Conf. Washington, DC 7, 111 (2014).
  27. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J., Gerner, H. J. & Rupp, R. ‘Thought ‘ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 3336 (2003).
  28. Caria, A., Sitaram, R. & Birbaumer, N. Real-time fMRI: a tool for local brain regulation. Neuroscientist. 18, 487501 (2012).
  29. Chaudhary, U., Birbaumer, N. & Curado, M. R. Brain-machine interface (BMI) in paralysis. Ann. Phys. Rehabil. Med. 58, 913 (2015).
  30. Nijboer, F. et al. An auditory brain–computer interface (BCI). J. Neurosci. Methods 167, 4350 (2008).
  31. Chatterjee, A., Aggarwal, V., Ramos, A., Acharya, S. & Thakor, N. V. A brain-computer interface with vibrotactile biofeedback for haptic information. J. Neuroeng. Rehabil. 4, 112(2007).
  32. Lugo, Z. R. et al. A vibrotactile p300-based brain-computer interface for consciousness detection and communication. Clin. EEG Neurosci. 45, 1421 (2014).
  33. Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E. & Donoghue, J. P. Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. J. Neurophysiol. 107, 13371355 (2012).
  34. Flint, R. D., Wright, Z. A., Scheid, M. R. & Slutzky, M. W. Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J. Neural Eng. 10, 056005 (2013).
  35. So, K., Dangi, S., Orsborn, A. L., Gastpar, M. C. & Carmena, J. M. Subject-specific modulation of local field potential spectral power during brain-machine interface control in primates. J. Neural Eng. 11, 026002 (2014).
  36. Mehring, C. et al. Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex. J. Physiol. Paris 98, 498506 (2004).
  37. Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neuronal population coding of movement direction. Science 233, 14161419 (1986).
  38. Georgopoulos, A. P. & Kettner, R. E. & Schwartz, A. B. Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J. Neurosci. 8, 29282937 (1988).
  39. Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M. R. & Donoghue, J. P. Brain-machine interface: Instant neural control of a movement signal. Nature 416, 121142 (2002).
  40. Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G. & Moran, D. W. A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 6371(2004).
  41. Felton, E. a, Wilson, J. A., Williams, J. C. & Garell, P. C. Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. J. Neurosurg. 106, 495500 (2007).
  42. Clancy, K. B., Koralek, A. C., Costa, R. M., Feldman, D. E. & Carmena, J. M. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 17, 807809 (2014).
  43. Birbaumer, N., Elbert, T., Canavan, A. & Rockstroh, B. Slow potentials of the cerebral cortex and behavior. Physiol. Rev. 70, 141 (1990).
  44. Kubler, A. et al. Brain-computer communication: self regulation of slow cortical potentials for verbal communication. Arch. Phys. Med. Rehabil. 82, 15331539 (2001).
  45. Birbaumer, N., Hinterberger, T., Kübler, A. & Neumann, N. The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 120123 (2003).
  46. Pfurtscheller, G. & Aranibar, A. Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr. Clin. Neurophysiol. 46, 138146 (1979).
  47. Kübler, A. et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64, 17751777 (2005).
  48. Wolpaw, J. R. et al. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767791 (2002).
  49. Farwell, L. A. & Donchin, E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70, 510523(1988).
  50. Kübler, A. et al. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Ann. NY Acad. Sci. 1157, 90100 (2009).
  51. Halder, S. et al. An auditory oddball brain-computer interface for binary choices. Clin. Neurophysiol. 121, 516523 (2010).
  52. Pires, G., Nunes, U. & Castelo-Branco, M. Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. J. Neurosci. Methods 195, 270281 (2011).
  53. Sellers, E. W. & Donchin, E. A P300-based brain-computer interface: Initial tests by ALS patients. Clin. Neurophysiol. 117, 538548 (2006).
  54. Sellers, E. W., Vaughan, T. M. & Wolpaw, J. R. A brain-computer interface for long-term independent home use. Amyotroph. Lateral Scler. 11, 449455 (2010).
  55. Lesenfants, D. et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J. Neural Eng. Neural Eng. 11, 035002 (2014).
  56. Zhu, D., Bieger, J., Molina, G. G. & Aarts, R. M. A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci.
  57. Chavarriaga, R. & Millán, J. del R. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 381388 (2010).
  58. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150157 (2001).
  59. Birbaumer, N., Ruiz, S. & Sitaram, R. Learned regulation of brain metabolism. Trends Cogn. Sci. 17, 295302 (2013).
    An extensive review of basic and clinical neurofeedback studies using learning of metabolic brain resonses (BOLD or oxygenation) and the effects on behaviour and cognition.
  60. DeCharms, R. C. et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21, 436443 (2004).
  61. Rota, G., Handjaras, G., Sitaram, R., Birbaumer, N. & Dogil, G. Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing. Brain Lang. 117, 123132 (2011).
  62. Weiskopf, N. et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19, 577586 (2003).
  63. Yoo, S. S. et al. Brain computer interface using fMRI: spatial navigation by thoughts. Neuroreport 15, 15911595 (2004).
  64. Birbaumer, N. et al. Deficient fear conditioning in psychopathy: a functional magnetic resonance imaging study. Arch. Gen. Psychiatry 62, 799805 (2005).
  65. Linden, D. E. J. et al. Real-time self-regulation of emotion networks in patients with depression. PLoS One (2012).
  66. Li, X. et al. Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: A preliminary real-time fMRI study. Addict. Biol. 18, 739748(2013).
  67. Chaudhary, U., Hall, M., DeCerce, J., Rey, G. & Godavarty, A. Frontal activation and connectivity using near-infrared spectroscopy: verbal fluency language study. Brain Res. Bull. 84, 197205 (2011).
  68. Chaudhary, U. et al. Motor response investigation in individuals with cerebral palsy using near infrared spectroscopy: pilot study. Appl. Opt. 53, 503510 (2014).
  69. Obrig, H. NIRS in clinical neurology – a ‘promising’ tool? Neuroimage 85, 535546 (2014).
  70. Gallegos-Ayala, G. et al. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology 82, 19301932 (2014).
    The first report of a controlled case study with BCI in a completely paralyzed, locked-in patient restoring communication.

  71. Naito, M. et al. A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Trans. Inf. Syst. E90D, 10281037 (2007).
  72. Birbaumer, N. et al. A spelling device for the paralysed. Nature 398, 297298 (1999).
  73. Ramos-Murguialday, A. et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100108 (2014).
  74. Birbaumer, N., Murguialday, A. R. & Cohen, L. Brain-computer interface in paralysis. Curr. Opin. Neurol. 21, 634638 (2008).
  75. Chou, S. M. & Norris, F. H. Amyotrophic lateral sclerosis: Lower motor neuron disease spreading to upper motor neurons. Muscle Nerve 16, 864869 (1993).
  76. Bauer, G., Gerstenbrand, F. & Rumpl, E. Varieties of the Locked-in Syndrome. J. Neurol.221, 7791 (1979).
  77. Beukelman, D., Fager, S. & Nordness, A. Communication support for people with ALS. Neurol. Res. Int. 2011, 714693 (2011).
  78. Beukelman, D. & Mirenda, P. Augmentative & alternative communication: Supporting children & adults with complex communication needs. (Paul, H. Brookes, Baltimore, MD, 2005).
  79. Birbaumer, N. & Cohen, L. G. Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621636 (2007).
  80. Kennedy, P. R. & Bakay, R. A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9, 17071711 (1998).
  81. Kennedy, P. R., Bakay, R. A., Moore, M. M., Adams, K. & Goldwaithe, J. Direct control of a computer from the human central nervous system. IEEE Trans. Rehabil. Eng. 8, 198202(2000).
  82. Kennedy, P. et al. Using human extra-cortical local field potentials to control a switch. J. Neural Eng. 1, 7277 (2004).
  83. Wilhelm, B., Jordan, M. & Birbaumer, N. Communication in locked-in syndrome: effects of imagery on salivary pH. Neurology 67, 534535 (2006).
  84. Murguialday, A. R. et al. Transition from the locked in to the completely locked-in state: a physiological analysis. Clin. Neurophysiol. 122, 925933 (2011).
  85. Birbaumer, N. Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 43, 517532 (2006).
  86. Kübler, A. & Birbaumer, N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin. Neurophysiol. 119, 26582666 (2008).
  87. Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl Acad. Sci. USA 101, 1784917854 (2004).
  88. Bai, O., Lin, P., Huang, D., Fei, D. Y. & Floeter, M. K. Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients. Clin. Neurophysiol. 121, 12931303 (2010).
  89. Thorns, J. et al. Movement initiation and inhibition are impaired in amyotrophic lateral sclerosis. Exp. Neurol. 224, 389394 (2010).
  90. Birbaumer, N., Piccione, F., Silvoni, S. & Wildgruber, M. Ideomotor silence: the case of complete paralysis and brain-computer interfaces (BCI). Psychol. Res. 76, 183191 (2012).
  91. Hinterberger, T. et al. Neuronal mechanisms underlying control of a brain – computer interface. Eur. J. Neurosci. 21, 31693181 (2005).
  92. Hinterberger, T. et al. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy Behav. 13, 300306 (2008).
  93. Koralek, A. C. et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483, 331335 (2012).
  94. Dworkin, B. R. & Miller, N. E. Failure to replicate visceral learning in the acute curarized rat preparation. Behav. Neurosci. 100, 299314 (1986).
    This paper describes the failure to establish instrumental learning of physiological responses in the curarized rat and possible reasons for this problem.
  95. Stocco, A., Lebiere, C. & Anderson, J. R. Conditional routing of information to the cortex: a model of the basal ganglia’s role in cognitive coordination. Psychol. Rev. 117, 541574(2010).
  96. Birbaumer, N. & Chaudhary, U. Learning from brain control: clinical application of brain–computer interfaces. e-Neuroforum 6, 8795 (2015).
  97. Furdea, A. et al. A new (semantic) reflexive brain-computer interface: in search for a suitable classifier. J. Neurosci. Methods 203, 233240 (2012).
  98. Ruf, C. A., De Massari, D., Wagner-Podmaniczky, F., Matuz, T. & Birbaumer, N. Semantic conditioning of salivary pH for communication. Artif. Intell. Med. 59, 18 (2013).
  99. De Massari, D. et al. Brain communication in the locked-in state. Brain 136, 19892000(2013).
  100. Lulé, D. et al. Brain responses to emotional stimuli in patients with amyotrophic lateral sclerosis (ALS). J. Neurol. 254, 519527 (2007).
  101. Lulé, D. et al. Life can be worth living in locked-in syndrome. Prog. Brain Res. 177, 339351(2009).
  102. Lulé, D. et al. Quality of life in fatal disease: the flawed judgement of the social environment. J. Neurol. 260, 28362843 (2013).
  103. Chaudhary, U. & Birbaumer, N. Communication in locked-in state after brainstem stroke: a brain- computer-interface approach. Ann. Transl. Med. 3, 24 (2015).
  104. Simeral, J. D., Kim, S. P., Black, M. J., Donoghue, J. P. & Hochberg, L. R. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J. Neural Eng. 8, 025027 (2011).
  105. Kübler, A. et al. Self-regulation of slow cortical potentials in completely paralyzed human patients. Neurosci. Lett. 252, 171174 (1998).
  106. Piccione, F. et al. P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin. Neurophysiol. 117, 531537 (2006).
  107. Sellers, E. W., Ryan, D. B. & Hauser, C. K. Noninvasive brain-computer interface enables communication after brainstem stroke. Sci. Transl. Med. 6, 257re7 (2014).
  108. Cirstea, M. C., Ptito, A. & Levin, M. F. Arm reaching improvements with short-term practice depend on the severity of the motor deficit in stroke. Exp. Brain Res. 152, 476488 (2003).
  109. Young, J. & Forster, A. Review of stroke rehabilitation. BMJ 334, 8690 (2007).
  110. Saka, O., McGuire, A. & Wolfe, C. Cost of stroke in the United Kingdom. Age Ageing 38, 2732 (2008).
  111. Langhorne, P., Bernhardt, J. & Kwakkel, G. Stroke rehabilitation. Lancet 377, 16931702(2015).
  112. Hendricks, H. T., van Limbeek, J., Geurts, A. C. & Zwarts, M. J. Motor recovery after stroke: a systematic review of the literature. Arch. Phys. Med. Rehabil. 83, 16291637 (2002).
  113. Ward, N. S. & Cohen, L. G. Mechanisms underlying recovery of motor function after stroke. Arch. Neurol. 61, 18441848 (2004).
  114. Taub, E., Uswatte, G. & Pidikiti, R. Constraint-induced movement therapy: a new family of techniques with broad application to physical rehabilitation – a clinical review. J. Rehabil. Res. Dev. 36, 237251 (1999).
  115. Wolf, S. L. et al. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA 296, 20952104(2006).
  116. Buch, E. R. et al. Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 135, 596614 (2012).
  117. Belda-Lois, J.-M. et al. Rehabilitation of gait after stroke: a review towards a top-down approach. J. Neuroeng. Rehabil. 8, 66 (2011).
  118. Chollet, F. et al. Fluoxetine for motor recovery after acute ischaemic stroke (FLAME): a randomised placebo-controlled trial. Lancet Neurol. 10, 123130 (2011).
  119. Savitz, S. I. et al. Stem cells as an emerging paradigm in stroke 3: enhancing the development of clinical trials. Stroke 45, 634639 (2014).
  120. Ganguly, K., Dimitrov, D. F., Wallis, J. D. & Carmena, J. M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14, 662667(2011).
  121. Gulati, T. et al. Robust neuroprosthetic control from the stroke perilesional cortex. J. Neurosci. 35, 86538661 (2015).
  122. Nishimura, Y., Perlmutter, S. I., Eaton, R. W. & Fetz, E. E. Spike-timing-dependent plasticity in primate corticospinal connections induced during free behavior. Neuron 80, 13011309(2013).
    This paper describes the neurophysiological bases of BCI applications in spinal cord injury.

  123. Lucas, T. H. & Fetz, E. E. Myo-cortical crossed feedback reorganizes primate motor cortex output. J. Neurosci. 33, 52615274 (2013).
  124. Ang, K. K. et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front. Neuroeng. (2014).
  125. Ono, T. et al. Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front. Neuroeng. (2014).
  126. Pichiorri, F. et al. Brain–computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 77, 851865 (2015).
  127. Kasahara, K., DaSalla, C. S., Honda, M. & Hanakawa, T. Neuroanatomical correlates of brain–computer interface performance. Neuroimage 110, 95100 (2015).
  128. Bensmaia, S. J. & Miller, L. E. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat. Rev. Neurosci. 15, 313325 (2014).
  129. Ren, X. et al. Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications. Biomed. Eng. IEEE Trans. 61, 288296 (2014).
  130. Jiang, N., Gizzi, L., Mrachacz-Kersting, N., Dremstrup, K. & Farina, D. A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials. Clin. Neurophysiol. 126, 154159 (2015).
  131. Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557564 (2013).
  132. Ouzký, M. Towards concerted efforts for treating and curing spinal cord injury (Council of Europe Parliamentary Assembly document 9401). (2002)
  133. Van Den Berg, M. E., Castellote, J. M., Mahillo-Fernandez, I. & De Pedro-Cuesta, J.Incidence of spinal cord injury worldwide: a systematic review. Neuroepidemiology 34, 184192 (2010).
  134. Wolpaw, J. R. The complex structure of a simple memory. Trends Neurosci. 20, 588594(1997).
  135. Wang, W. et al. An electrocorticographic brain interface in an individual with tetraplegia. PLoS ONE (2013).
  136. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J. & Gerner, H. J. & Rupp, R. ‘Thought’ – Control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 3336 (2003).
  137. Nguyen, J. S., Su, S. W. & Nguyen, H. T. Experimental study on a smart wheelchair system using a combination of stereoscopic and spherical vision. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013, 45974600 (2013).
  138. Kasashima-Shindo, Y. et al. Brain–computer interface training combined with transcranial direct current stimulation in patients with chronic severe hemiparesis: proof of concept study. J. Rehabil. Med. 47, 318324 (2015).
  139. Enzinger, C. et al. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp. Brain Res. 190, 215223 (2008).
  140. King, C. E. et al. The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia. J. Neuroeng. Rehabil. 12, 80 (2015).
  141. Pfurtscheller, G., Guger, C., Müller, G., Krausz, G. & Neuper, C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211214 (2000).
    The first paper demonstrating noninvasive brain control using a sensorimotor rhythm brain–computer interface in a high spinal cord patient.

  142. Courtine, G. & Bloch, J. Defining Ecological Strategies in Neuroprosthetics. Neuron 86, 2933 (2015).
  143. van den Brand, R. et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 336, 11821185 (2012).
  144. Combaz, A. et al. A comparison of two spelling brain-computer interfaces based on visual P3 and SSVEP in locked-in syndrome. PLoS ONE (2013).
  145. Bardin, J. C. et al. Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain 134, 769782(2011).
  146. Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362, 579589 (2010).
  147. Schnakers, C. et al. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. Neurocase 15, 271277 (2009).
  148. Lulé, D. et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin. Neurophysiol. 124, 101106 (2013).

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


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.


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.


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.

Continue —> Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients : Scientific Reports

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


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


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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