Archive for September, 2018

[Abstract + References] Robotic and Sensor Technology for Upper Limb Rehabilitation – PM&R

Abstract

Robotic and sensor-based neurologic rehabilitation for the upper limb is an established concept for motor learning and is recommended in many national guidelines. The complexity of the human hands and arms and the different activities of daily living are leading to an approach in which robotic and sensor-based devices are used in combination to fulfill the multiple requirements of this intervention. A multidisciplinary team of the Fondazione Don Carlo Gnocchi (FDG), an Italian nonprofit foundation, which spans across the entire Italian territory with 28 rehabilitation centers, developed a strategy for the implementation of robotic rehabilitation within the FDG centers. Using an ad hoc form developed by the team, 4 robotic and sensor-based devices were identified among the robotic therapy devices commercially available to treat the upper limb in a more comprehensive way (from the shoulder to the hand). Encouraging results from a pilot study, which compared this robotic approach with a conventional treatment, led to the deployment of the same set of robotic devices in 8 other FDG centers to start a multicenter randomized controlled trial. Efficiency and economic factors are just as important as clinical outcome. The comparison showed that robotic group therapy costs less than half per session in Germany than standard individual arm therapy with equivalent outcomes. To ensure access to high-quality therapy to the largest possible patient group and lower health care costs, robot-assisted group training is a likely option.

 

References

  1. Lo, A.C., Guarino, P.D., Richards, L.G. et al, Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med2011;365:1749.
  2. Hesse, S., Heß, A., Werner, C.C., Kabbert, N., Buschfort, R. Effect on arm function and cost of robot assisted group therapy in subacute patients with stroke and a moderately to severely affected arm: A randomized controlled trial. Clin Rehabil2014;28:637–647.
  3. Smith BM, Albus JS, Barbera AJ. A Glossary of Terms for Robotics. Prepared for U.S. Air Force Materials Laboratory Integrated Computer Aided Manufacturing Program. U.S. Department of Commerce. National Bureau of Standards. 1981. Available at: https://www.gpo.gov/fdsys/pkg/GOVPUB-C13-7a9025561f229e1f7fb504ace852d602/pdf/GOVPUB-C13-7a9025561f229e1f7fb504ace852d602.pdf. Accessed September 5, 2018..

  4. Feigin, V.L., Forouzanfar, M.H., Krishnamurthi, R. et al, Global and regional burden of stroke during 1990-2010: Findings from the Global Burden of Disease Study 2010. Lancet2014;383:245–255.
  5. Mehrholz, J., Pohl, M., Platz, T., Kugler, J., Elsner, B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke.Cochrane Database Syst Rev2015;11:CD006876.
  6. Kolominsky-Rabas, P.L., Heuschmann, P.U., Marschall, D. et al, Lifetime cost of ischemic stroke in Germany: Results and national projections from a population-based stroke registry. The Erlangen Stroke Project. Stroke2006;37:1179–1183.
  7. Ringelstein, E.B., Nabavi, D.G. Der ischämische Schlaganfall: Eine praxisorientierte Darstellung von Pathophysiologie, Diagnostik und Therapie. 1st ed. KohlhammerGermany2007.
  8. Deutscher Verband für Physiotherapie. Aktuelle Arbeitsmarktdaten veröffentlicht—Fachkräftemangel in der Physiotherapie mehr als deutlich. Available at: https://www.physio-deutschland.de/fachkreise/news-bundesweit/einzelansicht/artikel/Aktuelle-Arbeitsmarktdaten-veroeffentlicht-Fachkraeftemangel-in-der-Physiotherapie-mehr-als-deutlich.html. Published March 2017. Accessed April 5, 2018..

  9. Wright, D.L., Shea, C.H. Cognition and motor skill acquisition: Contextual dependencies. in: C.R. Reynolds (Ed.) Cognitive assessment: A multidisciplinary perspectiveSpringer Verlag USBoston, MA1994:89–106.
  10. Masiero, S., Armani, M., Rosati, G. Upper-limb robot-assisted therapy in rehabilitation of acute stroke patients: Focused review and results of new randomized controlled trial. J Rehabil Res Dev2011;48:355–366.
  11. Mehrholz, J., Hädrich, A., Platz, T., Kugler, J., Pohl, M. Electromechanical and robot-assisted arm training for improving generic activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev2012;6:CD006876.
  12. Norouzi-Gheidari, N., Archambault, P.S., Fung, J. Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: Systematic review and meta-analysis of the literature. J Rehabil Res Dev2012;49:479–496.
  13. Masiero, S., Carraro, E., Ferraro, C., Gallina, P., Rossi, A., Rosati, G. Upper limb rehabilitation robotics after stroke: A perspective from the University of Padua, Italy. J Rehabil Med2009;41:981–985.
  14. Wagner, T.H., Lo, A.C., Peduzzi, P. et al, An economic analysis of robot-assisted therapy for long-term upper-limb impairment after stroke. Stroke2011;42:2630–2632.
  15. Masiero, S., Poli, P., Armani, M., Ferlini, G., Rizzello, R., Rosati, G. Robotic upper limb rehabilitation after acute stroke by NeReBot: Evaluation of treatment costs. Biomed Res Int2014;2014:265634.
  16. Aprile I. Multi-segmental robotic and technological upper limb rehabilitation in stroke. Fondazione Don Carlo Gnocchi Onlus. Clinical trial registration number NCT02879279. Available at: https://clinicaltrials.gov..

  17. De Wit, L., Putman, K., Dejaeger, E. et al, Use of time by stroke patients: A comparison of four European rehabilitation centers. Stroke2005;36:1977–1983.
  18. Lee, K.B., Lim, S.H., Kim, K.H. et al, Six-month functional recovery of stroke patients: A multi-time-point study. Int J Rehabil Res2015;38:173–180.
  19. Veerbeek, J.M., van Wegen, E., van Peppen, R. et al, What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One2014;9 (e87987).

 

via Robotic and Sensor Technology for Upper Limb Rehabilitation – PM&R

, , , , , , , ,

Leave a comment

[WEB SITE] Robotic trousers could help disabled people walk again

Robotic trousers could help disabled people walk again

Balloon muscles. Credit: University of Bristol

Could the answer to mobility problems one day be as easy as pulling on a pair of trousers? A research team led by Bristol University’s Professor Jonathan Rossiter has recently unveiled a prototype pair of robotic trousers that they hope could help some disabled people walk without other assistance.

As an engineer who researches ways of helping people with spinal chord injuries move their limbs again, I’m acutely aware of how the loss of mobility can affect a person’s quality of life, and how restoring that movement can help. Given the staggering number of people with disabilities (over 6.5m people with  in the UK alone) and our ageing population, devices that improve mobility could help a large segment of the population.

Yet despite 50 years of research, this kind of technology has rarely been adopted outside the lab. So is the novel development of robotic  on course to finally take a working mobility technology into the home?

Unlike the rigid robotic device in the Wallace and Gromit animated film The Wrong Trousers, the new so-called “Right Trousers” use soft  to create movement, as well as harnessing the wearer’s real muscles. These mimic human muscles in producing a force simply by becoming shorter and pulling on both ends.

By bundling several artificial muscles together, the assistive trousers can move a joint such as the knee, and help the user with movements such as standing up from a chair. Because the artificial muscles are elastic and soft they are safer than traditional motors used in rigid robotic exoskeletons that, although powerful, are stiff and uncomfortable.

The researchers have put forward several different ideas for how to shorten the artificial muscles and create movement. One design adapts the concept of air muscles, which are effectively balloons that expand sideways and shorten in length as they fill with air.

Another proposed design uses electricity to shorten an artificial  made from a gel placed between two copper plates. The gel is attracted to areas of high electrical voltage. So creating two different voltages in the plates forces the gel to shrink around one of them, bringing them closer together and shortening the muscle.

Another technology integrated in the assistive trousers is functional electrical stimulation (FES). Electrodes woven into the trousers strategically located over muscles can send specially designed electrical impulses into the body to hijack the communication channel between the brain and the muscles and directly command muscles to contract. By using existing muscles and bypassing the brain, the assistive trousers can even command muscles that the wearers might have difficulty using on their own (for example due to stroke).

The trousers can also help users who struggle to stand for any length of time thanks to specially made plastic knee braces that stiffen as they cool. Controlling the temperature of the braces allows the knee to move or lock in position to maintain standing without much effort needed by the muscles (real or artificial).

Other features include an automatic belt, using a mechanism similar to the air muscles, to make it easy and safe to put on and take off the trousers.

Robotic trousers could help disabled people walk again

Knee brace. Credit: University of Bristol

The researchers suggest creating an embedded electronic system that receives information about the wearer’s motion and state from sensors embedded throughout the trousers, and controls all of the systems to tailor movements to the user’s needs. The electronics would allow users to control their movement via controls directly woven onto the trousers. The challenge will be to time the movement of the artificial muscles and the electrical stimulation of the real muscles in response to the user’s posture.

Remaining challenges

The Right Trousers are unique in their approach to merging cutting edge research and well-established techniques in a single prototype. Aside from the novelty of robotic trousers, what makes the device so compelling as a practical assistive technology is the fact it can be adapted to many different users. This raises the hope it could be widely adopted where other previous ventures have failed.

However, this is only the prototype. A working product is probably at least five years away and significant questions must be answered to get to that stage. Where will it store all the power it needs? How can all the systems be miniaturised and embedded in the trousers so they don’t become bulky and awkward to wear? Can the controller predict the best action to take amid the ever-changing complexity of real environments where users will be walking?

Yet other technologies have the potential to improve the trousers even further. Brain-computer interfaces that can decode brain signals are now being used in systems that help paralysed people move again. Controlling the assistive trousers by thought could make taking a step effortless again for many people.

via Robotic trousers could help disabled people walk again

, , , , , , ,

Leave a comment

[Abstract + References] Brain Computer Interfaces in Rehabilitation Medicine – PM&R

Abstract

One innovation currently influencing physical medicine and rehabilitation is brain–computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user’s intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user’s interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.

References

  1. Wolpaw, J.R., Birbaumer, N., Heetderks, W.J. et al, Brain-computer interface technology: A review of the first international meeting. IEEE Trans Rehabil Eng2000;8:164–173.
  2. Kubanek, J., Miller, K., Ojemann, J., Wolpaw, J., Schalk, G. Decoding flexion of individual fingers using electrocorticographic signals in humans. J Neural Engin2009;6:066001.
  3. Arle JE, Shils JL, Malik WQ. Localized stimulation and recording in the spinal cord with microelectrode arrays. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE2012..

  4. Thakor, N.V. Translating the brain-machine interface. Sci Transl Med2013;5 (210ps217-210ps217).
  5. Irani, F., Platek, S.M., Bunce, S., Ruocco, A.C., Chute, D. Functional near infrared spectroscopy (fNIRS): An emerging neuroimaging technology with important applications for the study of brain disorders. Clin Neuropsychologist2007;21:9–37.
  6. Olson, J.D., Wander, J.D., Johnson, L. et al, Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans. Clin Neurophysiol2016;127:277–284.
  7. Olson, J.D., Wander, J.D., Darvas, F. Demonstration of motor-related beta and high gamma brain signals in subdermal electroencephalography recordings. Clin Neurophysiol2017;128:395–396.
  8. Schalk, G., Wolpaw, J.R., McFarland, D.J., Pfurtscheller, G. EEG-based communication: Presence of an error potential. Clin Neurophysiol2000;111:2138–2144.
  9. McFarland, D.J., McCane, L.M., Wolpaw, J.R. EEG-based communication and control: Short-term role of feedback. IEEE Trans Rehabil Eng1998;6:7–11.
  10. Widge, A.S., Moritz, C.T., Matsuoka, Y. Direct neural control of anatomically correct robotic hands. Brain-Computer Interfaces. Springer-VerlagBerlin, Heidelberg2010:105–119.
  11. Fetz, E.E. Volitional control of neural activity: Implications for brain–computer interfaces. J Physiol2007;579:571–579.
  12. Miller, K.J., Schalk, G., Fetz, E.E., Den Nijs, M., Ojemann, J.G., Rao, R.P. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci USA2010;107:4430–4435.
  13. Bouton, C.E., Shaikhouni, A., Annetta, N.V. et al, Restoring cortical control of functional movement in a human with quadriplegia. Nature2016;533:247–250.
  14. Sharma, G., Friedenberg, D.A., Annetta, N. et al, Using an artificial neural bypass to restore cortical control of rhythmic movements in a human with quadriplegia. Sci Rep2016;6:33807.
  15. Collinger, J.L., Boninger, M.L., Bruns, T.M., Curley, K., Wang, W., Weber, D.J. Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. J Rehabil Res Dev2013;50:145–160.
  16. Wodlinger, B., Downey, J.E., Tyler-Kabara, E.C., Schwartz, A.B., Boninger, M.L., Collinger, J.L. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: Difficulties, solutions, and limitations. J Neural Eng2015;12:016011.
  17. Friedenberg, D.A., Schwemmer, M.A., Landgraf, A.J. et al, Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human. Sci Rep2017;7:8386.
  18. Ajiboye, A.B., Willett, F.R., Young, D.R. et al, Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. Lancet2017;389:1821–1830.
  19. Batula, A.M., Mark, J.A., Kim, Y.E., Ayaz, H. Comparison of brain activation during motor imagery and motor movement using fNIRS. Comput Intell Neurosci2017;2017:5491296.
  20. Wu J, Casimo K, Caldwell DJ, Rao RP, Ojemann JG. Electrocorticographic dynamics predict visually guided motor imagery of grasp shaping. Paper presented at: Neural Engineering (NER), 2017 8th International IEEE/EMBS Conference on, 2017..

  21. Flesher, S., Downey, J., Collinger, J. et al, Intracortical microstimulation as a feedback source for brain-computer interface users. in: C. Guger, B. Allison, J. Ushiba (Eds.) Brain-Computer Interface ResearchSpringer International PublishingBasel2017:43–54.
  22. Luan, S., Williams, I., Nikolic, K., Constandinou, T.G. Neuromodulation: Present and emerging methods. Front Neuroeng2014;7:27.
  23. Nardone, R., Höller, Y., Taylor, A. et al, Noninvasive spinal cord stimulation: Technical aspects and therapeutic applications. Neuromodulation2015;18:580–591.
  24. Cronin, J.A., Wu, J., Collins, K.L. et al, Task-specific somatosensory feedback via cortical stimulation in humans. IEEE Trans Haptics2016;9:515–522.
  25. Collins, K.L., Guterstam, A., Cronin, J., Olson, J.D., Ehrsson, H.H., Ojemann, J.G. Ownership of an artificial limb induced by electrical brain stimulation. Proc Natl Acad Sci USA2017;114:166–171.
  26. O’Doherty, J.E., Lebedev, M.A., Ifft, P.J. et al, Active tactile exploration using a brain–machine–brain interface. Nature2011;479:228.
  27. Hiremath, S.V., Tyler-Kabara, E.C., Wheeler, J.J. et al, Human perception of electrical stimulation on the surface of somatosensory cortex. PLoS One2017;12:e0176020.
  28. Venkatakrishnan, A., Francisco, G.E., Contreras-Vidal, J.L. Applications of brain–machine interface systems in stroke recovery and rehabilitation. Curr Phys Med Rehabil Rep2014;2:93–105.
  29. Friedenberg, D.A., Bouton, C.E., Annetta, N.V. et al, Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface. Conf Proc IEEE Eng Med Biol Soc2016;:3084–3087.
  30. Friedenberg DA, Schwemmer M, Skomrock N, et al. Neural decoding algorithm requirements for a take-home brain computer interface. Conf Proc IEEE Eng Med Biol Soc, 2018, in press..

  31. Downey, J.E., Weiss, J.M., Muelling, K. et al, Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. J Neuroeng Rehabil2016;13:28.
  32. Knutson, J.S., Fu, M.J., Sheffler, L.R., Chae, J. Neuromuscular electrical stimulation for motor restoration in hemiplegia. Phys Med Rehabil Clin N Am2015;26:729.
  33. Ragnarsson, K. Functional electrical stimulation after spinal cord injury: Current use, therapeutic effects and future directions. Spinal Cord2008;46:255.
  34. Peckham, P.H., Keith, M.W., Kilgore, K.L. et al, Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: A multicenter study. Arch Phys Med Rehabil2001;82:1380–1388.
  35. Pool, D., Elliott, C., Bear, N. et al, Neuromuscular electrical stimulation-assisted gait increases muscle strength and volume in children with unilateral spastic cerebral palsy. Dev Med Child Neurol2016;58:492–501.
  36. Mulcahey, M.J., Betz, R.R., Kozin, S., Smith, B.T., Hutchinson, D., Lutz, C. Implantaton of the Freehand system during initial rehabilitation using minimally invasive techniques. Spinal Cord2004;42:146–155.
  37. Lauer, R.T., Peckham, P.H., Kilgore, K.L. EEG-based control of a hand grasp neuroprosthesis.Neuroreport1999;10:1767–1771.
  38. Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R. EEG-based neuroprosthesis control: A step towards clinical practice. Neurosci Lett2005;382:169–174.
  39. 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 Lett2003;351:33–36.
  40. Rohm, M., Schneiders, M., Müller, C. et al, Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artif Intell Med2013;59:133–142.
  41. Rupp, R., Rohm, M., Schneiders, M. et al, Think2grasp-bci-controlled neuroprosthesis for the upper extremity. Biomed Tech (Berl)2013; (https://doi.org/10.1515/bmt-2013-4440).
  42. Grimm, F., Walter, A., Spüler, M., Naros, G., Rosenstiel, W., Gharabaghi, A. Hybrid neuroprosthesis for the upper limb: Combining brain-controlled neuromuscular stimulation with a multi-joint arm exoskeleton. Front Neurosci2016;10:367.
  43. Burke, D., Gorman, E., Stokes, D., Lennon, O. An evaluation of neuromuscular electrical stimulation in critical care using the ICF framework: A systematic review and meta-analysis. Clin Respir J2016;10:407–420.
  44. Stein, C., Fritsch, C.G., Robinson, C., Sbruzzi, G., Plentz, R.D.M. Effects of electrical stimulation in spastic muscles after stroke: Systematic review and meta-analysis of randomized controlled trials.Stroke2015;46:2197–2205.
  45. Marquez-Chin, C., Marquis, A., Popovic, M.R. EEG-triggered functional electrical stimulation therapy for restoring upper limb function in chronic stroke with severe hemiplegia. Case Rep Neurol Med2016;2016:9146213.
  46. Rodrıguez, M., Pierre, C., Couve, S. et al, Towards brain–robot interfaces in stroke rehabilitation.PLoS One2011;6:1–17.
  47. Takahashi, M., Takeda, K., Otaka, Y. et al, Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study. J Neuroeng Rehabil2012;9:56.
  48. Knaut, L.A., Subramanian, S.K., McFadyen, B.J., Bourbonnais, D., Levin, M.F. Kinematics of pointing movements made in a virtual versus a physical 3-dimensional environment in healthy and stroke subjects. Arch Phys Med Rehabil2009;90:793–802.
  49. Laver, K.E., Lange, B., George, S., Deutsch, J.E., Saposnik, G., Crotty, M. Virtual reality for stroke rehabilitation. Stroke2018; (STROKEAHA.117.020275).
  50. Collinger, J.L., Wodlinger, B., Downey, J.E. et al, High-performance neuroprosthetic control by an individual with tetraplegia. Lancet2013;381:557–564.
  51. Tidoni, E., Abu-Alqumsan, M., Leonardis, D. et al, Local and remote cooperation with virtual and robotic agents: A P300 BCI study in healthy and people living with spinal cord injury. IEEE Trans Neural Syst Rehabil Eng2017;25:1622–1632.
  52. Colachis, S.C. IV, Bockbrader, M.A., Zhang, M. et al, Dexterous control of seven functional hand movements using cortically-controlled transcutaneous muscle stimulation in a person with tetraplegia. Front Neurosci2018;12:208.
  53. Saleh, S., Fluet, G., Qiu, Q., Merians, A., Adamovich, S.V., Tunik, E. Neural patterns of reorganization after intensive robot-assisted virtual reality therapy and repetitive task practice in patients with chronic stroke. Front Neurol2017;8:452.
  54. Fluet G, Patel J, Qinyin Q, et al. Early versus delayed VR-based hand training in persons with acute stroke. Paper presented at: 2017 International Conference on Virtual Rehabilitation (ICVR); June 19-22, 2017..

  55. Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., Neuper, C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci Lett2000;292:211–214.
  56. Lee, K., Liu, D., Perroud, L., Chavarriaga, R., Millán, JdR. A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers. Robotics Autonomous Systems2017;90:15–23.
  57. Sakurada, T., Kawase, T., Takano, K., Komatsu, T., Kansaku, K. A BMI-based occupational therapy assist suit: Asynchronous control by SSVEP. Front Neurosci2013;7:172.
  58. Chen, G., Chan, C.K., Guo, Z., Yu, H. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng2013;41:343–363.
  59. Louie, D.R., Eng, J.J. Powered robotic exoskeletons in post-stroke rehabilitation of gait: A scoping review. J Neuroeng Rehabil2016;13:53.
  60. Veerbeek, J.M., Langbroek-Amersfoort, A.C., van Wegen, E.E., Meskers, C.G., Kwakkel, G. Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis.Neurorehabil Neural Repair2017;31:107–121.
  61. McConnell, A.C., Moioli, R.C., Brasil, F.L. et al, Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke. J Rehabil Med2017;49:449–460.
  62. Pfurtscheller, G., Allison, B., Bauernfeind, G. et al, The hybrid BCI. Front Neurosci2010;4:30.
  63. Galán, F., Nuttin, M., Lew, E. et al, A brain-actuated wheelchair: Asynchronous and non-invasive brain–computer interfaces for continuous control of robots. Clin Neurophysiol2008;119:2159–2169.
  64. Raspopovic, S., Capogrosso, M., Petrini, F.M. et al, Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci Transl Med2014;6 (222ra219-222ra219).
  65. Flesher, S.N., Collinger, J.L., Foldes, S.T. et al, Intracortical microstimulation of human somatosensory cortex. Sci Transl Med2016;8 (361ra141-361ra141).
  66. Jezernik, S., Colombo, G., Keller, T., Frueh, H., Morari, M. Robotic orthosis lokomat: A rehabilitation and research tool. Neuromodulation2003;6:108–115.
  67. Daly, J.J., Wolpaw, J.R. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol2008;7:1032–1043.
  68. Dobkin, B.H. Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol2007;579:637–642.
  69. Bamdad, M., Zarshenas, H., Auais, M.A. Application of BCI systems in neurorehabilitation: A scoping review. Disabil Rehabil2015;10:355–364.
  70. Sellers, E.W., Ryan, D.B., Hauser, C.K. Noninvasive brain-computer interface enables communication after brainstem stroke. Sci Transl Med2014;6 (257re257-257re257).
  71. Wang, F., He, Y., Qu, J. et al, Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness. J Neural Engin2017;14:046024.
  72. Hochberg, L.R., Serruya, M.D., Friehs, G.M. et al, Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature2006;442:164–171.
  73. Tankus, A., Yeshurun, Y., Flash, T., Fried, I. Encoding of speed and direction of movement in the human supplementary motor area. J Neurosurg2009;110:1304–1316.
  74. Wang Y, Hong B, Gao X, Gao S. Phase synchrony measurement in motor cortex for classifying single-trial EEG during motor imagery. Paper presented at: Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE2006..

  75. Hermes, D., Vansteensel, M.J., Albers, A.M. et al, Functional MRI-based identification of brain areas involved in motor imagery for implantable brain–computer interfaces. J Neural Engin2011;8:025007.
  76. Wang, W., Collinger, J.L., Degenhart, A.D. et al, An electrocorticographic brain interface in an individual with tetraplegia. PLoS One2013;8:e55344.
  77. Wang Y, Makeig S. Predicting intended movement direction using EEG from human posterior parietal cortex. Paper presented at: International Conference on Foundations of Augmented Cognition, 2009..

  78. Klaes, C., Kellis, S., Aflalo, T. et al, Hand shape representations in the human posterior parietal cortex. J Neurosci2015;35:15466–15476.
  79. Broetz, D., Braun, C., Weber, C., Soekadar, S.R., Caria, A., Birbaumer, N. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: A case report.Neurorehabil Neural Repair2010;24:674–679.
  80. Ramos-Murguialday, A., Broetz, D., Rea, M. et al, Brain–machine interface in chronic stroke rehabilitation: A controlled study. Ann Neurol2013;74:100–108.
  81. Ang, K.K., Guan, C., Phua, K.S. 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 Neuroeng2014;7:30.
  82. Heasman, J., Scott, T., Kirkup, L., Flynn, R., Vare, V., Gschwind, C. Control of a hand grasp neuroprosthesis using an electroencephalogram-triggered switch: Demonstration of improvements in performance using wavepacket analysis. Med Biol Eng Comput2002;40:588–593.
  83. Onose, G., Grozea, C., Anghelescu, A. et al, On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up. Spinal Cord2012;50:599.
  84. Kreilinger A, Kaiser V, Rohm M, Rupp R, Müller-Putz GR. BCI and FES training of a spinal cord injured end-user to control a neuroprosthesis. Biomed Tech (Berl), 2013. https://doi.org/10.1515/bmt-2013-4443..

  85. Downey, J.E., Brane, L., Gaunt, R.A., Tyler-Kabara, E.C., Boninger, M.L., Collinger, J.L. Motor cortical activity changes during neuroprosthetic-controlled object interaction. Sci Rep2017;7:16947.
  86. Kennedy, P.R., Bakay, R.A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport1998;9:1707–1711.
  87. Spataro, R., Chella, A., Allison, B. et al, Reaching and grasping a glass of water by locked-In ALS patients through a BCI-controlled humanoid robot. Front Hum Neurosci2017;11:68.
  88. Hochberg, L.R., Bacher, D., Jarosiewicz, B. et al, Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature2012;485:372–375.
  89. Keith, M.W., Peckham, P.H., Thrope, G.B., Buckett, J.R., Stroh, K.C., Menger, V. Functional neuromuscular stimulation neuroprostheses for the tetraplegic hand. Clin Orthop Relat Res1988;233:25–33.
  90. Bockbrader, M., Kortes, M.J., Annetta, N. et al, Implanted brain-computer interface controlling a neuroprosthetic for increasing upper limb function in a human with tetraparesis. PM R2016;8:S242–S243.
  91. Wang, P.T., King, C.E., Chui, L.A., Do, A.H., Nenadic, Z. Self-paced brain–computer interface control of ambulation in a virtual reality environment. J Neural Engin2012;9:056016.
  92. Louie, D.R., Eng, J.J., Lam, T. Gait speed using powered robotic exoskeletons after spinal cord injury: A systematic review and correlational study. J Neuroeng Rehabil2015;12:82.
  93. He Y, Nathan K, Venkatakrishnan A, et al. An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE2014..

  94. Osuagwu, B.C., Wallace, L., Fraser, M., Vuckovic, A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: A randomised pilot study. J Neural Engin2016;13:065002.
  95. Proietti, T., Crocher, V., Roby-Brami, A., Jarrassé, N. Upper-limb robotic exoskeletons for neurorehabilitation: a review on control strategies. IEEE Rev Biomed Engin2016;9:4–14.
  96. Donati, A.R., Shokur, S., Morya, E. et al, Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci Rep2016;6:30383.
  97. Buch, E., Weber, C., Cohen, L.G. et al, Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke2008;39:910–917.
  98. Caria, A., Weber, C., Brötz, D. et al, Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology2011;48:578–582.
  99. Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci2012;6:39.
  100. Várkuti, B., Guan, C., Pan, Y. et al, Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair2013;27:53–62.
  101. Zondervan, D.K., Augsburger, R., Bodenhoefer, B., Friedman, N., Reinkensmeyer, D.J., Cramer, S.C.Machine-based, self-guided home therapy for individuals with severe arm impairment after stroke: A randomized controlled trial. Neurorehabil Neural Repair2015;29:395–406.
  102. Chen, Y.-P., Howard, A.M. Effects of robotic therapy on upper-extremity function in children with cerebral palsy: A systematic review. Dev Neurorehabil2016;19:64–71.
  103. Dolbow, J.D., Mehler, C., Stevens, S.L., Hinojosa, J. Robotic-assisted gait training therapies for pediatric cerebral palsy: A review. J Rehabil Robotics2016;4:14–21.
  104. Hu, X.L., Tong, K.-y., Song, R., Zheng, X.J., Leung, W.W. A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke. Neurorehabil Neural Repair2009;23:837–846.
  105. Young, B.M., Nigogosyan, Z., Walton, L.M. et al, Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng2014;7:26.
  106. Sullivan JL, Bhagat NA, Yozbatiran N, et al. Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial. Paper presented at: Rehabilitation Robotics (ICORR), 2017 International Conference, 2017..

  107. Johansson, R.S., Flanagan, J.R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat Rev Neurosci2009;10:345.
  108. Monzée, J., Lamarre, Y., Smith, A.M. The effects of digital anesthesia on force control using a precision grip. J Neurophysiol2003;89:672–683.
  109. Johansson, R., Hager, C., Backstrom, L. Somatosensory control of precision grip during unpredictable pulling loads III. Impairments during digital anaesthesia. Exp Brain Res1992;89:204–213.
  110. Vaso, A., Adahan, H.-M., Gjika, A. et al, Peripheral nervous system origin of phantom limb pain.Pain2014;155:1384–1391.
  111. Polikandriotis, J.A., Hudak, E.M., Perry, M.W. Minimally invasive surgery through endoscopic laminotomy and foraminotomy for the treatment of lumbar spinal stenosis. J Orthop2013;10:13–16.
  112. Alimi, M., Njoku, I. Jr., Cong, G.-T. et al, Minimally invasive foraminotomy through tubular retractors via a contralateral approach in patients with unilateral radiculopathy. Op Neurosurg2014;10:436–447.
  113. Pirris, S.M., Dhall, S., Mummaneni, P.V., Kanter, A.S. Minimally invasive approach to extraforaminal disc herniations at the lumbosacral junction using an operating microscope: Case series and review of the literature. Neurosurg Focus2008;25:E10.
  114. Cruccu, G., Aziz, T., Garcia-Larrea, L. et al, EFNS guidelines on neurostimulation therapy for neuropathic pain. Eur J Neurol2007;14:952–970.
  115. Liem, L., Russo, M., Huygen, F.J. et al, A multicenter, prospective trial to assess the safety and performance of the spinal modulation dorsal root ganglion neurostimulator system in the treatment of chronic pain. Neuromodulation2013;16:471–482.
  116. Deer, T.R., Grigsby, E., Weiner, R.L., Wilcosky, B., Kramer, J.M. A prospective study of dorsal root ganglion stimulation for the relief of chronic pain. Neuromodulation2013;16:67–72.
  117. Eldabe, S., Burger, K., Moser, H. et al, Dorsal root ganglion (DRG) stimulation in the treatment of phantom limb pain (PLP). Neuromodulation2015;18:610–617.
  118. Lynch, P.J., McJunkin, T., Eross, E., Gooch, S., Maloney, J. Case report: Successful epiradicular peripheral nerve stimulation of the C2 dorsal root ganglion for postherpetic neuralgia.Neuromodulation2011;14:58–61.
  119. Tan, D., Tyler, D., Sweet, J., Miller, J. Intensity modulation: A novel approach to percept control in spinal cord stimulation. Neuromodulation2016;19:254–259.
  120. Tan, D.W., Schiefer, M.A., Keith, M.W., Anderson, J.R., Tyler, J., Tyler, D.J. A neural interface provides long-term stable natural touch perception. Sci Transl Med2014;6 (257ra138-257ra138).
  121. Viswanathan, A., Phan, P.C., Burton, A.W. Use of spinal cord stimulation in the treatment of phantom limb pain: Case series and review of the literature. Pain Practice2010;10:479–484.
  122. Berg, J., Dammann, J. III, Tenore, F. et al, Behavioral demonstration of a somatosensory neuroprosthesis. IEEE Trans Neural Syst Rehabil Eng2013;21:500–507.
  123. Kim, S., Callier, T., Tabot, G.A., Gaunt, R.A., Tenore, F.V., Bensmaia, S.J. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc Natl Acad Sci USA2015;112:15202–15207.
  124. Kim, S., Callier, T., Tabot, G.A., Tenore, F.V., Bensmaia, S.J. Sensitivity to microstimulation of somatosensory cortex distributed over multiple electrodes. Front Syst Neurosci2015;9:47.
  125. Callier, T., Schluter, E.W., Tabot, G.A., Miller, L.E., Tenore, F.V., Bensmaia, S.J. Long-term stability of sensitivity to intracortical microstimulation of somatosensory cortex. J Neural Engin2015;12:056010.
  126. Yuste, R., Goering, S., Agüera y Arcas, B. et al, Four ethical priorities for neurotechnologies and AI.Nature2017;551:159–163.
  127. Klein, E. Informed consent in implantable BCI research: Identifying risks and exploring meaning.Sci Engin Ethics2016;22:1299–1317.

 

via Brain Computer Interfaces in Rehabilitation Medicine – PM&R

, , , , , ,

Leave a comment

[Abstract + References] New Treatment Approaches on the Horizon for Spastic Hemiparesis – PM&R

Abstract

This article presents 2 recent articles that propose novel interventions for treating spastic hemiparesis by changing biological infrastructure. In 18 patients with unilateral spastic arm paralysis due to chronic cerebral injury greater than 5 years’ duration, Zheng et al transferred the C7 nerve from the nonparalyzed side to the side of the arm that was paralyzed. Over a follow-up period of 12 months, they found greater improvement in function and a reduction of spasticity compared to rehabilitation alone. Using functional magnetic resonance imaging, they also found evidence for physiological connectivity between the ipsilateral cerebral hemisphere and the paralyzed hand. In the second article, Raghavan et al examine the concept of stiffness, a common symptom in patients with spastic hemiparesis, as a physical change in the infrastructure of muscle. Raghavan’s non-neural hyaluronan hypothesis postulates that an accumulation of hyaluronan within spastic muscles promotes the development of muscle stiffness in patients with an upper motor neuron syndrome (UMNS). In a case series of 20 patients with spastic hemiparesis, Raghavan et al report that upper limb intramuscular injections of hyaluronidase increased passive and active joint movement and reduced muscle stiffness. Interventions that change biological infrastructure in UMNS is a paradigm on the horizon that bears watching.

References

  1. Zheng, M.X., Hua, X.Y., Feng, J.T. et al, Trial of contralateral seventh cervical nerve transfer for spastic arm paralysis. N Engl J Med2018;378:22–34.
  2. Feigin, V.L., Krishnamurthi, R.V., Parmar, P. et al, Update on the global burden of ischemic and hemorrhagic stroke in 1990–2013: The GBD 2013 study. Neuroepidemiology2015;45:161–176.
  3. Langhorne, P., Coupar, F., Pollock, A. Motor recovery after stroke: A systematic review. Lancet Neurol2009;8:741–754.
  4. Grefkes, C., Ward, N.S. Cortical reorganization after stroke: How much and how functional?.Neuroscientist2014;20:56–70.
  5. Seidler, R.D., Noll, D.C., Thiers, G. Feedforward and feedback processes in motor control.Neuroimage2004;22:1775–1783.
  6. Verstynen, T., Diedrichsen, J., Albert, N., Aparicio, P., Ivry, R.B. Ipsilateral motor cortex activity during unimanual hand movements relates to task complexity. J Neurophysiol2005;93:1209–1222.
  7. Lotze, M., Markert, J., Sauseng, P., Hoppe, J., Plewnia, C., Gerloff, C. The role of multiple contralesional motor areas for complex hand movements after internal capsular lesion. J Neurosci2006;26:6096–6102.
  8. Buetefisch, C.M. Role of the contralesional hemisphere in post-stroke recovery of upper extremity motor function. Front Neurol2015;6:214.
  9. Ziemann, U., Ishii, K., Borgheresi, A. et al, Dissociation of the pathways mediating ipsilateral and contralateral motor-evoked potentials in human hand and arm muscles. J Physiol1999;518:895–906.
  10. Jankowska, E., Edgley, S.A. How can corticospinal tract neurons contribute to ipsilateral movements? A question with implications for recovery of motor functions. Neuroscientist2006;12:67–79.
  11. Currà, A., Trompetto, C., Abbruzzese, G., Berardelli, A. Central effects of botulinum toxin type A: Evidence and supposition. Mov Disord2004;19:S60–S64.
  12. Caleo, M., Antonucci, F., Restani, L., Mazzocchio, R. A reappraisal of the central effects of botulinum neurotoxin type A: By what mechanism?. J Neurochem2009;109:15–24.
  13. Palomar, F.J., Mir, P. Neurophysiological changes after intramuscular injection of botulinum toxin.Clin Neurophysiol2012;123:54–60.
  14. Spinner, R.J., Shin, A.Y., Bishop, A.T. Rewiring to regain function in patients with spastic hemiplegia. N Engl J Med2018;378:83–84.
  15. Raghavan, P., Lub, Y., Mirchandani, M., Stecco, A. Human recombinant hyaluronidase injections for upper limb muscle stiffness in individuals with cerebral injury: A case series. EBioMedicine2016;9:306–313.
  16. Lance, J.W. The control of muscle tone, reflexes, and movement: Robert Wartenberg lecture.Neurology1980;30:1303–1313.
  17. Stecco, A., Stecco, C., Raghavan, P. Peripheral mechanisms of spasticity and treatment implications. Curr Phys Med Rehabil Rep2014;2:121–127.
  18. Piehl-Aulin, K., Laurent, C., Engström-Laurent, A., Hellström, S., Henriksson, J. Hyaluronan in human skeletal muscle of lower extremity: Concentration, distribution and effect of exercise. J Appl Physiol (1985)1991;71:2493–2498.
  19. Springer, J., Schust, S., Peske, K. et al, Catabolic signaling and muscle wasting after acute ischemic stroke in mice: Indication for a stroke-specific sarcopenia. Stroke2014;45:3675–3683.
  20. de Bruin, M., Smeulders, M.J., Kreulen, M., Huijing, P.A., Jaspers, R.T. Intramuscular connective tissue differences in spastic and control muscle: A mechanical and histological study. PLoS One2014;9:e101038.
  21. Al’Qteishat, A., Gaffney, J., Krupinski, J. et al, Changes in hyaluronan production and metabolism following ischaemic stroke in man. Brain2006;129:2158–2176.
  22. Okita, M., Yoshimura, T., Nakano, J., Motomura, M., Eguchi, K. Effects of reduced joint mobility on sarcomere length, collagen fibril arrangement in the endomysium and hyaluronan in rat soleus muscle. J Muscle Res Cell Motil2004;25:159–166.
  23. Matteini, P., Dei, L., Carretti, E., Volpi, N., Goti, A., Pini, R. Structural behavior of highly concentrated hyaluronan. Biomacromolecules2009;10:1516–1522.
  24. Cowman, M.K., Schmidt, T.A., Raghavan, P., Stecco, A. Viscoelastic properties of hyaluronan in physiological conditions. F1000Res2015;4:622.
  25. Purslow, P.P. Muscle fascia and force transmission. J Bodyw Mov Ther2010;14:411–417.
  26. Stecco, C. The Functional Atlas of the Human Fascial System. Churchill LivingstoneLondon2015.
  27. Jenkins, R.H., Thomas, G.J., Williams, J.D., Steadman, R. Myofibroblastic differentiation leads to hyaluronan accumulation through reduced hyaluronan turnover. J Biol Chem2004;279:41453–41460.
  28. Fleuren, J.F., Voerman, G.E., Erren-Wolters, C.V. et al, Stop using the Ashworth Scale for the assessment of spasticity. J Neurol Neurosurg Psychiatry2010;81:46–52.
  29. Phadke, C.P., Balasubramanian, C.K., Holz, A., Davidson, C., Ismail, F., Boulias, C. Adverse clinical effects of botulinum toxin intramuscular injections for spasticity. Can J Neurol Sci2016;43:298–310.

via New Treatment Approaches on the Horizon for Spastic Hemiparesis – PM&R

, , , , , , ,

Leave a comment

[WEB SITE] Physical therapy aids recovery process

October is Physical Therapy Awareness Month. Physical therapy helps those who are experiencing pain, impairment or disability and can significantly improve their quality of life. PT not only treats patients with orthopedic injuries but patients who suffer from neurological issues, multiple sclerosis and strokes.

PT is a team effort, and that’s why it’s important to work with your physical therapist to achieve the best results for you.

PT involves the examination, evaluation and treatment of physical impairments through the use of special exercise, application of heat or cold, and other physical treatments. A physical therapist can work with you to manage or eliminate pain without medication and its side effects. In many cases, PT can be an alternative to surgery. A physical therapist will examine you and develop a plan of care using treatments to help your ability to move, reduce pain, restore function and prevent disability.

Physical therapy helps you achieve maximal function independence by improving and restoring mobility, strength and functional movement; physical therapy is also a regimen known to reduce pain. Many techniques are used in physical therapy including exercise, hot-cold therapy, electrical stimulation, deep tissue ultrasound, massage therapy and traction therapy.

Your physical therapist may start your treatment by having you perform activities and exercises that will help you understand how to improve your gait, without taking a single step. These exercises might include simple activities such as having you stand and lift your leg in place, to more complex strategies such as stepping in place and initiating contact with your heel to the ground before other parts of the foot.

Your physical therapist may employ neuromuscular re-education techniques to activate any inactive muscle groups that might be affecting your gait.

If the gait dysfunction is from significant weakness or paralysis of a ligament, your physical therapist may teach you how to use adaptive equipment such as a brace or splint to help you move.

Your physical therapist may prescribe balance activities for you to perform to help stabilize your walking pattern.

Your physical therapist will help you focus on retraining the way you walk. Because the underlying condition might be vestibular, neurological or muscular, variations in the training exist. Your physical therapist will design the safest and best training for your specific condition.

One system used for early standing training, gait training and high-level balance training is a supported ambulation system. It provides for safe ambulation and support over ground, stairs, parallel bars or equipment.

Boulder City Hospital has recently installed the Biodex FreeStep SAS in its therapy clinic. The Biodex FreeStep SAS is an overhead track and harness system that provides a safe ambulation environment for the therapist and patient. Without the fear of falling, patients can focus more fully on their tasks of gait and balance. Likewise, therapists can focus on assisting, rather than supporting.

Benefits of the FreeStep include minimizing the patient’s fear of falling down, increasing the patient’s confidence, reducing the patient’s inhibition and allowing patients to challenge themselves, and creating a safe environment for balance, strength and gait training.

The FreeStep system can be used with nearly any type of patient who is at risk of falling or just needs some extra confidence to reach their next milestone.

Patients who might benefit from using the FreeStep include those with diagnoses of stroke, multiple sclerosis, brain injury, Parkinson’s disease, spinal cord injury, balance disorder and amputation and anyone at risk of falling.

If you feel you would benefit from therapy services, speak with your physician about a referral.

Boulder City Hospital offers physical, occupational and speech therapy services in inpatient and outpatient settings. Boulder City Hospital accepts most health plans and Medicare for qualified services.

Remember, if your physician prescribes therapy, you have the right to choose your therapy provider.

Boulder City Hospital’s licensed rehabilitation staff will help you achieve your highest possible level of independence and improve your quality of life. Contact the therapy clinic by calling 702-698-8333.

To Your Health is provided by the staff of Boulder City Hospital. For more information, call 702-293-4111, ext. 576, or visit bouldercityhospital.org.

 

via Physical therapy aids recovery process – Boulder City Review

, , , , , , , ,

Leave a comment

[Abstract] Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances – PM&R

Abstract

Recent technologic advancements have enabled the creation of portable, low-cost, and unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation. The application of wearable sensors to track movement has emerged as a promising paradigm to enhance the care provided to patients with neurologic or musculoskeletal conditions. These sensors enable quantification of motor behavior across disparate patient populations and emerging research shows their potential for identifying motor biomarkers, differentiating between restitution and compensation motor recovery mechanisms, remote monitoring, telerehabilitation, and robotics. Moreover, the big data recorded across these applications serve as a pathway to personalized and precision medicine. This article presents state-of-the-art and next-generation wearable movement sensors, ranging from inertial measurement units to soft sensors. An overview of clinical applications is presented across a wide spectrum of conditions that have potential to benefit from wearable sensors, including stroke, movement disorders, knee osteoarthritis, and running injuries. Complementary applications enabled by next-generation sensors that will enable point-of-care monitoring of neural activity and muscle dynamics during movement also are discussed.

 

via Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances – PM&R

, , , , , , , , ,

Leave a comment

[Abstract + References] Transcranial Direct Current Stimulation for Poststroke Motor Recovery: Challenges and Opportunities – PM&R

Abstract

There has been a renewed research interest in transcranial direct current stimulation (tDCS) as an adjunctive tool for poststroke motor recovery as it has a neuro-modulatory effect on the human cortex. However, there are barriers towards its successful application in motor recovery as several scientific issues remain unresolved, including device-related issues (ie, dose-response relationship, safety and tolerability concerns, interhemispheric imbalance model, and choice of montage) and clinical trial-related issues (ie, patient selection, timing of study, and choice of outcomes). This narrative review examines and discusses the existing challenges in using tDCS as a brain modulation tool in facilitating recovery after stroke. Potential solutions pertinent to using tDCS with the goal of harnessing the brains plasticity are proposed.

References

  1. Kreisel, S.H., Bazner, H., Hennerici, M.G. Pathophysiology of stroke rehabilitation: Temporal aspects of neuro-functional recovery. Cerebrovasc Dis2006;21:6–17.
  2. Nitsche, M.A., Paulus, W. Sustained excitability elevations induced by transcranial DC motor cortex stimulation in humans. Neurology2001;57:1899–1901.
  3. Fritsch, B., Reis, J., Martinowich, K. et al, Direct current stimulation promotes BDNF-dependent synaptic plasticity: Potential implications for motor learning. Neuron2010;66:198–204.
  4. Schlaug, G., Renga, V., Nair, D. Transcranial direct current stimulation in stroke recovery. Arch Neurol2008;65:1571–1576.
  5. Brunoni, A.R., Nitsche, M.A., Bolognini, N. et al, Clinical research with transcranial direct current stimulation (TDCS): Challenges and future directions. Brain Stimul2012;5:175–195.
  6. Fregni, F., Nitsche, M., Loo, C. et al, Regulatory considerations for the clinical and research use of transcranial direct current stimulation (TDCS): Review and recommendations from an expert panel.Clin Res Regul Aff2015;32:22–35.
  7. Feng, W.W., Bowden, M.G., Kautz, S. Review of transcranial direct current stimulation in poststroke recovery. Top Stroke Rehabil2013;20:68–77.
  8. Ferbert, A., Priori, A., Rothwell, J.C., Day, B.L., Colebatch, J.G., Marsden, C.D. Interhemispheric inhibition of the human motor cortex. J Physiol1992;453:525–546.
  9. Di Lazzaro, V., Oliviero, A., Profice, P. et al, Direct demonstration of interhemispheric inhibition of the human motor cortex produced by transcranial magnetic stimulation. Exp Brain Res1999;124:520–524.
  10. Stinear, C.M., Petoe, M.A., Byblow, W.D. Primary motor cortex excitability during recovery after stroke: Implications for neuromodulation. Brain Stimul2015;8:1183–1190.
  11. McDonnell, M.N., Stinear, C.M. TMS measures of motor cortex function after stroke: A meta-analysis. Brain Stimul2017;10:721–734.
  12. Wu, D., Qian, L., Zorowitz, R.D., Zhang, L., Qu, Y., Yuan, Y. Effects on decreasing upper-limb poststroke muscle tone using transcranial direct current stimulation: A randomized sham-controlled study. Arch Phys Med Rehabil2013;94:1–8.
  13. Waters, S., Wiestler, T., Diedrichsen, J. Cooperation not competition: Bihemispheric tdcs and fmri show role for ipsilateral hemisphere in motor learning. J Neurosci2017;37:7500–7512.
  14. Truong, D.Q., Huber, M., Xie, X. et al, Clinician accessible tools for gui computational models of transcranial electrical stimulation: Bonsai and spheres. Brain Stimul2014;7:521–524.
  15. Saturnino, G.B., Antunes, A., Thielscher, A. On the importance of electrode parameters for shaping electric field patterns generated by TDCS. NeuroImage2015;120:25–35.
  16. Vines, B.W., Cerruti, C., Schlaug, G. Dual-hemisphere TDCS facilitates greater improvements for healthy subjects’ non-dominant hand compared to uni-hemisphere stimulation. BMC Neurosci2008;9:103.
  17. Chi, R.P., Fregni, F., Snyder, A.W. Visual memory improved by non-invasive brain stimulation. Brain Res2010;1353:168–175.
  18. Chhatbar, P.Y., Ramakrishnan, V., Kautz, S., George, M.S., Adams, R.J., Feng, W. Transcranial direct current stimulation post-stroke upper extremity motor recovery studies exhibit a dose-response relationship. Brain Stimul2016;9:16–26.
  19. Nitsche, M.A., Paulus, W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol2000;527:633–639.
  20. Bastani, A., Jaberzadeh, S. Differential modulation of corticospinal excitability by different current densities of anodal transcranial direct current stimulation. PLoS One2013;8:e72254.
  21. Bastani, A., Jaberzadeh, S. A-TDCS differential modulation of corticospinal excitability: The effects of electrode size. Brain Stimul2013;6:932–937.
  22. Liebetanz, D., Koch, R., Mayenfels, S., Konig, F., Paulus, W., Nitsche, M.A. Safety limits of cathodal transcranial direct current stimulation in rats. Clin Neurophysiol2009;120:1161–1167.
  23. Chhatbar, P.Y., George, M.S., Kautz, S.A., Feng, W. Quantitative reassessment of safety limits of tdcs for two animal studies. Brain Stimul2017;10:1011–1012.
  24. Chhatbar, P.Y., George, M.S., Kautz, S.A., Feng, W. Charge density, not current density, is a more comprehensive safety measure of transcranial direct current stimulation. Brain Behav Immun2017;66:414–415.
  25. Palm, U., Keeser, D., Schiller, C. et al, Skin lesions after treatment with transcranial direct current stimulation (TDCS). Brain Stimul2008;1:386–387.
  26. Frank, E., Wilfurth, S., Landgrebe, M., Eichhammer, P., Hajak, G., Langguth, B. Anodal skin lesions after treatment with transcranial direct current stimulation. Brain Stimul2010;3:58–59.
  27. Wang, J., Wei, Y., Wen, J., Li, X. Skin burn after single session of transcranial direct current stimulation (TDCS). Brain Stimul2015;8:165–166.
  28. Minhas, P., Datta, A., Bikson, M. Cutaneous perception during TDCS: Role of electrode shape and sponge salinity. Clin Neurophysiol2011;122:637–638.
  29. Chhatbar, P.Y., Chen, R., Deardorff, R. et al, Safety and tolerability of transcranial direct current stimulation to stroke patients—a phase I current escalation study. Brain Stimul2017;10:553–559.
  30. Kessler, S.K., Minhas, P., Woods, A.J., Rosen, A., Gorman, C., Bikson, M. Dosage considerations for transcranial direct current stimulation in children: A computational modeling study. PLoS One2013;8:e76112.
  31. Truong, D.Q., Magerowski, G., Blackburn, G.L., Bikson, M., Alonso-Alonso, M. Computational modeling of transcranial direct current stimulation (TDCS) in obesity: Impact of head fat and dose guidelines.Neuroimage Clin2013;2:759–766.
  32. Datta, A., Bikson, M., Fregni, F. Transcranial direct current stimulation in patients with skull defects and skull plates: High-resolution computational FEM study of factors altering cortical current flow.Neuroimage2010;52:1268–1278.
  33. Datta, A., Baker, J.M., Bikson, M., Fridriksson, J. Individualized model predicts brain current flow during transcranial direct-current stimulation treatment in responsive stroke patient. Brain Stimul2011;4:169–174.
  34. Suh, H.S., Lee, W.H., Kim, T.-S. Influence of anisotropic conductivity in the skull and white matter on transcranial direct current stimulation via an anatomically realistic finite element head model. Phys Med Biol2012;57:6961.
  35. Lee, W., Seo, H., Kim, S., Cho, M., Lee, S., Kim, T.-S. Influence of white matter anisotropy on the effects of transcranial direct current stimulation: A finite element study. in: C.K. Lim, J.C.H. Goh (Eds.)ICBME 2008-13th International Conference on Biomedical EngineeringSpringerHeidelberg2009:460–464.
  36. Metwally, M.K., Han, S.M., Kim, T.S. The effect of tissue anisotropy on the radial and tangential components of the electric field in transcranial direct current stimulation. Med Biol Eng Comput2015;53:1085–1101.
  37. Huang, Y., Liu, A.A., Lafon, B. et al, Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation. Elife2017;6:e18834.
  38. Opitz, A., Falchier, A., Yan, C.G. et al, Spatiotemporal structure of intracranial electric fields induced by transcranial electric stimulation in humans and nonhuman primates. Sci Rep2016;6:31236.
  39. Chhatbar, P.Y., Kautz, S.A., Takacs, I. et al, Evidence of transcranial direct current stimulation-generated electric fields at subthalamic level in human brain in vivo. Brain Stimul2018;11:727–733.
  40. Lindenberg, R., Renga, V., Zhu, L.L., Nair, D., Schlaug, G. Bihemispheric brain stimulation facilitates motor recovery in chronic stroke patients. Neurology2010;75:2176–2184.
  41. Levy, R.M., Harvey, R.L., Kissela, B.M. et al, Epidural electrical stimulation for stroke rehabilitation: Results of the prospective, multicenter, randomized, single-blinded Everest trial. Neurorehabil Neural Repair2016;30:107–119.
  42. Feng, W., Wang, J., Chhatbar, P.Y. et al, Corticospinal tract lesion load—a potential imaging biomarker for stroke motor outcomes. Ann Neurol2015;78:860–870.
  43. Dromerick, A.W., Edwardson, M.A., Edwards, D.F. et al, Critical periods after stroke study: Translating animal stroke recovery experiments into a clinical trial. Front Hum Neurosci2015;9:231.
  44. Jorgensen, H.S., Nakayama, H., Raaschou, H.O., Vive-Larsen, J., Stoier, M., Olsen, T.S. Outcome and time course of recovery in stroke. Part II: Time course of recovery. The Copenhagen Stroke Study.Arch Phys Med Rehabil1995;76:406–412.
  45. Cortes, J.C., Goldsmith, J., Harran, M.D. et al, A short and distinct time window for recovery of arm motor control early after stroke revealed with a global measure of trajectory kinematics. Neurorehabil Neural Repair2017;31:552–560.
  46. Bushnell, C., Bettger, J.P., Cockroft, K.M. et al, Chronic stroke outcome measures for motor function intervention trials: Expert panel recommendations. Circ Cardiovasc Qual Outcomes2015;8:S163–S169.
  47. Viana, R.T., Laurentino, G.E., Souza, R.J. et al, Effects of the addition of transcranial direct current stimulation to virtual reality therapy after stroke: A pilot randomized controlled trial.NeuroRehabilitation2014;34:437–446.
  48. Fusco, A., Assenza, F., Iosa, M. et al, The ineffective role of cathodal tdcs in enhancing the functional motor outcomes in early phase of stroke rehabilitation: An experimental trial. Biomed Res Int2014;2014:547290.
  49. Kim, D.Y., Lim, J.Y., Kang, E.K. et al, Effect of transcranial direct current stimulation on motor recovery in patients with subacute stroke. Am J Phys Med Rehabil2010;89:879–886.
  50. Boggio, P.S., Nunes, A., Rigonatti, S.P., Nitsche, M.A., Pascual-Leone, A., Fregni, F. Repeated sessions of noninvasive brain DC stimulation is associated with motor function improvement in stroke patients. Restor Neurol Neurosci2007;25:123–129.
  51. Bolognini, N., Vallar, G., Casati, C., Latif, L.A., El-Nazer, R., Williams, J. et al, Neurophysiological and behavioral effects of TDCS combined with constraint-induced movement therapy in poststroke patients. Neurorehabil Neural Repair2011;25:819–829.
  52. Hesse, S., Waldner, A., Mehrholz, J., Tomelleri, C., Pohl, M., Werner, C. Combined transcranial direct current stimulation and robot-assisted arm training in subacute stroke patients: An exploratory, randomized multicenter trial. Neurorehabil Neural Repair2011;25:838–846.
  53. Di Lazzaro, V., Dileone, M., Capone, F. et al, Immediate and late modulation of interhemipheric imbalance with bilateral transcranial direct current stimulation in acute stroke. Brain Stimul2014;7:841–848.
  54. Rossi, C., Sallustio, F., Di Legge, S., Stanzione, P., Koch, G. Transcranial direct current stimulation of the affected hemisphere does not accelerate recovery of acute stroke patients. Eur J Neurol2013;20:202–204.
  55. Nair, D.G., Renga, V., Lindenberg, R., Zhu, L., Schlaug, G. Optimizing recovery potential through simultaneous occupational therapy and non-invasive brain-stimulation using tdcs. Restor Neurol Neurosci2011;29:411–420.
  56. Ang, K.K., Guan, C., Phua, K.S. et al, Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation. Arch Phys Med Rehabil2015;96:S79–S87.
  57. Sattler, V., Acket, B., Raposo, N. et al, Anodal tdcs combined with radial nerve stimulation promotes hand motor recovery in the acute phase after ischemic stroke. Neurorehabil Neural Repair2015;29:743–754.
  58. Andrade, S.M., Batista, L.M., Nogueira, L.L. et al, Constraint-induced movement therapy combined with transcranial direct current stimulation over premotor cortex improves motor function in severe stroke: A pilot randomized controlled trial. Rehabil Res Pract2017;2017:6842549.
  59. Figlewski, K., Blicher, J.U., Mortensen, J., Severinsen, K.E., Nielsen, J.F., Andersen, H. Transcranial direct current stimulation potentiates improvements in functional ability in patients with chronic stroke receiving constraint-induced movement therapy. Stroke2017;48:229–232.
  60. Medeiros, L.F., de Souza, I.C.C., Vidor, L.P. et al, Neurobiological effects of transcranial direct current stimulation: A review. Front Psychiatry2012;3:110.

 

via Transcranial Direct Current Stimulation for Poststroke Motor Recovery: Challenges and Opportunities – PM&R

, , , ,

Leave a comment

[Infographic] TRAUMATIC BRAIN INJURY – Brain Injury Symptoms

traumatic-brain-injury-infographic

 

, ,

Leave a comment

[WEB SITE] Attention network plays key role in restoring vision after brain damage – ScienceDaily

New study highlights the role of attention as a component of vision restoration training in hemianopia

Summary:
About one-third of patients who have suffered a stroke end up with low vision, losing up to half of their visual field. This partial blindness was long considered irreversible, but recent studies have shown that vision training after optic nerve and brain damage can help restore or improve vision. A new study reports on key mechanisms of vision restoration: attention.
 
FULL STORY

About one third of patients who have suffered a stroke end up with low vision, losing up to half of their visual field. This partial blindness was long considered irreversible, but recent studies have shown that vision training after optic nerve and brain damage can help restore or improve vision. A new study published in the journal Clinical Neurophysiology reports on key mechanisms of vision restoration: attention.

Hemianopia is a decreased vision or blindness in half the visual field, usually as a consequence of stroke or trauma to the brain. It greatly reduces quality of life, affecting patients’ reading, driving and spatial navigation.

“Knowledge in this field is still rather fragmentary, but recent studies have shown that vision can be partially restored by vision training, which improves the deficient visual field sectors,” explains Prof. Bernhard Sabel, PhD, Director of the Institute of Medical Psychology at Magdeburg University, Germany, co-investigator of the study. “Neuroimaging evidence supports a possible role of attention in this vision restoration.”

The study confirmed this hypothesis by obtaining evidence from functional magnetic resonance imaging (fMRI) that visual training led to functional connectivity reorganization of the brain´s attentional network.

Seven chronic hemianopic patients with lesions of the visual cortex took part in vision rehabilitation training for five weeks. After the pre-tests all received training sessions lasting one and a half hours per day for six days per week for five weeks. Each training session, lasting about 60 minutes, was composed of six blocks with 120 training trials each, during which participants had to respond to specially designed visual stimuli on a computer monitor. The pre- and post-test included perimetry testing, contrast sensitivity testing and fMRI scanning one or two days before and after training, respectively. Each contrast sensitivity test consisted of 420 trials in six blocks. The visual rehabilitation training was performed with one eye open, which was randomly chosen, while the non-trained eye was covered with an opaque eye patch.

After training, the patients had significantly improved visual function at the training location, and fMRI showed that the training led to a strengthening of the cortical attentional network connections between the brain region of the right temporoparietal junction (rTPJ) and the insula and the anterior cingulate cortex (ACC).

“Our MRI results highlight the role of attention and the right TPJ activation as a component of vision restoration training in hemianopia,” notes lead investigator Yifeng Zhou, DSc, of the Hefei National Laboratory for Physical Sciences at Microscale and School of Life Science, University of Science and Technology of China, Hefei, P.R. China, and State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, P.R. China. “However, it is unclear whether the rehabilitation of attentional networks is the direct result of training or the result of the rebalancing of bottom-up sensory streams, which should be investigated in future studies.”

“This discovery that the brain´s attention network is a key mechanism in partially reversing blindness is an exciting advance in the field of restoring vision in the blind, and it opens up new avenues to design new therapies that are even more effective than current methods to help people with low vision or blindness,” concludes Prof. Sabel.

Story Source:

Materials provided by Institute for Medical Psychology, Otto-v.-Guericke University MagdeburgNote: Content may be edited for style and length.


Journal Reference:

  1. Qilin Lu, Xiaoxiao Wang, Lin Li, Bensheng Qiu, Shihui Wei, Bernhard A. Sabel, Yifeng Zhou. Visual rehabilitation training alters attentional networks in hemianopia: An fMRI studyClinical Neurophysiology, 2018; 129 (9): 1832 DOI: 10.1016/j.clinph.2018.05.027

Cite This Page:

Institute for Medical Psychology, Otto-v.-Guericke University Magdeburg. “Attention network plays key role in restoring vision after brain damage: New study highlights the role of attention as a component of vision restoration training in hemianopia.” ScienceDaily. ScienceDaily, 4 September 2018. <www.sciencedaily.com/releases/2018/09/180904114753.htm>.
RELATED STORIES

via Attention network plays key role in restoring vision after brain damage: New study highlights the role of attention as a component of vision restoration training in hemianopia — ScienceDaily

, , , , ,

Leave a comment

[WEB SITE] Different Types of Brain Waves: Delta, Theta, Alpha, Beta, Gamma

Our brain consists of 5 different types of brain waves; Delta, Theta, Alpha, Beta and Gamma brain waves. Each of these of these brain waves has a normal frequency range in which they operate. The table below gives a brief overview of the primary function of these brain waves.

Frequency range

Name

Usually associated with:

> 40 Hz

Gamma waves Higher mental activity, including perception, problem solving, and consciousness

13–39 Hz

Beta waves Active, busy thinking, active processing , active concentration, arousal, and cognition

7–13 Hz

Alpha waves Calm relaxed yet alert state

4–7 Hz

Theta waves Deep meditation /relaxation, REM sleep

< 4 Hz

Delta waves Deep dreamless sleep, loss of body awareness
delta theta alpha beta gamma brain waves

Each type of brainwave controls a variety of states of consciousness ranging from sleep to active thinking. While all brain waves work simultaneously, one brainwave can be more predominant and active than the others. The dominant brainwave will determine your current state of mind. So if you are awake and relaxed you would be considered to be in an “alpha state of mind” because your Alpha brain waves would be the strongest with the highest amplitude.

To recap: each brain wave has a frequency it operates at (Hz). The frequency ranges listed above are the “normal” ranges these brain waves should operate at; however, they can fall out of these ranges. Each brain wave has an amplitude (uV) which determines the strength of the brainwave; this, in turn, determines your active state of mind. All brainwave types can be active at the same time but some will be more active than others having the highest amplitude.

In a perfect healthy brain all your brain waves fall within these normal ranges and you have the correct strong dominant brainwave depending on your state of mind. If this is the case, you are feeling FANTASTIC, waking up feeling energetic, in a completely relaxed state, focused, happy, feeling sharp and clear, essentially feeling good all the time, Congratulations! You are a zen monk! But, let’s get back to reality. This is more or less impossible to achieve. Our brain waves are probably not falling within the correct range– some may be a little too high while others are too low. Everything in our daily lives—from stress, poor diet, lack of exercise, trauma, pollution, the environment, and more– causes our brain waves to become unbalanced. Fortunately, we can use brainwave entrainment tools like Itsu Sync to help rebalance our brain waves.

 

The Effects of Specific Brain Waves

Each different brainwave has a certain effect at a specific frequency. The list below will break down the different brainwave ranges to specific frequencies listing their effects.

Delta Brain Waves (0.5Hz – 4Hz)

how to improve sleep with binaural beats

0.5 Hz – Complete relaxation and headache relief.
0.5-1.5Hz – Natural pain relief through stimulating endorphin release.
0.9 Hz – Euphoric state.
1 Hz – Feeling of well-being; stimulation of pituitary glands to release growth hormones (helps recover from injuries, rejuvenate, and develop muscles).
2 Hz – Nerve regeneration.
2.5 Hz – Further pain and migraine relief from production of endogenous opiates. Natural sedative effect.
1-3 Hz – Restorative sleep and profound relaxation.
3.4Hz – Restful sleep.
3.5 Hz – Feeling of calmness, reducing anger and irritability. Retention of languages.
4Hz – Enkephalin release for natural stress and pain reduction. Improved memory, subconscious learning and problem solving.

 

Theta brain waves (4Hz – 7Hz)

how to relax with binaural beats

4.5 Hz – Brings you into what is referred to as “the Tibetan state of consciousness”, a state of meditation.
4.9 Hz – Induced relaxation, meditation, introspection, and a deeper sleep.
5 Hz – State of unconscious problem solving. Less sleep is needed due to the Theta waves replacing the need for extensive dreaming. Beta endorphin release as a natural pain killer.
5.35 Hz – Deeper breathing, relaxing the lungs.
5.5 Hz – Giving the feeling of intuition, your inner guidance, and “gut feeling”.
5.8 Hz – Reduce fear, absent-mindedness, and dizziness.
6 Hz – Improves long-term memory and motivation; reduces procrastination and unwillingness to work.
4.5 Hz – 6.5 Hz – Waking dreaming (day dreaming) with vivid images.
6.5 Hz – Activates the frontal lobe which controls creativity.
6.2 Hz – 6.7 Hz – Activates Frontal Mid-line Theta that is active when engaged in cognitive activity such as solving math problems, playing Tetris, or other similar types of quick passive problem solving tasks.
6.88 Hz – Effects balance and stability.
3 Hz – 8 Hz – Deep relaxation, meditation, lucid dreaming, increased memory, focus, and creativity.
4 Hz – 7 Hz – Inner peace and emotional healing which lowers mental fatigue.
6 Hz – 10 Hz – Creative visualization, starts at 6Hz and moves up to 10Hz.

 

Alpha brain waves (7Hz – 13Hz)

meditation with binaural beats

7.5 Hz – Creative thought is activated for music, art, invention, and problem solving. Overcoming troublesome issues or problems due to ease in finding solutions through re-evaluation. This is a type of inter-awareness of self purpose.
7.83 Hz – The Schumann Resonance. Very grounding as it is the same frequency as the magnetic field of the earth.
7.5 Hz – 8 Hz – For treating addictions, drug, alcohol, food, etc. Gives the person the “satisfied” feeling that they would normally get from their addiction.
8 Hz – 14 Hz – Qi Gong.
8.3 Hz – Heightens clairvoyance around visual images and metal objects.
8 Hz – 8.6Hz – Reduced stress and anxiety.
8 Hz – 10 Hz – Start of “super learning”, your passive ability to learn new information and memorize. It activates creative problem solving and intuitive insights. This is not an active focus on learning but a relaxed state your mind is in absorbing without active concentration.
9 Hz – Brings awareness of body imbalances.
10 Hz – Increased serotonin release bringing mood elevation, arousal; relives headaches and stimulates the body. Will bring clarity to the mind and give subconscious correlation.
10 Hz – 12 Hz – Improves the mind, body connection.
10.5 Hz – Lowers blood pressure. Associated with the Heart Chakra which is related to the Thymus, heart, blood, and circulatory system.
10.6 Hz – Relaxed and alert.
11 Hz – Brings you to a relaxed yet awake state. Can be a lucid state (day dreaming) but not from tiredness. Thoughts and emotions may pleasantly drift through your mind bringing calm. It can be a bridge between the conscious mind and the unconscious mind. Gives stress reduction.
12 Hz – Gives mental stability.
12 Hz – 14 Hz – Improves learning by absorbing information when you plan to think about it later.
12.3 Hz – Improves visualization.

 

Beta brain waves (13Hz – 39Hz) & Gamma brain waves (40Hz+)

how to improve studying with binaural beats

13 Hz – 27 Hz – Focus with attention toward external stimuli. The normal waking consciousness, active thought process, and alert mental activity.
13 Hz – 30 Hz – Processing outside data your brain takes in, problem solving and active conscious thinking. A very wakeful state which combats drowsiness.
14 Hz – An awake, alert state. Allows you to focus and concentrate on tasks.
15 Hz – 18 Hz – Increases your mental abilities including focus, alertness, attentiveness and IQ. You are aware of yourself and surroundings and are alert but not agitated.
16 Hz – Stimulates oxygen/-calcium release into cells.
18 Hz – 24 Hz – Euphoria and ecstasy, similar to a runners high with serotonin release around 22Hz.
18+ Hz – A fully awake state with normal alertness. Significant improvements can be seen in memory, reading, spelling, math, and planning.
20Hz – Stimulation of the pineal gland. The frequency can help with tinnitus.
20 Hz – 40 Hz – Ideal meditation frequency for stress release.
31 Hz – Release of growth hormone, can help to develop muscles and recover from injuries (rejuvenation effects).
32Hz – Enhanced vigor and alertness.
33 Hz – Christ consciousness and the Pyramid frequency.
35 Hz – Balance of all chakras.
36 Hz – 44 Hz – The learning frequency range; will help maintain alertness when actively studying or thinking. Related to the prepiriform cortex and amygdala. Coordinates processing of information in different areas of the brain simultaneously.
38 Hz – Endorphin release.
40 Hz – Having efficient 40Hz activity creates a good memory also dominant in problem solving. Important for information and high-level information processing. A lack of this frequency can create learning disabilities.
40 Hz – 60 Hz – Can stimulate the release of beta-endorphins and give anxiolytic effects.
55 Hz – Tantric yoga which stimulates the “kundalini”.
62 Hz – Feeling of physical vigor.
72 Hz – The emotional spectrum.
40+ Hz – The higher gamma frequencies are less specific occurring over larger ranges and are mostly related to intelligence, problem solving, focus, memorization, concentration, etc. They can be looked at as an enhancement for a lot of the benefits of the beta frequencies .

via Different Types of Brain Waves: Delta, Theta, Alpha, Beta, Gamma

, , , , ,

Leave a comment

%d bloggers like this: