Hybrid neuroprosthesis for the upper limb: Combining brain-controlled neuromuscular stimulation with a multi-joint arm exoskeleton

Frontiers in Neuroscience

Volumn 10, Issue AUG, 2016, Pages

  Grimm, Florian ^a   Walter, Armin ^a   Spüler, Martin ^a   Naros, Georgios ^a   Rosenstiel, Wolfgang ^a   Gharabaghi, Alireza ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

Brain computer interface; Brain machine interface; Brain robot interface; Functional electrical stimulation; Functional restoration; Motor recovery; Robot assisted rehabilitation; Upper limb assistance

Indexed keywords

ADULT; AGED; ARM EXOSKELETON; ARTICLE; CEREBROVASCULAR ACCIDENT; CLINICAL ARTICLE; ELECTROENCEPHALOGRAM; ELECTROMYOGRAPHY; EVOKED RESPONSE; EXERCISE; FEASIBILITY STUDY; FEMALE; HUMAN; MALE; MEASUREMENT ACCURACY; MULTI JOINT ARM EXOSKELETON; NEUROFEEDBACK; NEUROMUSCULAR ELECTRICAL STIMULATION; NEUROPROSTHESIS; RANGE OF MOTION; UPPER LIMB NEUROPROSTHESIS; VIRTUAL REALITY; WRIST EXTENSION EXERCISE; WRIST FLEXION EXERCISE;

EID: 84988354844     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2016.00367     Document Type: Article

References (63)

1. Ang, K. K., Chua, K. S., Phua, K. S., Wang, C., Chin, Z. Y., Kuah, C. W., et al. (2015). A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin. EEG Neurosci. 46, 310-320. doi: 10.1177/1550059414522229.
2. Bauer, R., Fels, M., Royter, V., Raco, V., and Gharabaghi, A. (2016a). Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation. Clin. Neurophysiol. 127, 3156-3164. doi: 10.1016/j.clinph.2016.06.020.
3. Bauer, R., Fels, M., Vukelic, M., Ziemann, U., and Gharabaghi, A. (2015). Bridging the gap between motor imagery and motor execution with a brain-robot interface. Neuroimage 108, 319-327. doi: 10.1016/j.neuroimage.2014.12.026.
4. Bauer, R., and Gharabaghi, A. (2015a). Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation. Front. Neurosci. 9:36. doi: 10.3389/fnins.2015.00036.
5. Bauer, R., and Gharabaghi, A. (2015b). Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces. Front. Behav. Neurosci. 9:21. doi: 10.3389/fnbeh.2015.00021.
6. Bauer, R., Vukelic, M., and Gharabaghi, A. (2016b). What is the optimal task difficulty for reinforcement learning of brain self-regulation? Clin. Neurophysiol. 127, 3033-3041. doi: 10.1016/j.clinph.2016.06.016.
7. Brauchle, D., Vukelic, M., Bauer, R., and Gharabaghi, A. (2015). Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation. Front. Hum. Neurosci. 9:564. doi: 10.3389/fnhum.2015.00564.
8. Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., et al. (2013). High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557-564. doi: 10.1016/S0140-6736(12)61816-9.
9. De Marchis, C., Monteiro, T. S., Simon-Martinez, C., Conforto, S., and Gharabaghi, A. (2016). Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site. J. Neuroeng. Rehabil. 13, 1. doi: 10.1186/s12984-016-0129-6.
10. Delorme, A., and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9-21. doi: 10.1016/j.jneumeth.2003.10.009.
11. Dobkin, B. H. (2005). Rehabilitation after stroke. N. Engl. J. Med. 352, 1677-1684. doi: 10.1056/NEJMcp043511.
12. Ethier, C., Gallego, J. A., and Miller, L. E. (2015). Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery. Curr. Opin. Neurobiol. 33, 95-102. doi: 10.1016/j.conb.2015.03.007.
13. Feigin, V. L., Barker-Collo, S., McNaughton, H., Brown, P., and Kerse, N. (2008). Long-term neuropsychological and functional outcomes in stroke survivors: current evidence and perspectives for new research. Int. J. Stroke 3, 33-40. doi: 10.1111/j.1747-4949.2008.00177.x.
14. Fels, M., Bauer, R., and Gharabaghi, A. (2015). Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge? J. Neural Eng. 12:046029. doi: 10.1088/1741-2560/12/4/046029.
15. Gharabaghi, A. (2015). Activity-dependent brain stimulation and robot-assisted movements for use-dependent plasticity. Clin. Neurophysiol. 126, 853-854. doi: 10.1016/j.clinph.2014.09.004.
16. Gharabaghi, A., Kraus, D., Leão, M. T., Spüler, M., Walter, A., Bogdan, M., et al. (2014a). Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation. Front. Hum. Neurosci. 8:122. doi: 10.3389/fnhum.2014.00122.
17. Gharabaghi, A., Naros, G., Khademi, F., Jesser, J., Spüler, M., Walter, A., et al. (2014b). Learned self-regulation of the lesioned brain with epidural electrocorticography. Front. Behav. Neurosci. 8:429. doi: 10.3389/fnbeh.2014.00429.
18. Gharabaghi, A., Naros, G., Walter, A., Grimm, F., Schuermeyer, M., Roth, A., et al. (2014c). From assistance towards restoration with an implanted brain-computer interface based on epidural electrocorticography: a single case study. Restor. Neurol. Neurosci. 32, 517-525. doi: 10.3233/RNN-140387.
19. Gharabaghi, A., Naros, G., Walter, A., Roth, A., Bogdan, M., Rosenstiel, W., et al. (2014d). Epidural electrocorticography of phantom hand movement following long-term upper-limb amputation. Front. Hum. Neurosci. 8:285. doi: 10.3389/fnhum.2014.00285.
20. Grimm, F., and Gharabaghi, A. (2016). Closed-loop neuroprosthesis for reach-to-grasp assistance: combining adaptive multi-channel neuromuscular stimulation with a multi-joint arm exoskeleton. Front. Neurosci. 10:284. doi: 10.3389/fnins.2016.00284.
21. Grimm, F., Naros, G., and Gharabaghi, A. (2016). Compensation or restoration: closed-loop feedback of movement quality for assisted reach-to-grasp exercises with a multi-joint arm exoskeleton. Front. Neurosci. 10:280. doi: 10.3389/fnins.2016.00280.
22. Guadagnoli, M. A., and Lee, T. D. (2004). Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J. Mot. Behav. 36, 212-224. doi: 10.3200/JMBR.36.2.212-224.
23. Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., et al. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372-375. doi: 10.1038/nature11076.
24. Hortal, E., Planelles, D., Resquin, F., Climent, J. M., Azorín, J. M., and Pons, J. L. (2015). Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions. J. Neuroeng. Rehabil. 12, 92. doi: 10.1186/s12984-015-0082-9.
25. Housman, S. J., Scott, K. M., and Reinkensmeyer, D. J. (2009). A randomized controlled trial of gravity-supported, computer-enhanced arm exercise for individuals with severe hemiparesis. Neurorehabil. Neural Repair 23, 505-514. doi: 10.1177/1545968308331148.
26. Howlett, O. A., Lannin, N. A., Ada, L., and McKinstry, C. (2015). Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch. Phys. Med. Rehabil. 96, 934-943. doi: 10.1016/j.apmr.2015.01.013.
27. Jørgensen, H. S., Nakayama, H., Raaschou, H. O., and Olsen, T. S. (1999). Stroke. Neurologic and functional recovery the Copenhagen Stroke Study. Phys. Med. Rehabil. Clin. N. Am. 10, 887-906.
28. Klamroth-Marganska, V., Blanco, J., Campen, K., Curt, A., Dietz, V., Ettlin, T., et al. (2014). Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial. Lancet Neurol. 13, 159-166. doi: 10.1016/S1474-4422(13)70305-3.
29. Kraus, D., Naros, G., Bauer, R., Khademi, F., Leão, M. T., Ziemann, U., et al. (2016b). Brain state-dependent transcranial magnetic closed-loop stimulation controlled by sensorimotor desynchronization induces robust increase of corticospinal excitability. Brain Stimul. 9, 415-424. doi: 10.1016/j.brs.2016.02.007.
30. Kraus, D., Naros, G., Bauer, R., Leão, M. T., Ziemann, U., and Gharabaghi, A. (2016a). Brain-robot interface driven plasticity: distributed modulation of corticospinal excitability. Neuroimage 125, 522-532. doi: 10.1016/j.neuroimage.2015.09.074.
31. Kreilinger, A., Kaiser, V., Rohm, M., Rupp, R., and Müller-Putz, G. R. (2013). BCI and FES training of a spinal cord injured end-user to control a neuroprosthesis. Biomed. Tech. (Berl.). doi: 10.1515/bmt-2013-4443. [Epub ahead of print].
32. Leeb, R., Sagha, H., Chavarriaga, R., and Millán Jdel, R. (2011). A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities. J. Neural Eng. 8, 025011. doi: 10.1088/1741-2560/8/2/025011.
33. Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., et al. (2010). Robot-assisted therapy for long-term upper-limb impairment after stroke. N. Engl. J. Med. 362, 1772-1783. doi: 10.1056/NEJMoa0911341.
34. Mann, G., Taylor, P., and Lane, R. (2011). Accelerometer-triggered electrical stimulation for reach and grasp in chronic stroke patients: a pilot study. Neurorehabil. Neural Repair 25, 774-781. doi: 10.1177/1545968310397200.
35. Massie, C. L., Kantak, S. S., Narayanan, P., and Wittenberg, G. F. (2015). Timing of motor cortical stimulation during planar robotic training differentially impacts neuroplasticity in older adults. Clin. Neurophysiol. 126, 1024-1032. doi: 10.1016/j.clinph.2014.06.053.
36. McFarland, D. J., and Wolpaw, J. R. (2008). Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis. J. Neural Eng. 5, 155-162. doi: 10.1088/1741-2560/5/2/006.
37. Meadmore, K. L., Exell, T. A., Hallewell, E., Hughes, A. M., Freeman, C. T., Kutlu, M., et al. (2014). The application of precisely controlled functional electrical stimulation to the shoulder, elbow and wrist for upper limb stroke rehabilitation: a feasibility study. J. Neuroeng. Rehabil. 11:105. doi: 10.1186/1743-0003-11-105.
38. Meadmore, K. L., Hughes, A. M., Freeman, C. T., Cai, Z., Tong, D., Burridge, J. H., et al. (2012). Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke. J. Neuroeng. Rehabil. 9:32. doi: 10.1186/1743-0003-9-32.
39. Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N., et al. (2007). An MEG-based brain-computer interface (BCI). Neuroimage 36, 581-593. doi: 10.1016/j.neuroimage.2007.03.019.
40. Metzger, J. C., Lambercy, O., Califfi, A., Dinacci, D., Petrillo, C., Rossi, P., et al. (2014). Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot. J. Neuroeng. Rehabil. 11:154. doi: 10.1186/1743-0003-11-154.
41. Morone, G., Pisotta, I., Pichiorri, F., Kleih, S., Paolucci, S., Molinari, M., et al. (2015). Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability. Arch. Phys. Med. Rehabil. 96, S71-S78. doi: 10.1016/j.apmr.2014.05.026.
42. Müller, G. R., Neuper, C., Rupp, R., Keinrath, C., Gerner, H. J., and Pfurtscheller, G. (2003). Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man. Neurosci. Lett. 340, 143-147. doi: 10.1016/S0304-3940(03)00019-3.
43. Naros, G., Geyer, M., Koch, S., Mayr, L., Ellinger, T., Grimm, F., et al. (2016b). Enhanced motor learning with bilateral transcranial direct current stimulation: impact of polarity or current flow direction? Clin. Neurophysiol. 127, 2119-2126. doi: 10.1016/j.clinph.2015.12.020.
44. Naros, G., and Gharabaghi, A. (2015). Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke. Front. Hum. Neurosci. 9:391. doi: 10.3389/fnhum.2015.00391.
45. Naros, G., Naros, I., Grimm, F., Ziemann, U., and Gharabaghi, A. (2016a). Reinforcement learning of self-regulated sensorimotor beta-oscillations improves motor performance. Neuroimage 134, 142-152. doi: 10.1016/j.neuroimage.2016.03.016.
46. Oujamaa, L., Relave, I., Froger, J., Mottet, D., and Pelissier, J.-Y. (2009). Rehabilitation of arm function after stroke. Literature review. Annu. Phys. Rehabil. Med. 52, 269-293. doi: 10.1016/j.rehab.2008.10.003.
47. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J., Gerner, H. J., and Rupp, R. (2003). 'Thought'-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 33-36. doi: 10.1016/S0304-3940(03)00947-9.
48. Pichiorri, F., Morone, G., Petti, M., Toppi, J., Pisotta, I., Molinari, M., et al. (2015). Brain-computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 77, 851-865. doi: 10.1002/ana.24390.
49. Reynolds, C., Osuagwu, B. A., and Vuckovic, A. (2015). Influence of motor imagination on cortical activation during functional electrical stimulation. Clin. Neurophysiol. 126, 1360-1369. doi: 10.1016/j.clinph.2014.10.007.
50. Rohm, M., Schneiders, M., Müller, C., Kreilinger, A., Kaiser, V., Müller-Putz, G. R., et al. (2013). Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artif. Intell. Med. 59, 133-142. doi: 10.1016/j.artmed.2013.07.004.
51. Rossiter, H. E., Boudrias, M. H., and Ward, N. S. (2014). Do movement-related beta oscillations change after stroke? J. Neurophysiol. 112, 2053-2058. doi: 10.1152/jn.00345.2014.
52. Royter, V., and Gharabaghi, A. (2016). Brain state-dependent closed-loop modulation of paired associative stimulation controlled by sensorimotor desynchronization. Front. Cell. Neurosci. 10:115. doi: 10.3389/fncel.2016.00115.
53. Spüler, M., Grimm, F., Gharabaghi, A., Bogdan, M., and Rosenstiel, W. (2016). "Comparing methods for decoding movement trajectory from ECoG in chronic stroke patients" in Advances in Neurotechnology, Electronics and Informatics, eds A. R. Londral and P. Encarnação (Rome: Springer International Publishing), 125-139.
54. Spüler, M., Walter, A., Ramos-Murguialday, A., Naros, G., Birbaumer, N., Gharabaghi, A., et al. (2014). Decoding of motor intentions from epidural EcoG recordings in severely paralyzed chronic stroke patients. J. Neural Eng. 11:066008. doi: 10.1088/1741-2560/11/6/066008.
55. Stein, R. B., Everaert, D. G., Roy, F. D., Chong, S., and Soleimani, M. (2013). Facilitation of corticospinal connections in able-bodied people and people with central nervous system disorders using eight interventions. J. Clin. Neurophysiol. 30, 66-78. doi: 10.1097/WNP.0b013e31827ed6bd.
56. Tenore, F. V. G., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., and Thakor, N. V. (2009). Decoding of individuated finger movements using surface electromyography. IEEE Trans. Biomed. Eng. 56, 1427-1434. doi: 10.1109/TBME.2008.2005485.
57. Thrasher, T. S., Zivanovic, V., McIiroy, W., and Popovic, M. R. (2008). Rehabilitation of reaching and grasping function in severe hemiplegic patients using functional electrical stimulation therapy. Neurorehabil. Neural Repair 22, 706-714. doi: 10.1177/1545968308317436.
58. Vuckovic, A., Wallace, L., and Allan, D. B. (2015). Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. J. Neurol. Phys. Ther. 39, 3-14. doi: 10.1097/NPT.0000000000000063.
59. Vukelic, M., and Gharabaghi, A. (2015a). Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality. Neuroimage 111, 1-11. doi: 10.1016/j.neuroimage.2015.01.058.
60. Vukelic, M., and Gharabaghi, A. (2015b). Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks. Front. Behav. Neurosci. 9:181. doi: 10.3389/fnbeh.2015.00181.
61. Vukelic, M., Bauer, R., Naros, G., Naros, I., Braun, C., and Gharabaghi, A. (2014). Lateralized alpha-band cortical networks regulate volitional modulation of beta-band sensorimotor oscillations. Neuroimage 87, 147-153. doi: 10.1016/j.neuroimage.2013.10.003.
62. Walter, A., Ramos Murguialday, A., Spüler, M., Naros, G., Leao, M. T., Gharabaghi, A., et al. (2012). Coupling, B. C. I., and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects. Front. Neural Circuits 6:87. doi: 10.3389/fncir.2012.00087.
63. Wright, Z. A., Rymer, W. Z., and Slutzky, M. W. (2014). Reducing abnormal muscle coactivation after stroke using a myoelectric-computer interface a pilot study. Neurorehabil. Neural Repair 28, 443-451. doi: 10.1177/1545968313517751.