The DOI matches its Crossref record; 126 of the 126 references it cites were found in a registry or an index.
Brain–machine interfaces in neurorehabilitation of stroke
Open accessAbstract
Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.
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Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T., & Murray, C. J. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68770-9 · Cited by 5,630
Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., Moran, A. E., Sacco, R. L., Anderson, L., Truelsen, T., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61953-4 · Cited by 3,899
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
Langhorne, P., Bernhardt, J., & Kwakkel, G. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60325-5 · Cited by 2,726
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719
High-performance neuroprosthetic control by an individual with tetraplegia
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013) · The Lancet · doi:10.1016/s0140-6736(12)61816-9 · Cited by 1,848
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761
Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks
Pfurtscheller, G., Brunner, C., Schlögl, A., & Lopes da Silva, F. H. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.003 · Cited by 1,587
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
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Hatem, S. M., Saussez, G., della Faille, M., Prist, V., Zhang, X., Dispa, D., & Bleyenheuft, Y. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00442 · Cited by 918
BCI for stroke rehabilitation: motor and beyond
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Frolov, A. A., Mokienko, O., Lyukmanov, R., Biryukova, E., Kotov, S., Turbina, L., Nadareyshvily, G., & Bushkova, Y. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00400 · Cited by 396
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296
Metadata
- Authors
- Soekadar, S. R., Birbaumer, N., Slutzky, M. W., & Cohen, L. G.
- Venue
- Neurobiology of Disease
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 83
- Pages
- 172-179
- ISSN
- 0969-9961 (ISSN-L)
- OpenAlex
- W1991557943
- Cited by
- 335
- References
- 126
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
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