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Neurobiol Dis. 2015 Nov;83:172-9. doi: 10.1016/j.nbd.2014.11.025. Epub 2014 Dec 7.

Brain-machine interfaces in neurorehabilitation of stroke.

Author information

1
Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Tübingen, Germany; Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany. Electronic address: surjo.soekadar@uni-tuebingen.de.
2
Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany; Ospedale San Camillo, IRCCS, Venice, Italy. Electronic address: niels.birbaumer@uni-tuebingen.de.
3
Northwestern University, Feinberg School of Medicine, Chicago, USA. Electronic address: mslutzky@northwestern.edu.
4
Human Cortical Physiology and Neurorehabilitation Section, NINDS, NIH, Bethesda, MD, USA. Electronic address: cohenl@ninds.nih.gov.

Abstract

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.

KEYWORDS:

Assistive technology; Brain stimulation; Brain–machine interface (BMI); Neurorehabilitation; Robotics; Stroke

PMID:
25489973
DOI:
10.1016/j.nbd.2014.11.025
[Indexed for MEDLINE]
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