Brain-machine interfaces in neurorehabilitation of stroke.

By Surjo Raphael Soekadar, Niels Birbaumer, Marc W. Slutzky, Leonardo G. Cohen. 2015

310 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “We will not attempt to cover all the related works many of which are already presented in the research and survey works we cite [1] [2] [9] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23].”
Rehabilitation of Motor Function after Stroke: A Multiple Systematic Review Focused on Techniques to Stimulate Upper Extremity Recovery (2016, unknown): “Among other complications of stroke the neglect syndrome (Ringman et al., 2004) and spasticity (Sommerfeld et al., 2004; Welmer et al., 2010) affect motor and functional outcomes.”
Progress in Neuroengineering for brain repair: New challenges and open issues (2018, background): “For example, BMIs and brain prostheses were designed with the ultimate goal of restoring a missing function or enhancing a poor one, following brain injury or neurological disorder (Berger et al., 2011, 2012; Hatsopoulos and Donoghue, 2009; Lebedev and Nicolelis, 2006; Mussa-Ivaldi and Miller, 2003;”
Brain–computer interface boosts motor imagery practice during stroke recovery (2015, background): “The re-establishment of an instantaneous and contingent link between the patient’s brain activity—related to motor intent/attempt—and the actual paretic arm movements, supported by a hand orthosis (haptic feedback) in a motor relearning context, has been hypothesized to be an essential component of ”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, unknown): “As non-conventional neurorehabilitation methodology, BCI has been investigated for assisting and aiding motor impairment rehabilitation, such as for patients who suffered and survived the stroke, which is a frequent high disease and generally declines patients mobility afterwards [183].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Thanks to recent clinical studies, however, a third potential future application of this paradigm has been introduced: the use of BMIs as a neurorehabilitation tool (26, 27, 82, 204, 205, 737, 741, 751, 752, 819).”