Literature DB >> 25489973

Brain-machine interfaces in neurorehabilitation of stroke.

Surjo R Soekadar1, Niels Birbaumer2, Marc W Slutzky3, Leonardo G Cohen4.   

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.
Copyright © 2014. Published by Elsevier Inc.

Entities:  

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

Mesh:

Year:  2014        PMID: 25489973     DOI: 10.1016/j.nbd.2014.11.025

Source DB:  PubMed          Journal:  Neurobiol Dis        ISSN: 0969-9961            Impact factor:   5.996


  64 in total

Review 1.  Physiological properties of brain-machine interface input signals.

Authors:  Marc W Slutzky; Robert D Flint
Journal:  J Neurophysiol       Date:  2017-06-14       Impact factor: 2.714

Review 2.  Toward Functional Restoration of the Central Nervous System: A Review of Translational Neuroscience Principles.

Authors:  Max O Krucoff; Jonathan P Miller; Tarun Saxena; Ravi Bellamkonda; Shervin Rahimpour; Stephen C Harward; Shivanand P Lad; Dennis A Turner
Journal:  Neurosurgery       Date:  2019-01-01       Impact factor: 4.654

3.  Continuous decoding of human grasp kinematics using epidural and subdural signals.

Authors:  Robert D Flint; Joshua M Rosenow; Matthew C Tate; Marc W Slutzky
Journal:  J Neural Eng       Date:  2016-11-30       Impact factor: 5.379

4.  Emergent coordination underlying learning to reach to grasp with a brain-machine interface.

Authors:  Mukta Vaidya; Karthikeyan Balasubramanian; Joshua Southerland; Islam Badreldin; Ahmed Eleryan; Kelsey Shattuck; Suchin Gururangan; Marc Slutzky; Leslie Osborne; Andrew Fagg; Karim Oweiss; Nicholas G Hatsopoulos
Journal:  J Neurophysiol       Date:  2017-12-13       Impact factor: 2.714

Review 5.  Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations.

Authors:  Marc W Slutzky
Journal:  Neuroscientist       Date:  2018-05-17       Impact factor: 7.519

Review 6.  A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke.

Authors:  Alexander Remsik; Brittany Young; Rebecca Vermilyea; Laura Kiekhoefer; Jessica Abrams; Samantha Evander Elmore; Paige Schultz; Veena Nair; Dorothy Edwards; Justin Williams; Vivek Prabhakaran
Journal:  Expert Rev Med Devices       Date:  2016-05       Impact factor: 3.166

7.  Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback.

Authors:  Sook-Lei Liew; Mohit Rana; Sonja Cornelsen; Marcos Fortunato de Barros Filho; Niels Birbaumer; Ranganatha Sitaram; Leonardo G Cohen; Surjo R Soekadar
Journal:  Neurorehabil Neural Repair       Date:  2015-12-14       Impact factor: 3.919

8.  Remapping residual coordination for controlling assistive devices and recovering motor functions.

Authors:  Camilla Pierella; Farnaz Abdollahi; Ali Farshchiansadegh; Jessica Pedersen; Elias B Thorp; Ferdinando A Mussa-Ivaldi; Maura Casadio
Journal:  Neuropsychologia       Date:  2015-09-02       Impact factor: 3.139

9.  TechnoBrainBodies-in-Cultures: An Intersectional Case.

Authors:  Sigrid Schmitz
Journal:  Front Sociol       Date:  2021-04-27

Review 10.  Upper Limb Home-Based Robotic Rehabilitation During COVID-19 Outbreak.

Authors:  Hemanth Manjunatha; Shrey Pareek; Sri Sadhan Jujjavarapu; Mostafa Ghobadi; Thenkurussi Kesavadas; Ehsan T Esfahani
Journal:  Front Robot AI       Date:  2021-05-24
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