Literature DB >> 22208122

Brain-computer interface in stroke: a review of progress.

Stefano Silvoni1, Ander Ramos-Murguialday, Marianna Cavinato, Chiara Volpato, Giulia Cisotto, Andrea Turolla, Francesco Piccione, Niels Birbaumer.   

Abstract

Brain-computer interface (BCI) technology has been used for rehabilitation after stroke and there are a number of reports involving stroke patients in BCI-feedback training. Most publications have demonstrated the efficacy of BCI technology in post-stroke rehabilitation using output devices such as Functional Electrical Stimulation, robot, and orthosis. The aim of this review is to focus on the progress of BCI-based rehabilitation strategies and to underline future challenges. A brief history of clinical BCI-approaches is presented focusing on stroke motor rehabilitation. A context for three approaches of a BCI-based motor rehabilitation program is outlined: the substitutive strategy, classical conditioning and operant conditioning. Furthermore, we include an overview of a pilot study concerning a new neuro-forcefeedback strategy. This pilot study involved healthy participants. Finally we address some challenges for future BCI-based rehabilitation.

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Mesh:

Year:  2011        PMID: 22208122     DOI: 10.1177/155005941104200410

Source DB:  PubMed          Journal:  Clin EEG Neurosci        ISSN: 1550-0594            Impact factor:   1.843


  54 in total

1.  Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface.

Authors:  Natalie Mrachacz-Kersting; Ning Jiang; Andrew James Thomas Stevenson; Imran Khan Niazi; Vladimir Kostic; Aleksandra Pavlovic; Sasa Radovanovic; Milica Djuric-Jovicic; Federica Agosta; Kim Dremstrup; Dario Farina
Journal:  J Neurophysiol       Date:  2015-12-30       Impact factor: 2.714

Review 2.  Robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery from stroke: updates and advances.

Authors:  Michael L Boninger; Lawrence R Wechsler; Joel Stein
Journal:  Am J Phys Med Rehabil       Date:  2014-11       Impact factor: 2.159

3.  A new parameter tuning approach for enhanced motor imagery EEG signal classification.

Authors:  Shiu Kumar; Alok Sharma
Journal:  Med Biol Eng Comput       Date:  2018-04-04       Impact factor: 2.602

Review 4.  New generation emerging technologies for neurorehabilitation and motor assistance.

Authors:  Antonio Frisoli; Massimiliano Solazzi; Claudio Loconsole; Michele Barsotti
Journal:  Acta Myol       Date:  2016-12

5.  Noninvasive brain-computer interface enables communication after brainstem stroke.

Authors:  Eric W Sellers; David B Ryan; Christopher K Hauser
Journal:  Sci Transl Med       Date:  2014-10-08       Impact factor: 17.956

6.  The impact of mind-body awareness training on the early learning of a brain-computer interface.

Authors:  Kaitlin Cassady; Albert You; Alex Doud; Bin He
Journal:  Technology (Singap World Sci)       Date:  2014-09

7.  Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Authors:  A R Marathe; D M Taylor
Journal:  J Neural Eng       Date:  2013-04-23       Impact factor: 5.379

Review 8.  Cortical neuroprosthetics from a clinical perspective.

Authors:  Adelyn P Tsu; Mark J Burish; Jason GodLove; Karunesh Ganguly
Journal:  Neurobiol Dis       Date:  2015-08-05       Impact factor: 5.996

9.  EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application.

Authors:  Long Chen; Bin Gu; Zhongpeng Wang; Lei Zhang; Minpeng Xu; Shuang Liu; Feng He; Dong Ming
Journal:  Front Med       Date:  2021-06-22       Impact factor: 4.592

10.  Neural Population Dynamics Underlying Motor Learning Transfer.

Authors:  Saurabh Vyas; Nir Even-Chen; Sergey D Stavisky; Stephen I Ryu; Paul Nuyujukian; Krishna V Shenoy
Journal:  Neuron       Date:  2018-02-15       Impact factor: 17.173

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