Literature DB >> 30609208

Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review.

Raquel Carvalho1,2, Nuno Dias2,3,4, João José Cerqueira2,3.   

Abstract

BACKGROUND: Technologies such as brain-computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy.
OBJECTIVE: The aim of this systematic review was to provide a status report regarding advances in brain-computer interface, focusing in particular in upper limb motor recovery.
METHODS: The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain-computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale.
RESULTS: From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non-invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl-Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies.
CONCLUSIONS: Neurofeedback training with brain-computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long-term efficacy.
Copyright © 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  arm; brain-computer interface; hemiplegia; neurofeedback

Mesh:

Year:  2019        PMID: 30609208     DOI: 10.1002/pri.1764

Source DB:  PubMed          Journal:  Physiother Res Int        ISSN: 1358-2267


  12 in total

1.  BCI-FES With Multimodal Feedback for Motor Recovery Poststroke.

Authors:  Alexander B Remsik; Peter L E van Kan; Shawna Gloe; Klevest Gjini; Leroy Williams; Veena Nair; Kristin Caldera; Justin C Williams; Vivek Prabhakaran
Journal:  Front Hum Neurosci       Date:  2022-07-06       Impact factor: 3.473

2.  Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology.

Authors:  Andrew Y Paek; Justin A Brantley; Barbara J Evans; Jose L Contreras-Vidal
Journal:  IEEE Syst J       Date:  2020-12-18       Impact factor: 4.802

3.  EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery.

Authors:  J Lanzone; M A Colombo; S Sarasso; F Zappasodi; M Rosanova; M Massimini; V Di Lazzaro; G Assenza
Journal:  Clin Neurophysiol       Date:  2022-03-08       Impact factor: 4.861

4.  Effectiveness of interventions to improve hand motor function in individuals with moderate to severe stroke: a systematic review protocol.

Authors:  Hewei Wang; Ray Arceo; Shugeng Chen; Li Ding; Jie Jia; Jun Yao
Journal:  BMJ Open       Date:  2019-09-27       Impact factor: 2.692

Review 5.  Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies.

Authors:  Léa Pillette; Fabien Lotte; Bernard N'Kaoua; Pierre-Alain Joseph; Camille Jeunet; Bertrand Glize
Journal:  Neuroimage Clin       Date:  2020-09-15       Impact factor: 4.881

6.  Effectiveness and safety of brain-computer interface technology in the treatment of poststroke motor disorders: a protocol for systematic review and meta-analysis.

Authors:  Xiaolin Zhang; Di Cao; Junnan Liu; Qi Zhang; Mingjun Liu
Journal:  BMJ Open       Date:  2021-01-28       Impact factor: 2.692

7.  Feasibility and Safety of Bilateral Hybrid EEG/EOG Brain/Neural-Machine Interaction.

Authors:  Marius Nann; Niels Peekhaus; Cornelius Angerhöfer; Surjo R Soekadar
Journal:  Front Hum Neurosci       Date:  2020-12-09       Impact factor: 3.169

Review 8.  Optimal Method of Electrical Stimulation for the Treatment of Upper Limb Dysfunction After Stroke: A Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials.

Authors:  Yuqi Tang; Linjia Wang; Jinxi He; Yipeng Xu; Shijie Huang; Yu Fang
Journal:  Neuropsychiatr Dis Treat       Date:  2021-09-15       Impact factor: 2.570

9.  Effects of Active Upper Limb Orthoses Using Brain-Machine Interfaces for Rehabilitation of Patients With Neurological Disorders: Protocol for a Systematic Review and Meta-Analysis.

Authors:  Emília M G S Silva; Ledycnarf J Holanda; Gustavo K B Coutinho; Fernanda S Andrade; Gabriel I S Nascimento; Danilo A P Nagem; Ricardo A de M Valentim; Ana Raquel Lindquist
Journal:  Front Neurosci       Date:  2021-06-24       Impact factor: 4.677

Review 10.  Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation.

Authors:  Colin Simon; David A E Bolton; Niamh C Kennedy; Surjo R Soekadar; Kathy L Ruddy
Journal:  Front Neurosci       Date:  2021-07-02       Impact factor: 4.677

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