Literature DB >> 24109675

Functional recovery in upper limb function in stroke survivors by using brain-computer interface A single case A-B-A-B design.

Takashi Ono, Masahiko Mukaino, Junichi Ushiba.   

Abstract

Resent studies suggest that brain-computer interface (BCI) training for chronic stroke patient is useful to improve their motor function of paretic hand. However, these studies does not show the extent of the contribution of the BCI clearly because they prescribed BCI with other rehabilitation systems, e.g. an orthosis itself, a robotic intervention, or electrical stimulation. We therefore compared neurological effects between interventions with neuromuscular electrical stimulation (NMES) with motor imagery and BCI-driven NMES, employing an ABAB experimental design. In epoch A, the subject received NMES on paretic extensor digitorum communis (EDC). The subject was asked to attempt finger extension simultaneously. In epoch B, the subject received NMES when BCI system detected motor-related electroencephalogram change while attempting motor imagery. Both epochs were carried out for 60 min per day, 5 days per week. As a result, EMG activity of EDC was enhanced by BCI-driven NMES and significant cortico-muscular coherence was observed at the final evaluation. These results indicate that the training by BCI-driven NMES is effective even compared to motor imagery combined with NMES, suggesting the superiority of closed-loop training with BCI-driven NMES to open-loop NMES for chronic stroke patients.

Entities:  

Mesh:

Year:  2013        PMID: 24109675     DOI: 10.1109/EMBC.2013.6609488

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  6 in total

Review 1.  [Neurofeedback-based motor imagery training for rehabilitation after stroke].

Authors:  C Dettmers; N Braun; I Büsching; T Hassa; S Debener; J Liepert
Journal:  Nervenarzt       Date:  2016-10       Impact factor: 1.214

Review 2.  The influence of functional electrical stimulation on hand motor recovery in stroke patients: a review.

Authors:  Fanny Quandt; Friedhelm C Hummel
Journal:  Exp Transl Stroke Med       Date:  2014-08-21

3.  Electroencephalogram-Electromyography Coupling Analysis in Stroke Based on Symbolic Transfer Entropy.

Authors:  Yunyuan Gao; Leilei Ren; Rihui Li; Yingchun Zhang
Journal:  Front Neurol       Date:  2018-01-04       Impact factor: 4.003

4.  Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review.

Authors:  Paul Dominick E Baniqued; Emily C Stanyer; Muhammad Awais; Ali Alazmani; Andrew E Jackson; Mark A Mon-Williams; Faisal Mushtaq; Raymond J Holt
Journal:  J Neuroeng Rehabil       Date:  2021-01-23       Impact factor: 4.262

5.  Application of a Brain-Computer Interface System with Visual and Motor Feedback in Limb and Brain Functional Rehabilitation after Stroke: Case Report.

Authors:  Wen Gao; Zhengzhe Cui; Yang Yu; Jing Mao; Jun Xu; Leilei Ji; Xiuli Kan; Xianshan Shen; Xueming Li; Shiqiang Zhu; Yongfeng Hong
Journal:  Brain Sci       Date:  2022-08-16

Review 6.  Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review.

Authors:  Ahad Behboodi; Walker A Lee; Victoria S Hinchberger; Diane L Damiano
Journal:  J Neuroeng Rehabil       Date:  2022-09-28       Impact factor: 5.208

  6 in total

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