Literature DB >> 21096475

Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback.

Kai Keng Ang1, Cuntai Guan, Karen Sui Geok Chua, Beng Ti Ang, Christopher Kuah, Chuanchu Wang, Kok Soon Phua, Zheng Yang Chin, Haihong Zhang.   

Abstract

This clinical study investigates the ability of hemiparetic stroke patients in operating EEG-based motor imagery brain-computer interface (MI-BCI). It also assesses the efficacy in motor improvements on the stroke-affected upper limb using EEG-based MI-BCI with robotic feedback neurorehabilitation compared to robotic rehabilitation that delivers movement therapy. 54 hemiparetic stroke patients with mean age of 51.8 and baseline Fugl-Meyer Assessment (FMA) 14.9 (out of 66, higher = better) were recruited. Results showed that 48 subjects (89%) operated EEG-based MI-BCI better than at chance level, and their ability to operate EEG-based MI-BCI is not correlated to their baseline FMA (r=0.358). Those subjects who gave consent are randomly assigned to each group (N=11 and 14) for 12 1-hour rehabilitation sessions for 4 weeks. Significant gains in FMA scores were observed in both groups at post-rehabilitation (4.5, 6.2; p=0.032, 0.003) and 2-month post-rehabilitation (5.3, 7.3; p=0.020, 0.013), but no significant differences were observed between groups (p=0.512, 0.550). Hence, this study showed evidences that a majority of hemiparetic stroke patients can operate EEG-based MI-BCI, and that EEG-based MI-BCI with robotic feedback neurorehabilitation is effective in restoring upper extremities motor function in stroke.

Entities:  

Mesh:

Year:  2010        PMID: 21096475     DOI: 10.1109/IEMBS.2010.5626782

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  41 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

2.  Brain-computer interface: current and emerging rehabilitation applications.

Authors:  Janis J Daly; Jane E Huggins
Journal:  Arch Phys Med Rehabil       Date:  2015-03       Impact factor: 3.966

3.  Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy.

Authors:  Amy A Blank; James A French; Ali Utku Pehlivan; Marcia K O'Malley
Journal:  Curr Phys Med Rehabil Rep       Date:  2014-09

4.  Spatiotemporal dynamics of online motor correction processing revealed by high-density electroencephalography.

Authors:  Laura Dipietro; Howard Poizner; Hermano I Krebs
Journal:  J Cogn Neurosci       Date:  2014-02-24       Impact factor: 3.225

5.  Applications of Brain-Machine Interface Systems in Stroke Recovery and Rehabilitation.

Authors:  Anusha Venkatakrishnan; Gerard E Francisco; Jose L Contreras-Vidal
Journal:  Curr Phys Med Rehabil Rep       Date:  2014-06-01

6.  Brain-Machine Interface Induced Morpho-Functional Remodeling of the Neural Motor System in Severe Chronic Stroke.

Authors:  Andrea Caria; Josué Luiz Dalboni da Rocha; Giuseppe Gallitto; Niels Birbaumer; Ranganatha Sitaram; Ander Ramos Murguialday
Journal:  Neurotherapeutics       Date:  2020-04       Impact factor: 7.620

7.  Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke.

Authors:  Ethan R Buch; Amirali Modir Shanechi; Alissa D Fourkas; Cornelia Weber; Niels Birbaumer; Leonardo G Cohen
Journal:  Brain       Date:  2012-01-09       Impact factor: 13.501

Review 8.  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

9.  Crosstalk disrupts the production of motor imagery brain signals in brain-computer interfaces.

Authors:  Phoebe S-H Neo; Terence Mayne; Xiping Fu; Zhiyi Huang; Elizabeth A Franz
Journal:  Health Inf Sci Syst       Date:  2021-03-13

10.  Cortical excitability correlates with the event-related desynchronization during brain-computer interface control.

Authors:  Ian Daly; Caroline Blanchard; Nicholas P Holmes
Journal:  J Neural Eng       Date:  2018-04       Impact factor: 5.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.