Literature DB >> 24756025

A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke.

Kai Keng Ang1, Karen Sui Geok Chua2, Kok Soon Phua3, Chuanchu Wang3, Zheng Yang Chin3, Christopher Wee Keong Kuah2, Wilson Low4, Cuntai Guan3.   

Abstract

Electroencephalography (EEG)-based motor imagery (MI) brain-computer interface (BCI) technology has the potential to restore motor function by inducing activity-dependent brain plasticity. The purpose of this study was to investigate the efficacy of an EEG-based MI BCI system coupled with MIT-Manus shoulder-elbow robotic feedback (BCI-Manus) for subjects with chronic stroke with upper-limb hemiparesis. In this single-blind, randomized trial, 26 hemiplegic subjects (Fugl-Meyer Assessment of Motor Recovery After Stroke [FMMA] score, 4-40; 16 men; mean age, 51.4 years; mean stroke duration, 297.4 days), prescreened with the ability to use the MI BCI, were randomly allocated to BCI-Manus or Manus therapy, lasting 18 hours over 4 weeks. Efficacy was measured using upper-extremity FMMA scores at weeks 0, 2, 4 and 12. ElEG data from subjects allocated to BCI-Manus were quantified using the revised brain symmetry index (rBSI) and analyzed for correlation with the improvements in FMMA score. Eleven and 15 subjects underwent BCI-Manus and Manus therapy, respectively. One subject in the Manus group dropped out. Mean total FMMA scores at weeks 0, 2, 4, and 12 weeks improved for both groups: 26.3±10.3, 27.4±12.0, 30.8±13.8, and 31.5±13.5 for BCI-Manus and 26.6±18.9, 29.9±20.6, 32.9±21.4, and 33.9±20.2 for Manus, with no intergroup differences (P=.51). More subjects attained further gains in FMMA scores at week 12 from BCI-Manus (7 of 11 [63.6%]) than Manus (5 of 14 [35.7%]). A negative correlation was found between the rBSI and FMMA score improvement (P=.044). BCI-Manus therapy was well tolerated and not associated with adverse events. In conclusion, BCI-Manus therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. Motor gains were comparable to those attained with intensive robotic therapy (1,040 repetitions/session) despite reduced arm exercise repetitions using EEG-based MI-triggered robotic feedback (136 repetitions/session). The correlation of rBSI with motor improvements suggests that the rBSI can be used as a prognostic measure for BCI-based stroke rehabilitation. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Entities:  

Keywords:  EEG; brain-computer interface; motor imagery; rehabilitation; stroke

Mesh:

Year:  2014        PMID: 24756025     DOI: 10.1177/1550059414522229

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


  80 in total

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2.  An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface.

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3.  Quantifying mode mixing and leakage in multivariate empirical mode decomposition and application in motor imagery-based brain-computer interface system.

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Review 4.  Novel Stroke Therapeutics: Unraveling Stroke Pathophysiology and Its Impact on Clinical Treatments.

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Review 5.  Factors affecting post-stroke motor recovery: Implications on neurotherapy after brain injury.

Authors:  Ali Alawieh; Jing Zhao; Wuwei Feng
Journal:  Behav Brain Res       Date:  2016-08-13       Impact factor: 3.332

6.  Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain-Computer Interface Experiments.

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Review 7.  Neurorestoration after stroke.

Authors:  Tej D Azad; Anand Veeravagu; Gary K Steinberg
Journal:  Neurosurg Focus       Date:  2016-05       Impact factor: 4.047

Review 8.  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 9.  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

10.  EEG-Controlled Functional Electrical Stimulation Therapy With Automated Grasp Selection: A Proof-of-Concept Study.

Authors:  Jirapat Likitlersuang; Ryan Koh; Xinyi Gong; Lazar Jovanovic; Isabel Bolivar-Tellería; Matthew Myers; José Zariffa; César Márquez-Chin
Journal:  Top Spinal Cord Inj Rehabil       Date:  2018
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