Literature DB >> 26382749

Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits.

Luz Maria Alonso-Valerdi1, Ricardo Antonio Salido-Ruiz2, Ricardo A Ramirez-Mendoza3.   

Abstract

When the sensory-motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to reintegrate the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Brain–computer interface; Brain–machine interface; Motor imagery; Post-stroke rehabilitation

Mesh:

Year:  2015        PMID: 26382749     DOI: 10.1016/j.neuropsychologia.2015.09.012

Source DB:  PubMed          Journal:  Neuropsychologia        ISSN: 0028-3932            Impact factor:   3.139


  12 in total

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

Authors:  Yang Zheng; Guanghua Xu
Journal:  Med Biol Eng Comput       Date:  2019-02-09       Impact factor: 2.602

2.  Massage Therapy's Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain.

Authors:  Huihui Li; Kai Fan; Junsong Ma; Bo Wang; Xiaohao Qiao; Yan Yan; Wenjing Du; Lei Wang
Journal:  IEEE J Transl Eng Health Med       Date:  2021-02-03       Impact factor: 3.316

3.  Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification.

Authors:  Qingshan She; Haitao Gan; Yuliang Ma; Zhizeng Luo; Tom Potter; Yingchun Zhang
Journal:  Neural Plast       Date:  2016-11-03       Impact factor: 3.599

Review 4.  Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Authors:  Keum-Shik Hong; Muhammad Jawad Khan
Journal:  Front Neurorobot       Date:  2017-07-24       Impact factor: 2.650

5.  Frequency Specific Cortical Dynamics During Motor Imagery Are Influenced by Prior Physical Activity.

Authors:  Selina C Wriessnegger; Clemens Brunner; Gernot R Müller-Putz
Journal:  Front Psychol       Date:  2018-10-25

6.  Brain wave classification using long short-term memory network based OPTICAL predictor.

Authors:  Shiu Kumar; Alok Sharma; Tatsuhiko Tsunoda
Journal:  Sci Rep       Date:  2019-06-24       Impact factor: 4.379

7.  SPECTRA: a tool for enhanced brain wave signal recognition.

Authors:  Tatsuhiko Tsunoda; Alok Sharma; Shiu Kumar
Journal:  BMC Bioinformatics       Date:  2021-06-02       Impact factor: 3.307

8.  Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG-Based Brain Network.

Authors:  Yunfa Fu; Zhouzhou Zhou; Anmin Gong; Qian Qian; Lei Su; Lei Zhao
Journal:  Comput Intell Neurosci       Date:  2021-06-23

9.  Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization.

Authors:  Yingji Qi; Feng Ding; Fangzhou Xu; Jimin Yang
Journal:  Comput Intell Neurosci       Date:  2020-08-01

10.  Low-Cost Robotic Guide Based on a Motor Imagery Brain-Computer Interface for Arm Assisted Rehabilitation.

Authors:  Eduardo Quiles; Ferran Suay; Gemma Candela; Nayibe Chio; Manuel Jiménez; Leandro Álvarez-Kurogi
Journal:  Int J Environ Res Public Health       Date:  2020-01-21       Impact factor: 3.390

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