Literature DB >> 33414824

BCI-Based Rehabilitation on the Stroke in Sequela Stage.

Yangyang Miao1, Shugeng Chen2, Xinru Zhang1, Jing Jin1, Ren Xu3, Ian Daly4, Jie Jia2, Xingyu Wang1, Andrzej Cichocki5,6,7, Tzyy-Ping Jung8.   

Abstract

Background: Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method: Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function.
Results: The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study. Conclusions: Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.
Copyright © 2020 Yangyang Miao et al.

Entities:  

Year:  2020        PMID: 33414824      PMCID: PMC7752268          DOI: 10.1155/2020/8882764

Source DB:  PubMed          Journal:  Neural Plast        ISSN: 1687-5443            Impact factor:   3.599


  46 in total

1.  recoveriX: a new BCI-based technology for persons with stroke.

Authors:  D Irimia; N Sabathiel; R Ortner; M Poboroniuc; W Coon; B Z Allison; C Guger
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2016-08

2.  Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest.

Authors:  G Pfurtscheller
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1992-07

3.  An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps.

Authors:  Minqiang Huang; Ian Daly; Jing Jin; Yu Zhang; Xingyu Wang; Andrzej Cichocki
Journal:  Cogn Neurodyn       Date:  2016-01-23       Impact factor: 5.082

4.  An ERP-based BCI with peripheral stimuli: validation with ALS patients.

Authors:  Yangyang Miao; Erwei Yin; Brendan Z Allison; Yu Zhang; Yan Chen; Yi Dong; Xingyu Wang; Dewen Hu; Andrzej Chchocki; Jing Jin
Journal:  Cogn Neurodyn       Date:  2019-06-11       Impact factor: 5.082

5.  Graphical display and statistical evaluation of event-related desynchronization (ERD).

Authors:  G Pfurtscheller
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1977-11

6.  Brain-machine interface in chronic stroke rehabilitation: a controlled study.

Authors:  Ander Ramos-Murguialday; Doris Broetz; Massimiliano Rea; Leonhard Läer; Ozge Yilmaz; Fabricio L Brasil; Giulia Liberati; Marco R Curado; Eliana Garcia-Cossio; Alexandros Vyziotis; Woosang Cho; Manuel Agostini; Ernesto Soares; Surjo Soekadar; Andrea Caria; Leonardo G Cohen; Niels Birbaumer
Journal:  Ann Neurol       Date:  2013-08-07       Impact factor: 10.422

7.  Modulation of proprioceptive feedback during functional electrical stimulation: an fMRI study.

Authors:  Mark Schram Christensen; Michael James Grey
Journal:  Eur J Neurosci       Date:  2013-03-05       Impact factor: 3.386

8.  Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report.

Authors:  Athanasios Vourvopoulos; Carolina Jorge; Rodolfo Abreu; Patrícia Figueiredo; Jean-Claude Fernandes; Sergi Bermúdez I Badia
Journal:  Front Hum Neurosci       Date:  2019-07-11       Impact factor: 3.169

9.  Rehabilitation with poststroke motor recovery: a review with a focus on neural plasticity.

Authors:  Naoyuki Takeuchi; Shin-Ichi Izumi
Journal:  Stroke Res Treat       Date:  2013-04-30

10.  Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device.

Authors:  Brittany Mei Young; Zack Nigogosyan; Alexander Remsik; Léo M Walton; Jie Song; Veena A Nair; Scott W Grogan; Mitchell E Tyler; Dorothy Farrar Edwards; Kristin Caldera; Justin A Sattin; Justin C Williams; Vivek Prabhakaran
Journal:  Front Neuroeng       Date:  2014-07-08
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  9 in total

1.  EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training.

Authors:  Gege Zhan; Shugeng Chen; Yanyun Ji; Ying Xu; Zuoting Song; Junkongshuai Wang; Lan Niu; Jianxiong Bin; Xiaoyang Kang; Jie Jia
Journal:  Front Hum Neurosci       Date:  2022-06-27       Impact factor: 3.473

2.  The Effect of Brain-Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials.

Authors:  Weiwei Yang; Xiaoyun Zhang; Zhenjing Li; Qiongfang Zhang; Chunhua Xue; Yaping Huai
Journal:  Front Neurosci       Date:  2022-02-07       Impact factor: 4.677

3.  Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method.

Authors:  Yao Hou; Zhenghui Gu; Zhu Liang Yu; Xiaofeng Xie; Rongnian Tang; Jinghan Xu; Feifei Qi
Journal:  Front Hum Neurosci       Date:  2022-08-11       Impact factor: 3.473

4.  Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials.

Authors:  Yu-Lei Xie; Yu-Xuan Yang; Hong Jiang; Xing-Yu Duan; Li-Jing Gu; Wu Qing; Bo Zhang; Yin-Xu Wang
Journal:  Front Neurosci       Date:  2022-08-03       Impact factor: 5.152

5.  Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks.

Authors:  Vicente A Lomelin-Ibarra; Andres E Gutierrez-Rodriguez; Jose A Cantoral-Ceballos
Journal:  Sensors (Basel)       Date:  2022-08-15       Impact factor: 3.847

Review 6.  Exploration on neurobiological mechanisms of the central-peripheral-central closed-loop rehabilitation.

Authors:  Jie Jia
Journal:  Front Cell Neurosci       Date:  2022-09-02       Impact factor: 6.147

7.  A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm.

Authors:  Rui Li; Di Liu; Zhijun Li; Jinli Liu; Jincao Zhou; Weiping Liu; Bo Liu; Weiping Fu; Ahmad Bala Alhassan
Journal:  Front Neurosci       Date:  2022-09-13       Impact factor: 5.152

Review 8.  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

Review 9.  Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.

Authors:  Daniela Camargo-Vargas; Mauro Callejas-Cuervo; Stefano Mazzoleni
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

  9 in total

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