Literature DB >> 32613947

BCI for stroke rehabilitation: motor and beyond.

Ravikiran Mane1, Tushar Chouhan, Cuntai Guan.   

Abstract

Stroke is one of the leading causes of long-term disability among adults and contributes to major socio-economic burden globally. Stroke frequently results in multifaceted impairments including motor, cognitive and emotion deficits. In recent years, brain-computer interface (BCI)-based therapy has shown promising results for post-stroke motor rehabilitation. In spite of the success received by BCI-based interventions in the motor domain, non-motor impairments are yet to receive similar attention in research and clinical settings. Some preliminary encouraging results in post-stroke cognitive rehabilitation using BCI seem to suggest that it may also hold potential for treating non-motor deficits such as cognitive and emotion impairments. Moreover, past studies have shown an intricate relationship between motor, cognitive and emotion functions which might influence the overall post-stroke rehabilitation outcome. A number of studies highlight the inability of current treatment protocols to account for the implicit interplay between motor, cognitive and emotion functions. This indicates the necessity to explore an all-inclusive treatment plan targeting the synergistic influence of these standalone interventions. This approach may lead to better overall recovery than treating the individual deficits in isolation. In this paper, we review the recent advances in BCI-based post-stroke motor rehabilitation and highlight the potential for the use of BCI systems beyond the motor domain, in particular, in improving cognition and emotion of stroke patients. Building on the current results and findings of studies in individual domains, we next discuss the possibility of a holistic BCI system for motor, cognitive and affect rehabilitation which may synergistically promote restorative neuroplasticity. Such a system would provide an all-encompassing rehabilitation platform, leading to overarching clinical outcomes and transfer of these outcomes to a better quality of living. This is one of the first works to analyse the possibility of targeting cross-domain influence of post-stroke functional recovery enabled by BCI-based rehabilitation.

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Year:  2020        PMID: 32613947     DOI: 10.1088/1741-2552/aba162

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  27 in total

Review 1.  Pathological changes of brain oscillations following ischemic stroke.

Authors:  Yoshimichi Sato; Oliver Schmitt; Zachary Ip; Gratianne Rabiller; Shunsuke Omodaka; Teiji Tominaga; Azadeh Yazdan-Shahmorad; Jialing Liu
Journal:  J Cereb Blood Flow Metab       Date:  2022-06-25       Impact factor: 6.960

2.  BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study.

Authors:  Kai Yuan; Cheng Chen; Xin Wang; Winnie Chiu-Wing Chu; Raymond Kai-Yu Tong
Journal:  Brain Sci       Date:  2021-01-06

3.  Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study.

Authors:  Cathy C Y Lau; Kai Yuan; Patrick C M Wong; Winnie C W Chu; Thomas W Leung; Wan-Wa Wong; Raymond K Y Tong
Journal:  Front Hum Neurosci       Date:  2021-01-20       Impact factor: 3.169

4.  An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation.

Authors:  Lei Cao; Shugeng Chen; Jie Jia; Chunjiang Fan; Haoran Wang; Zhixiong Xu
Journal:  Front Neurosci       Date:  2021-01-28       Impact factor: 4.677

5.  Long-Term Mutual Training for the CYBATHLON BCI Race With a Tetraplegic Pilot: A Case Study on Inter-Session Transfer and Intra-Session Adaptation.

Authors:  Lea Hehenberger; Reinmar J Kobler; Catarina Lopes-Dias; Nitikorn Srisrisawang; Peter Tumfart; John B Uroko; Paul R Torke; Gernot R Müller-Putz
Journal:  Front Hum Neurosci       Date:  2021-02-26       Impact factor: 3.169

6.  A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.

Authors:  Juntao Xue; Feiyue Ren; Xinlin Sun; Miaomiao Yin; Jialing Wu; Chao Ma; Zhongke Gao
Journal:  Neural Plast       Date:  2020-12-07       Impact factor: 3.599

7.  Artifacts in EEG-Based BCI Therapies: Friend or Foe?

Authors:  Eric James McDermott; Philipp Raggam; Sven Kirsch; Paolo Belardinelli; Ulf Ziemann; Christoph Zrenner
Journal:  Sensors (Basel)       Date:  2021-12-24       Impact factor: 3.576

8.  A transfer learning framework based on motor imagery rehabilitation for stroke.

Authors:  Yanan Sun; Dongju Guo; Jiali Xu; Yuandong Wang; Jincheng Li; Han Li; Gege Dong; Fenqi Rong; Fangzhou Xu; Yunjing Miao; Jiancai Leng; Yang Zhang
Journal:  Sci Rep       Date:  2021-10-05       Impact factor: 4.379

9.  Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population.

Authors:  Diego Marcos-Martínez; Víctor Martínez-Cagigal; Eduardo Santamaría-Vázquez; Sergio Pérez-Velasco; Roberto Hornero
Journal:  Entropy (Basel)       Date:  2021-11-25       Impact factor: 2.524

Review 10.  Combination of Stem Cells and Rehabilitation Therapies for Ischemic Stroke.

Authors:  Reed Berlet; Stefan Anthony; Beverly Brooks; Zhen-Jie Wang; Nadia Sadanandan; Alex Shear; Blaise Cozene; Bella Gonzales-Portillo; Blake Parsons; Felipe Esparza Salazar; Alma R Lezama Toledo; Germán Rivera Monroy; Joaquín Vega Gonzales-Portillo; Cesario V Borlongan
Journal:  Biomolecules       Date:  2021-09-06
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