Literature DB >> 30063219

Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke.

S L Norman1, D J McFarland, A Miner, S C Cramer, E T Wolbrecht, J R Wolpaw, D J Reinkensmeyer.   

Abstract

OBJECTIVE: Brain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke. APPROACH: Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance. MAIN
RESULTS: Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3  ±  7.5 blocks versus 3.5  ±  3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score. SIGNIFICANCE: These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.

Entities:  

Mesh:

Year:  2018        PMID: 30063219      PMCID: PMC6158016          DOI: 10.1088/1741-2552/aad724

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


  58 in total

Review 1.  Event-related EEG/MEG synchronization and desynchronization: basic principles.

Authors:  G Pfurtscheller; F H Lopes da Silva
Journal:  Clin Neurophysiol       Date:  1999-11       Impact factor: 3.708

2.  Beta rebound after different types of motor imagery in man.

Authors:  G Pfurtscheller; C Neuper; C Brunner; F Lopes da Silva
Journal:  Neurosci Lett       Date:  2005-01-08       Impact factor: 3.046

3.  Trained modulation of sensorimotor rhythms can affect reaction time.

Authors:  C B Boulay; W A Sarnacki; J R Wolpaw; D J McFarland
Journal:  Clin Neurophysiol       Date:  2011-03-15       Impact factor: 3.708

4.  Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy.

Authors:  Etienne Combrisson; Karim Jerbi
Journal:  J Neurosci Methods       Date:  2015-01-14       Impact factor: 2.390

5.  Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy.

Authors:  Sumner Norman; Mark Dennison; Eric Wolbrecht; Steven Cramer; Ramesh Srinivasan; David Reinkensmeyer
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2016-02-11       Impact factor: 3.802

6.  A functional MRI study of subjects recovered from hemiparetic stroke.

Authors:  S C Cramer; G Nelles; R R Benson; J D Kaplan; R A Parker; K K Kwong; D N Kennedy; S P Finklestein; B R Rosen
Journal:  Stroke       Date:  1997-12       Impact factor: 7.914

7.  Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report.

Authors:  Doris Broetz; Christoph Braun; Cornelia Weber; Surjo R Soekadar; Andrea Caria; Niels Birbaumer
Journal:  Neurorehabil Neural Repair       Date:  2010-06-02       Impact factor: 3.919

Review 8.  Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review.

Authors:  Gert Kwakkel; Boudewijn J Kollen; Hermano I Krebs
Journal:  Neurorehabil Neural Repair       Date:  2007-09-17       Impact factor: 3.919

9.  Finger strength, individuation, and their interaction: Relationship to hand function and corticospinal tract injury after stroke.

Authors:  Eric T Wolbrecht; Justin B Rowe; Vicky Chan; Morgan L Ingemanson; Steven C Cramer; David J Reinkensmeyer
Journal:  Clin Neurophysiol       Date:  2018-02-03       Impact factor: 3.708

10.  Effects of training pre-movement sensorimotor rhythms on behavioral performance.

Authors:  Dennis J McFarland; William A Sarnacki; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2015-11-03       Impact factor: 5.379

View more
  8 in total

1.  Single-trial decoding of movement intentions using functional ultrasound neuroimaging.

Authors:  Sumner L Norman; David Maresca; Vassilios N Christopoulos; Whitney S Griggs; Charlie Demene; Mickael Tanter; Mikhail G Shapiro; Richard A Andersen
Journal:  Neuron       Date:  2021-03-22       Impact factor: 17.173

2.  Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review.

Authors:  Paul Dominick E Baniqued; Emily C Stanyer; Muhammad Awais; Ali Alazmani; Andrew E Jackson; Mark A Mon-Williams; Faisal Mushtaq; Raymond J Holt
Journal:  J Neuroeng Rehabil       Date:  2021-01-23       Impact factor: 4.262

3.  Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study.

Authors:  Xin Li; Lu Wang; Si Miao; Zan Yue; Zhiming Tang; Liujie Su; Yadan Zheng; Xiangzhen Wu; Shan Wang; Jing Wang; Zulin Dou
Journal:  Front Neurosci       Date:  2022-03-11       Impact factor: 4.677

4.  Robot-Assisted Bimanual Training Improves Hand Function in Patients With Subacute Stroke: A Randomized Controlled Pilot Study.

Authors:  Di Ma; Xin Li; Quan Xu; Fei Yang; Yutong Feng; Wenxu Wang; Jian-Jia Huang; Yu-Cheng Pei; Yu Pan
Journal:  Front Neurol       Date:  2022-07-06       Impact factor: 4.086

Review 5.  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

6.  Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke.

Authors:  Quentin Sanders; Vicky Chan; Renee Augsburger; Steven C Cramer; David J Reinkensmeyer; An H Do
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-04-15       Impact factor: 4.528

7.  Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis.

Authors:  Zhongfei Bai; Kenneth N K Fong; Jack Jiaqi Zhang; Josephine Chan; K H Ting
Journal:  J Neuroeng Rehabil       Date:  2020-04-25       Impact factor: 4.262

8.  Intervention-induced changes in neural connectivity during motor preparation may affect cortical activity at motor execution.

Authors:  Kevin B Wilkins; Julius P A Dewald; Jun Yao
Journal:  Sci Rep       Date:  2020-04-30       Impact factor: 4.379

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.