Literature DB >> 20208465

Feasibility of a new application of noninvasive Brain Computer Interface (BCI): a case study of training for recovery of volitional motor control after stroke.

Janis J Daly1, Roger Cheng, Jean Rogers, Krisanne Litinas, Kenneth Hrovat, Mark Dohring.   

Abstract

BACKGROUND/
PURPOSE: A large proportion of individuals with stroke have persistent deficits for which current interventions have not restored normal motor behavior. Noninvasive brain computer interfaces (BCIs) have potential advantages for restoration of function. There are also potential advantages for combining BCI with functional electrical stimulation (FES). We tested the feasibility of combined BCI + FES for motor learning after stroke. CASE DESCRIPTION: The participant was a 43-year-old woman who was 10 months post-stroke. She was unable to produce isolated movement of any of the digits of her involved hand. With effort she exhibited simultaneous mass hyperextension of metacarpal phalangeal joints of all four fingers and thumb with simultaneous flexion of proximal interphalangeal and distal interphalangeal joints of all fingers. INTERVENTION: Brain signals from the lesioned hemisphere were used to trigger FES for movement practice. The BCI + FES intervention consisted of trials of either attempted finger movement and relax conditions or imagined finger movement and relax conditions. The training was performed three times per week for three weeks (nine sessions total). OUTCOME: : The participant exhibited highly accurate control of brain signal in the first session for attempted movement (97%), imagined movement (83%), and some difficulties with attempted relaxation (65%). By session 6, control of relaxation (deactivation of brain signal) improved to >80%. After nine sessions (three per week) of BCI + FES intervention, the participant demonstrated recovery of volitional isolated index finger extension. DISCUSSION: BCI + FES training for motor learning after stroke was feasible. A highly accurate brain signal control was achieved, and this signal could be reliably used to trigger the FES device for isolated index finger extension. With training, volitional control of isolated finger extension was attained in a small number of sessions. The source of motor recovery could be attributable to BCI, FES, combined BCI + FES, or whole arm or hand motor task practice.

Entities:  

Mesh:

Year:  2009        PMID: 20208465     DOI: 10.1097/NPT.0b013e3181c1fc0b

Source DB:  PubMed          Journal:  J Neurol Phys Ther        ISSN: 1557-0576            Impact factor:   3.649


  73 in total

1.  Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface.

Authors:  Natalie Mrachacz-Kersting; Ning Jiang; Andrew James Thomas Stevenson; Imran Khan Niazi; Vladimir Kostic; Aleksandra Pavlovic; Sasa Radovanovic; Milica Djuric-Jovicic; Federica Agosta; Kim Dremstrup; Dario Farina
Journal:  J Neurophysiol       Date:  2015-12-30       Impact factor: 2.714

2.  Brain-computer interface: current and emerging rehabilitation applications.

Authors:  Janis J Daly; Jane E Huggins
Journal:  Arch Phys Med Rehabil       Date:  2015-03       Impact factor: 3.966

Review 3.  Non-pharmaceutical therapies for stroke: mechanisms and clinical implications.

Authors:  Fan Chen; Zhifeng Qi; Yuming Luo; Taylor Hinchliffe; Guanghong Ding; Ying Xia; Xunming Ji
Journal:  Prog Neurobiol       Date:  2014-01-07       Impact factor: 11.685

4.  Early detection of hand movements from electroencephalograms for stroke therapy applications.

Authors:  A Muralidharan; J Chae; D M Taylor
Journal:  J Neural Eng       Date:  2011-05-27       Impact factor: 5.379

5.  ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance.

Authors:  Surjo R Soekadar; Matthias Witkowski; Jürgen Mellinger; Ander Ramos; Niels Birbaumer; Leonardo G Cohen
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-10       Impact factor: 3.802

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

Authors:  S L Norman; D J McFarland; A Miner; S C Cramer; E T Wolbrecht; J R Wolpaw; D J Reinkensmeyer
Journal:  J Neural Eng       Date:  2018-07-31       Impact factor: 5.379

Review 7.  Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery.

Authors:  C Ethier; J A Gallego; L E Miller
Journal:  Curr Opin Neurobiol       Date:  2015-03-28       Impact factor: 6.627

8.  Novel hybrid brain-computer interface system based on motor imagery and P300.

Authors:  Cili Zuo; Jing Jin; Erwei Yin; Rami Saab; Yangyang Miao; Xingyu Wang; Dewen Hu; Andrzej Cichocki
Journal:  Cogn Neurodyn       Date:  2019-10-21       Impact factor: 5.082

9.  Therapeutic Applications of BCI Technologies.

Authors:  Dennis J McFarland; Janis Daly; Chadwick Boulay; Muhammad Parvaz
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2017-04-10

10.  Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Authors:  A R Marathe; D M Taylor
Journal:  J Neural Eng       Date:  2013-04-23       Impact factor: 5.379

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