Literature DB >> 32164846

Brain-computer interfaces in neurologic rehabilitation practice.

Floriana Pichiorri1, Donatella Mattia2.   

Abstract

The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.
© 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Brain-computer interface; Cognitive rehabilitation; Electroencephalography; Motor imagery; Motor rehabilitation; Neurofeedback; Neuroplasticity; Spinal cord injury; Stroke; Traumatic brain injury

Mesh:

Year:  2020        PMID: 32164846     DOI: 10.1016/B978-0-444-63934-9.00009-3

Source DB:  PubMed          Journal:  Handb Clin Neurol        ISSN: 0072-9752


  12 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.  Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm.

Authors:  Emma Colamarino; Floriana Pichiorri; Jlenia Toppi; Donatella Mattia; Febo Cincotti
Journal:  Brain Topogr       Date:  2022-01-19       Impact factor: 3.020

3.  Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.

Authors:  Michael J Young; David J Lin; Leigh R Hochberg
Journal:  Semin Neurol       Date:  2021-03-19       Impact factor: 3.212

4.  The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response.

Authors:  Donatella Mattia; Floriana Pichiorri; Emma Colamarino; Marcella Masciullo; Giovanni Morone; Jlenia Toppi; Iolanda Pisotta; Federica Tamburella; Matteo Lorusso; Stefano Paolucci; Maria Puopolo; Febo Cincotti; Marco Molinari
Journal:  BMC Neurol       Date:  2020-06-27       Impact factor: 2.474

5.  Effects of Training with a Brain-Computer Interface-Controlled Robot on Rehabilitation Outcome in Patients with Subacute Stroke: A Randomized Controlled Trial.

Authors:  Chen-Guang Zhao; Fen Ju; Wei Sun; Shan Jiang; Xiao Xi; Hong Wang; Xiao-Long Sun; Min Li; Jun Xie; Kai Zhang; Guang-Hua Xu; Si-Cong Zhang; Xiang Mou; Hua Yuan
Journal:  Neurol Ther       Date:  2022-02-16

Review 6.  Corticospinal Motor Circuit Plasticity After Spinal Cord Injury: Harnessing Neuroplasticity to Improve Functional Outcomes.

Authors:  Syed Faraz Kazim; Christian A Bowers; Chad D Cole; Samantha Varela; Zafar Karimov; Erick Martinez; Jonathan V Ogulnick; Meic H Schmidt
Journal:  Mol Neurobiol       Date:  2021-08-03       Impact factor: 5.590

7.  MEG Source Localization via Deep Learning.

Authors:  Dimitrios Pantazis; Amir Adler
Journal:  Sensors (Basel)       Date:  2021-06-22       Impact factor: 3.576

Review 8.  Why brain-controlled neuroprosthetics matter: mechanisms underlying electrical stimulation of muscles and nerves in rehabilitation.

Authors:  Matija Milosevic; Cesar Marquez-Chin; Kei Masani; Masayuki Hirata; Taishin Nomura; Milos R Popovic; Kimitaka Nakazawa
Journal:  Biomed Eng Online       Date:  2020-11-04       Impact factor: 2.819

9.  Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review.

Authors:  Silvia Orlandi; Sarah C House; Petra Karlsson; Rami Saab; Tom Chau
Journal:  Front Hum Neurosci       Date:  2021-07-14       Impact factor: 3.169

10.  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

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