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Literature DB >> 33742433

Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.

Michael J Young¹, David J Lin^1,2,3, Leigh R Hochberg^1,2,3.

Abstract

Recent advances in brain-computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain-computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain-computer interfaces for persons with neurologic disease. Thieme. All rights reserved.

Entities: Chemical

Mesh：

Year: 2021 PMID： 33742433 PMCID： PMC8768507 DOI： 10.1055/s-0041-1725137

Source DB: PubMed Journal: Semin Neurol ISSN： 0271-8235 Impact factor: 3.212

126 in total

1. Protecting Privacy of Users in Brain-Computer Interface Applications.

Authors: Anisha Agarwal; Rafael Dowsley; Nicholas D McKinney; Dongrui Wu; Chin-Teng Lin; Martine De Cock; Anderson C A Nascimento
Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2019-07-04 Impact factor: 3.802

2. Neuroethics Guiding Principles for the NIH BRAIN Initiative.

Authors: Henry T Greely; Christine Grady; Khara M Ramos; Winston Chiong; James Eberwine; Nita A Farahany; L Syd M Johnson; Bradley T Hyman; Steven E Hyman; Karen S Rommelfanger; Elba E Serrano
Journal: J Neurosci Date: 2018-12-12 Impact factor: 6.167

Review 3. Closed-loop brain training: the science of neurofeedback.

Authors: Ranganatha Sitaram; Tomas Ros; Luke Stoeckel; Sven Haller; Frank Scharnowski; Jarrod Lewis-Peacock; Nikolaus Weiskopf; Maria Laura Blefari; Mohit Rana; Ethan Oblak; Niels Birbaumer; James Sulzer
Journal: Nat Rev Neurosci Date: 2016-12-22 Impact factor: 34.870

4. Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

Authors: Antonio Maria Chiarelli; Pierpaolo Croce; Arcangelo Merla; Filippo Zappasodi
Journal: J Neural Eng Date: 2018-02-15 Impact factor: 5.379

5. Targeting recovery: priorities of the spinal cord-injured population.

Authors: Kim D Anderson
Journal: J Neurotrauma Date: 2004-10 Impact factor: 5.269

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

Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.

1. Protecting Privacy of Users in Brain-Computer Interface Applications.

2. Neuroethics Guiding Principles for the NIH BRAIN Initiative.

Review 3. Closed-loop brain training: the science of neurofeedback.

4. Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

5. Targeting recovery: priorities of the spinal cord-injured population.

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

7. Performance assessment of a brain-computer interface driven hand orthosis.

8. Long-Term Surface Electrode Impedance Recordings Associated with Gliosis for a Closed-Loop Neurostimulation Device.

9. Embodiment and Estrangement: Results from a First-in-Human "Intelligent BCI" Trial.

10. Clinician awareness of brain computer interfaces: a Canadian national survey.

1. Ethical Considerations in Clinical Trials for Disorders of Consciousness.

2. The neuroethics of disorders of consciousness: a brief history of evolving ideas.