Literature DB >> 22325364

Brain-computer interfaces in medicine.

Jerry J Shih1, Dean J Krusienski, Jonathan R Wolpaw.   

Abstract

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.
Copyright © 2012 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2012        PMID: 22325364      PMCID: PMC3497935          DOI: 10.1016/j.mayocp.2011.12.008

Source DB:  PubMed          Journal:  Mayo Clin Proc        ISSN: 0025-6196            Impact factor:   7.616


  107 in total

1.  Neurophysiologic correlates of fMRI in human motor cortex.

Authors:  Dora Hermes; Kai J Miller; Mariska J Vansteensel; Erik J Aarnoutse; Frans S S Leijten; Nick F Ramsey
Journal:  Hum Brain Mapp       Date:  2011-06-20       Impact factor: 5.038

2.  A high-performance brain-computer interface.

Authors:  Gopal Santhanam; Stephen I Ryu; Byron M Yu; Afsheen Afshar; Krishna V Shenoy
Journal:  Nature       Date:  2006-07-13       Impact factor: 49.962

3.  Electroencephalographic (EEG) control of three-dimensional movement.

Authors:  Dennis J McFarland; William A Sarnacki; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2010-05-11       Impact factor: 5.379

4.  Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus.

Authors:  D J Krusienski; J J Shih
Journal:  J Neural Eng       Date:  2011-03-24       Impact factor: 5.379

5.  Using the electrocorticographic speech network to control a brain-computer interface in humans.

Authors:  Eric C Leuthardt; Charles Gaona; Mohit Sharma; Nicholas Szrama; Jarod Roland; Zac Freudenberg; Jamie Solis; Jonathan Breshears; Gerwin Schalk
Journal:  J Neural Eng       Date:  2011-04-07       Impact factor: 5.379

6.  A P300-based brain-computer interface for people with amyotrophic lateral sclerosis.

Authors:  F Nijboer; E W Sellers; J Mellinger; M A Jordan; T Matuz; A Furdea; S Halder; U Mochty; D J Krusienski; T M Vaughan; J R Wolpaw; N Birbaumer; A Kübler
Journal:  Clin Neurophysiol       Date:  2008-06-20       Impact factor: 3.708

7.  Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans.

Authors:  Jeremy R Manning; Joshua Jacobs; Itzhak Fried; Michael J Kahana
Journal:  J Neurosci       Date:  2009-10-28       Impact factor: 6.167

8.  Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases.

Authors:  Elizabeth A Felton; J Adam Wilson; Justin C Williams; P Charles Garell
Journal:  J Neurosurg       Date:  2007-03       Impact factor: 5.115

9.  A multimodal brain-based feedback and communication system.

Authors:  Thilo Hinterberger; Nicola Neumann; Mirko Pham; Andrea Kübler; Anke Grether; Nadine Hofmayer; Barbara Wilhelm; Herta Flor; Niels Birbaumer
Journal:  Exp Brain Res       Date:  2003-11-29       Impact factor: 1.972

10.  Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface.

Authors:  Alexander J Doud; John P Lucas; Marc T Pisansky; Bin He
Journal:  PLoS One       Date:  2011-10-26       Impact factor: 3.240

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  70 in total

Review 1.  Progress in Brain Computer Interface: Challenges and Opportunities.

Authors:  Simanto Saha; Khondaker A Mamun; Khawza Ahmed; Raqibul Mostafa; Ganesh R Naik; Sam Darvishi; Ahsan H Khandoker; Mathias Baumert
Journal:  Front Syst Neurosci       Date:  2021-02-25

2.  Decoding different working memory states during an operation span task from prefrontal fNIRS signals.

Authors:  Ting Chen; Cui Zhao; Xingyu Pan; Junda Qu; Jing Wei; Chunlin Li; Ying Liang; Xu Zhang
Journal:  Biomed Opt Express       Date:  2021-05-18       Impact factor: 3.732

3.  Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation.

Authors:  Kyle M Koss; Matthew A Churchward; Andrea F Jeffery; Vivian K Mushahwar; Anastasia L Elias; Kathryn G Todd
Journal:  J Vis Exp       Date:  2017-12-08       Impact factor: 1.355

4.  Differential expression of genes involved in the acute innate immune response to intracortical microelectrodes.

Authors:  Hillary W Bedell; Nicholas J Schaub; Jeffrey R Capadona; Evon S Ereifej
Journal:  Acta Biomater       Date:  2019-11-14       Impact factor: 8.947

5.  Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks.

Authors:  David Ibáñez-Soria; Aureli Soria-Frisch; Jordi Garcia-Ojalvo; Giulio Ruffini
Journal:  PLoS One       Date:  2019-07-05       Impact factor: 3.240

6.  Keeping Disability in Mind: A Case Study in Implantable Brain-Computer Interface Research.

Authors:  Laura Specker Sullivan; Eran Klein; Tim Brown; Matthew Sample; Michelle Pham; Paul Tubig; Raney Folland; Anjali Truitt; Sara Goering
Journal:  Sci Eng Ethics       Date:  2017-06-22       Impact factor: 3.525

Review 7.  The Evolution of Neuroprosthetic Interfaces.

Authors:  Dayo O Adewole; Mijail D Serruya; James P Harris; Justin C Burrell; Dmitriy Petrov; H Isaac Chen; John A Wolf; D Kacy Cullen
Journal:  Crit Rev Biomed Eng       Date:  2016

8.  Identifying Engineering, Clinical and Patient's Metrics for Evaluating and Quantifying Performance of Brain-Machine Interface (BMI) Systems.

Authors:  Jose L Contreras-Vidal
Journal:  Conf Proc IEEE Int Conf Syst Man Cybern       Date:  2014-10-05

9.  Protection and Repair After Spinal Cord Injury: Accomplishments and Future Directions.

Authors:  W Dalton Dietrich
Journal:  Top Spinal Cord Inj Rehabil       Date:  2015-04-12

10.  Empirical models of scalp-EEG responses using non-concurrent intracranial responses.

Authors:  Komalpreet Kaur; Jerry J Shih; Dean J Krusienski
Journal:  J Neural Eng       Date:  2014-05-19       Impact factor: 5.379

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