Literature DB >> 18835541

Brain-computer interfaces in neurological rehabilitation.

Janis J Daly1, Jonathan R Wolpaw.   

Abstract

Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.

Entities:  

Mesh:

Year:  2008        PMID: 18835541     DOI: 10.1016/S1474-4422(08)70223-0

Source DB:  PubMed          Journal:  Lancet Neurol        ISSN: 1474-4422            Impact factor:   44.182


  218 in total

Review 1.  Brain-computer interfaces in medicine.

Authors:  Jerry J Shih; Dean J Krusienski; Jonathan R Wolpaw
Journal:  Mayo Clin Proc       Date:  2012-02-10       Impact factor: 7.616

Review 2.  Brain computer interfaces, a review.

Authors:  Luis Fernando Nicolas-Alonso; Jaime Gomez-Gil
Journal:  Sensors (Basel)       Date:  2012-01-31       Impact factor: 3.576

3.  The neural substrate of predictive motor timing in spinocerebellar ataxia.

Authors:  Martin Bares; Ovidiu V Lungu; Tao Liu; Tobias Waechter; Christopher M Gomez; James Ashe
Journal:  Cerebellum       Date:  2011-06       Impact factor: 3.847

4.  Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation.

Authors:  Mads Jochumsen; Imran Khan Niazi; Kim Dremstrup; Ernest Nlandu Kamavuako
Journal:  Med Biol Eng Comput       Date:  2015-12-06       Impact factor: 2.602

5.  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

6.  Access interface strategies.

Authors:  Susan Fager; David R Beukelman; Melanie Fried-Oken; Tom Jakobs; John Baker
Journal:  Assist Technol       Date:  2011

7.  Neurological rehabilitation of stroke patients by means of a robotically assisted brain controlled interface.

Authors:  Mohd Zaid Abdullah
Journal:  Malays J Med Sci       Date:  2009-04

8.  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

Review 9.  Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity.

Authors:  Wei Wang; Jennifer L Collinger; Monica A Perez; Elizabeth C Tyler-Kabara; Leonardo G Cohen; Niels Birbaumer; Steven W Brose; Andrew B Schwartz; Michael L Boninger; Douglas J Weber
Journal:  Phys Med Rehabil Clin N Am       Date:  2010-02       Impact factor: 1.784

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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