Literature DB >> 19592793

Brain-computer interfaces and neurorehabilitation.

Roberta Carabalona1, Paolo Castiglioni, Furio Gramatica.   

Abstract

A brain-computer interface (BCI) directly uses brain-activity signals to allow users to operate the environment without any muscular activation. Thanks to this feature, BCI systems can be employed not only as assistive devices, but also as neurorehabilitation tools in clinical settings. However, several critical issues need to be addressed before using BCI in neurorehabilitation, issues ranging from signal acquisition and selection of the proper BCI paradigm to the evaluation of the affective state, cognitive load and system acceptability of the users. Here we discuss these issues, illustrating how a rehabilitation program can benefit from BCI sessions, and summarize the results obtained so far in this field. Also provided are experimental data concerning two important topics related to BCI usability in rehabilitation: the possibility of using dry electrodes for EEG acquisition, and the monitoring of psychophysiological effects during BCI tasks.

Mesh:

Year:  2009        PMID: 19592793

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  7 in total

1.  Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces.

Authors:  Germán Rodríguez-Bermúdez; Pedro J García-Laencina
Journal:  J Med Syst       Date:  2012-11-02       Impact factor: 4.460

Review 2.  Creating the feedback loop: closed-loop neurostimulation.

Authors:  Adam O Hebb; Jun Jason Zhang; Mohammad H Mahoor; Christos Tsiokos; Charles Matlack; Howard Jay Chizeck; Nader Pouratian
Journal:  Neurosurg Clin N Am       Date:  2013-10-23       Impact factor: 2.509

3.  Comparison of dry and gel based electrodes for p300 brain-computer interfaces.

Authors:  Christoph Guger; Gunther Krausz; Brendan Z Allison; Guenter Edlinger
Journal:  Front Neurosci       Date:  2012-05-07       Impact factor: 4.677

4.  A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes.

Authors:  Ivo Käthner; Sebastian Halder; Christoph Hintermüller; Arnau Espinosa; Christoph Guger; Felip Miralles; Eloisa Vargiu; Stefan Dauwalder; Xavier Rafael-Palou; Marc Solà; Jean M Daly; Elaine Armstrong; Suzanne Martin; Andrea Kübler
Journal:  Front Neurosci       Date:  2017-05-22       Impact factor: 4.677

Review 5.  Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders.

Authors:  Frédéric D Broccard; Tim Mullen; Yu Mike Chi; David Peterson; John R Iversen; Mike Arnold; Kenneth Kreutz-Delgado; Tzyy-Ping Jung; Scott Makeig; Howard Poizner; Terrence Sejnowski; Gert Cauwenberghs
Journal:  Ann Biomed Eng       Date:  2014-05-15       Impact factor: 3.934

6.  An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy.

Authors:  Darren J Leamy; Juš Kocijan; Katarina Domijan; Joseph Duffin; Richard Ap Roche; Sean Commins; Ronan Collins; Tomas E Ward
Journal:  J Neuroeng Rehabil       Date:  2014-01-28       Impact factor: 4.262

7.  Comparison of EEG measurement of upper limb movement in motor imagery training system.

Authors:  Arpa Suwannarat; Setha Pan-Ngum; Pasin Israsena
Journal:  Biomed Eng Online       Date:  2018-08-02       Impact factor: 2.819

  7 in total

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