Literature DB >> 9535518

EEG-based communication and control: short-term role of feedback.

D J McFarland1, L M McCane, J R Wolpaw.   

Abstract

When people learn to control the amplitudes of certain electroencephalogram (EEG) components (e.g., the 8-12 Hz mu-rhythm over sensorimotor cortex) and use them to move a cursor to a target on a video screen, feedback about performance is normally provided by cursor movement and by trial outcome (i.e., success or failure). We assessed the short-term effects of this feedback on EEG control. After subjects received initial training with feedback present, feedback was removed intermittently for periods of several minutes. Subjects still displayed EEG control when feedback was removed. Removal of cursor movement alone appeared to have effects comparable to removal of both cursor movement and trial outcome. These results show that, in the short-term at least, mu-rhythm control is not dependent on the sensory input provided by cursor movement. They also suggest that feedback can have inhibitory as well as facilitory effects on EEG control, and that these effects vary across subjects. This finding has implications for the design of training procedures.

Entities:  

Mesh:

Year:  1998        PMID: 9535518     DOI: 10.1109/86.662615

Source DB:  PubMed          Journal:  IEEE Trans Rehabil Eng        ISSN: 1063-6528


  13 in total

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2.  Towards an independent brain-computer interface using steady state visual evoked potentials.

Authors:  Brendan Z Allison; Dennis J McFarland; Gerwin Schalk; Shi Dong Zheng; Melody Moore Jackson; Jonathan R Wolpaw
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3.  Novel hybrid brain-computer interface system based on motor imagery and P300.

Authors:  Cili Zuo; Jing Jin; Erwei Yin; Rami Saab; Yangyang Miao; Xingyu Wang; Dewen Hu; Andrzej Cichocki
Journal:  Cogn Neurodyn       Date:  2019-10-21       Impact factor: 5.082

4.  Biased feedback in brain-computer interfaces.

Authors:  Alvaro Barbero; Moritz Grosse-Wentrup
Journal:  J Neuroeng Rehabil       Date:  2010-07-27       Impact factor: 4.262

5.  Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals.

Authors:  E A Felton; R G Radwin; J A Wilson; J C Williams
Journal:  J Neural Eng       Date:  2009-08-21       Impact factor: 5.379

6.  Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals.

Authors:  K Dijkstra; P Brunner; A Gunduz; W Coon; A L Ritaccio; J Farquhar; G Schalk
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2015-08-26

7.  Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles.

Authors:  Lucas R Trambaiolli; Claudinei E Biazoli; André M Cravo; Tiago H Falk; João R Sato
Journal:  Neurophotonics       Date:  2018-09-18       Impact factor: 3.593

8.  Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms.

Authors:  Audrey S Royer; Bin He
Journal:  J Neural Eng       Date:  2009-01-20       Impact factor: 5.379

Review 9.  Neurofeedback and the Aging Brain: A Systematic Review of Training Protocols for Dementia and Mild Cognitive Impairment.

Authors:  Lucas R Trambaiolli; Raymundo Cassani; David M A Mehler; Tiago H Falk
Journal:  Front Aging Neurosci       Date:  2021-06-09       Impact factor: 5.750

10.  Vibrotactile feedback for brain-computer interface operation.

Authors:  Febo Cincotti; Laura Kauhanen; Fabio Aloise; Tapio Palomäki; Nicholas Caporusso; Pasi Jylänki; Donatella Mattia; Fabio Babiloni; Gerolf Vanacker; Marnix Nuttin; Maria Grazia Marciani; José Del R Millán
Journal:  Comput Intell Neurosci       Date:  2007
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