Literature DB >> 17028907

Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

Babak Mahmoudi1, Abbas Erfanian.   

Abstract

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

Mesh:

Year:  2006        PMID: 17028907     DOI: 10.1007/s11517-006-0111-8

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  29 in total

1.  Optimal spatial filtering of single trial EEG during imagined hand movement.

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Journal:  IEEE Trans Rehabil Eng       Date:  2000-12

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Journal:  IEEE Trans Biomed Eng       Date:  2000-10       Impact factor: 4.538

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Journal:  IEEE Trans Rehabil Eng       Date:  2000-06

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Journal:  IEEE Trans Neural Netw       Date:  2002

Review 5.  The neurophysiological basis of motor imagery.

Authors:  J Decety
Journal:  Behav Brain Res       Date:  1996-05       Impact factor: 3.332

6.  Electrophysiological correlates of higher states of consciousness during sleep in long-term practitioners of the Transcendental Meditation program.

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Journal:  Sleep       Date:  1997-02       Impact factor: 5.849

7.  Walking from thought.

Authors:  Gert Pfurtscheller; Robert Leeb; Claudia Keinrath; Doron Friedman; Christa Neuper; Christoph Guger; Mel Slater
Journal:  Brain Res       Date:  2006-01-10       Impact factor: 3.252

8.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.

Authors:  A Kübler; F Nijboer; J Mellinger; T M Vaughan; H Pawelzik; G Schalk; D J McFarland; N Birbaumer; J R Wolpaw
Journal:  Neurology       Date:  2005-05-24       Impact factor: 9.910

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Journal:  Eur J Neurosci       Date:  1998-03       Impact factor: 3.386

10.  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

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  13 in total

1.  Delta band contribution in cue based single trial classification of real and imaginary wrist movements.

Authors:  Aleksandra Vuckovic; Francisco Sepulveda
Journal:  Med Biol Eng Comput       Date:  2008-04-17       Impact factor: 2.602

2.  The impact of mind-body awareness training on the early learning of a brain-computer interface.

Authors:  Kaitlin Cassady; Albert You; Alex Doud; Bin He
Journal:  Technology (Singap World Sci)       Date:  2014-09

3.  Analysis of eyes open, eye closed EEG signals using second-order difference plot.

Authors:  Ranjit A Thuraisingham; Yvonne Tran; Peter Boord; Ashley Craig
Journal:  Med Biol Eng Comput       Date:  2007-10-10       Impact factor: 2.602

4.  Performance assessment in brain-computer interface-based augmentative and alternative communication.

Authors:  David E Thompson; Stefanie Blain-Moraes; Jane E Huggins
Journal:  Biomed Eng Online       Date:  2013-05-16       Impact factor: 2.819

5.  Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination.

Authors:  A Broniec
Journal:  Med Biol Eng Comput       Date:  2016-04-08       Impact factor: 2.602

6.  User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

Authors:  Minkyu Ahn; Hohyun Cho; Sangtae Ahn; Sung C Jun
Journal:  Front Hum Neurosci       Date:  2018-02-15       Impact factor: 3.169

7.  Training with brain-machine interfaces, visuo-tactile feedback and assisted locomotion improves sensorimotor, visceral, and psychological signs in chronic paraplegic patients.

Authors:  Solaiman Shokur; Ana R C Donati; Debora S F Campos; Claudia Gitti; Guillaume Bao; Dora Fischer; Sabrina Almeida; Vania A S Braga; Patricia Augusto; Chris Petty; Eduardo J L Alho; Mikhail Lebedev; Allen W Song; Miguel A L Nicolelis
Journal:  PLoS One       Date:  2018-11-29       Impact factor: 3.240

8.  Using mental tasks transitions detection to improve spontaneous mental activity classification.

Authors:  Ferran Galán; Francesc Oliva; Joan Guàrdia
Journal:  Med Biol Eng Comput       Date:  2007-05-31       Impact factor: 3.079

9.  Gamma band activity associated with BCI performance: simultaneous MEG/EEG study.

Authors:  Minkyu Ahn; Sangtae Ahn; Jun H Hong; Hohyun Cho; Kiwoong Kim; Bong S Kim; Jin W Chang; Sung C Jun
Journal:  Front Hum Neurosci       Date:  2013-12-06       Impact factor: 3.169

Review 10.  Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review.

Authors:  George Papanastasiou; Athanasios Drigas; Charalabos Skianis; Miltiadis Lytras
Journal:  Heliyon       Date:  2020-09-05
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