Literature DB >> 24068244

Electroencephalography (EEG)-based neurofeedback training for brain-computer interface (BCI).

Kyuwan Choi1.   

Abstract

Electroencephalography has become a popular tool in basic brain research, but in recent years, several practical limitations have been highlighted. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery, we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues, we here introduce a nontrivial modification to brain-computer Interfaces (BCI). We combine the fast signal processing of motor imagery with the ability to parameterize external visual feedback in the context of a very simple control task: attempting to intentionally control the direction of an external cursor on command. By engaging the subject in motor imagery while providing real-time visual feedback on their instantaneous performance, we can take advantage of positive features present in both externally- and internally driven learning. We further use a classifier that automatically selects the cortical activation features that most likely maximize the performance accuracy. Under this closed loop coadaptation system, we saw a progression of the cortical activation that started in sensorymotor areas, when at chance performance motor imagery was explicitly used, migrated to BA6 under deliberate control and ended in the more frontal regions of prefrontal cortex, when at maximal performance accuracy, the subjects reportedly developed spontaneous mental control of the instructed direction. We discuss our results in light of possible applications of this simple BCI paradigm to study various cognitive phenomena involving the deliberate control of a directional signal in decision making tasks performed with intent.

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Year:  2013        PMID: 24068244     DOI: 10.1007/s00221-013-3699-6

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  24 in total

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

Authors:  H Ramoser; J Müller-Gerking; G Pfurtscheller
Journal:  IEEE Trans Rehabil Eng       Date:  2000-12

2.  Brain-computer interfaces based on the steady-state visual-evoked response.

Authors:  M Middendorf; G McMillan; G Calhoun; K S Jones
Journal:  IEEE Trans Rehabil Eng       Date:  2000-06

Review 3.  Brain-computer interfaces for communication and control.

Authors:  Jonathan R Wolpaw; Niels Birbaumer; Dennis J McFarland; Gert Pfurtscheller; Theresa M Vaughan
Journal:  Clin Neurophysiol       Date:  2002-06       Impact factor: 3.708

4.  A simple and efficient algorithm for gene selection using sparse logistic regression.

Authors:  S K Shevade; S S Keerthi
Journal:  Bioinformatics       Date:  2003-11-22       Impact factor: 6.937

5.  Motor imagery classification by means of source analysis for brain-computer interface applications.

Authors:  Lei Qin; Lei Ding; Bin He
Journal:  J Neural Eng       Date:  2004-08-31       Impact factor: 5.379

6.  Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis.

Authors:  Baharan Kamousi; Zhongming Liu; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-06       Impact factor: 3.802

7.  A practical VEP-based brain-computer interface.

Authors:  Yijun Wang; Ruiping Wang; Xiaorong Gao; Bo Hong; Shangkai Gao
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-06       Impact factor: 3.802

8.  Single-trial EEG source reconstruction for brain-computer interface.

Authors:  Quentin Noirhomme; Richard I Kitney; Benoĺt Macq
Journal:  IEEE Trans Biomed Eng       Date:  2008-05       Impact factor: 4.538

9.  Effectiveness of a brain-computer interface based programme for the treatment of ADHD: a pilot study.

Authors:  Choon Guan Lim; Tih-Shih Lee; Cuntai Guan; Daniel Shuen Sheng Fung; Yin Bun Cheung; Stephanie Sze Wei Teng; Haihong Zhang; K Ranga Krishnan
Journal:  Psychopharmacol Bull       Date:  2010

10.  Functional organization of human supplementary motor cortex studied by electrical stimulation.

Authors:  I Fried; A Katz; G McCarthy; K J Sass; P Williamson; S S Spencer; D D Spencer
Journal:  J Neurosci       Date:  1991-11       Impact factor: 6.167

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  6 in total

1.  Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface.

Authors:  Noman Naseer; Nauman Khalid Qureshi; Farzan Majeed Noori; Keum-Shik Hong
Journal:  Comput Intell Neurosci       Date:  2016-09-20

Review 2.  Object Recognition in Mental Representations: Directions for Exploring Diagnostic Features through Visual Mental Imagery.

Authors:  Stephanie M Roldan
Journal:  Front Psychol       Date:  2017-05-23

3.  Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.

Authors:  Noman Naseer; Farzan M Noori; Nauman K Qureshi; Keum-Shik Hong
Journal:  Front Hum Neurosci       Date:  2016-05-25       Impact factor: 3.169

Review 4.  Opportunities for Guided Multichannel Non-invasive Transcranial Current Stimulation in Poststroke Rehabilitation.

Authors:  Begonya Otal; Anirban Dutta; Águida Foerster; Oscar Ripolles; Amy Kuceyeski; Pedro C Miranda; Dylan J Edwards; Tihomir V Ilić; Michael A Nitsche; Giulio Ruffini
Journal:  Front Neurol       Date:  2016-02-24       Impact factor: 4.003

5.  Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features.

Authors:  Rihui Li; Thomas Potter; Weitian Huang; Yingchun Zhang
Journal:  Front Hum Neurosci       Date:  2017-09-15       Impact factor: 3.169

6.  Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients.

Authors:  Nauman Khalid Qureshi; Noman Naseer; Farzan Majeed Noori; Hammad Nazeer; Rayyan Azam Khan; Sajid Saleem
Journal:  Front Neurorobot       Date:  2017-07-17       Impact factor: 2.650

  6 in total

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