Literature DB >> 12727187

Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems.

Eleanor A Curran1, Maria J Stokes.   

Abstract

Brain-computer interface (BCI) technology relies on the ability of individuals to voluntarily and reliably produce changes in their electroencephalographic (EEG) activity. The present paper reviews research on cognitive tasks and other methods of generating and controlling specific changes in EEG activity that can be used to drive BCI systems. To date, motor imagery has been the most commonly used task. This paper explores the possibility that other cognitive tasks, including those used in imaging studies, may prove to be more effective. Other factors which influence performance are also considered in relation to selection of tasks, as well as training of subjects.

Mesh:

Year:  2003        PMID: 12727187     DOI: 10.1016/s0278-2626(03)00036-8

Source DB:  PubMed          Journal:  Brain Cogn        ISSN: 0278-2626            Impact factor:   2.310


  43 in total

1.  Adaptive feature extraction for EEG signal classification.

Authors:  Shiliang Sun; Changshui Zhang
Journal:  Med Biol Eng Comput       Date:  2006-09-12       Impact factor: 2.602

2.  Model analyses of visual biofeedback training for EEG-based brain-computer interface.

Authors:  Chih-Wei Chen; Ming-Shaung Ju; Yun-Nien Sun; Chou-Ching K Lin
Journal:  J Comput Neurosci       Date:  2009-04-09       Impact factor: 1.621

3.  Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery.

Authors:  Alvaro Fuentes Cabrera; Dario Farina; Kim Dremstrup
Journal:  Med Biol Eng Comput       Date:  2009-12-30       Impact factor: 2.602

4.  Identification of task parameters from movement-related cortical potentials.

Authors:  Ying Gu; Omar Feix do Nascimento; Marie-Françoise Lucas; Dario Farina
Journal:  Med Biol Eng Comput       Date:  2009-12       Impact factor: 2.602

5.  Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.

Authors:  Jennifer Stiso; Marie-Constance Corsi; Jean M Vettel; Javier Garcia; Fabio Pasqualetti; Fabrizio De Vico Fallani; Timothy H Lucas; Danielle S Bassett
Journal:  J Neural Eng       Date:  2020-07-24       Impact factor: 5.379

6.  Affective Brain-Computer Interfaces As Enabling Technology for Responsive Psychiatric Stimulation.

Authors:  Alik S Widge; Darin D Dougherty; Chet T Moritz
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2014-04-01

7.  Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

Authors:  Jianjun Meng; John Mundahl; Taylor Streitz; Kaitlin Maile; Nicholas Gulachek; Jeffrey He; Bin He
Journal:  IEEE Access       Date:  2017-09-11       Impact factor: 3.367

8.  Analyzing text recognition from tactually evoked EEG.

Authors:  A Khasnobish; S Datta; R Bose; D N Tibarewala; A Konar
Journal:  Cogn Neurodyn       Date:  2017-09-06       Impact factor: 5.082

9.  Offline Identification of Imagined Speed of Wrist Movements in Paralyzed ALS Patients from Single-Trial EEG.

Authors:  Ying Gu; Dario Farina; Ander Ramos Murguialday; Kim Dremstrup; Pedro Montoya; Niels Birbaumer
Journal:  Front Neurosci       Date:  2009-08-10       Impact factor: 4.677

10.  Biased feedback in brain-computer interfaces.

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

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