Literature DB >> 21436520

P300-based brain-computer interface for environmental control: an asynchronous approach.

F Aloise1, F Schettini, P Aricò, F Leotta, S Salinari, D Mattia, F Babiloni, F Cincotti.   

Abstract

Brain-computer interface (BCI) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for EEG-based BCIs. Classic P300-based BCIs work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient BCI system should be able to understand the user's intentions from the ongoing EEG instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous BCI and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this BCI system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.

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Year:  2011        PMID: 21436520     DOI: 10.1088/1741-2560/8/2/025025

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  16 in total

1.  A P300-based brain-computer interface aimed at operating electronic devices at home for severely disabled people.

Authors:  Rebeca Corralejo; Luis F Nicolás-Alonso; Daniel Alvarez; Roberto Hornero
Journal:  Med Biol Eng Comput       Date:  2014-08-28       Impact factor: 2.602

2.  Using the detectability index to predict P300 speller performance.

Authors:  B O Mainsah; L M Collins; C S Throckmorton
Journal:  J Neural Eng       Date:  2016-10-05       Impact factor: 5.379

3.  Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.

Authors:  Jun Lu; Dennis J McFarland; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2012-12-10       Impact factor: 5.379

4.  Implementation of an Embedded Web Server Application for Wireless Control of Brain Computer Interface Based Home Environments.

Authors:  Eda Akman Aydın; Ömer Faruk Bay; İnan Güler
Journal:  J Med Syst       Date:  2015-11-07       Impact factor: 4.460

5.  The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement.

Authors:  Jing Jin; Brendan Z Allison; Tobias Kaufmann; Andrea Kübler; Yu Zhang; Xingyu Wang; Andrzej Cichocki
Journal:  PLoS One       Date:  2012-11-26       Impact factor: 3.240

6.  Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen Aimed at Wheelchair Navigation Control.

Authors:  Omar Piña-Ramirez; Raquel Valdes-Cristerna; Oscar Yanez-Suarez
Journal:  Comput Math Methods Med       Date:  2018-02-14       Impact factor: 2.238

7.  Asynchronous non-invasive high-speed BCI speller with robust non-control state detection.

Authors:  Sebastian Nagel; Martin Spüler
Journal:  Sci Rep       Date:  2019-06-04       Impact factor: 4.379

8.  The cost of space independence in P300-BCI spellers.

Authors:  Srivas Chennu; Abdulmajeed Alsufyani; Marco Filetti; Adrian M Owen; Howard Bowman
Journal:  J Neuroeng Rehabil       Date:  2013-07-29       Impact factor: 4.262

9.  A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design.

Authors:  Ilya P Ganin; Sergei L Shishkin; Alexander Y Kaplan
Journal:  PLoS One       Date:  2013-10-31       Impact factor: 3.240

10.  Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials.

Authors:  Tobias Kaufmann; Andreas Herweg; Andrea Kübler
Journal:  J Neuroeng Rehabil       Date:  2014-01-16       Impact factor: 4.262

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