Literature DB >> 12899248

ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system.

Brendan Z Allison1, Jaime A Pineda.   

Abstract

A brain-computer interface (BCI) system may allow a user to communicate by selecting one of many options. These options may be presented in a matrix. Larger matrices allow a larger vocabulary, but require more time for each selection. In this study, subjects were asked to perform a target detection task using matrices appropriate for a BCI. The study sought to explore the relationship between matrix size and EEG measures, target detection accuracy, and user preferences. Results indicated that larger matrices evoked a larger P300 amplitude, and that matrix size did not significantly affect performance or preferences.

Mesh:

Year:  2003        PMID: 12899248     DOI: 10.1109/TNSRE.2003.814448

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  35 in total

1.  Control of a visual keyboard using an electrocorticographic brain-computer interface.

Authors:  Dean J Krusienski; Jerry J Shih
Journal:  Neurorehabil Neural Repair       Date:  2010-10-04       Impact factor: 3.919

2.  Predictive spelling with a P300-based brain-computer interface: Increasing the rate of communication.

Authors:  D B Ryan; G E Frye; G Townsend; D R Berry; S Mesa-G; N A Gates; E W Sellers
Journal:  Int J Hum Comput Interact       Date:  2011-01-01       Impact factor: 3.353

3.  Does the 'P300' speller depend on eye gaze?

Authors:  P Brunner; S Joshi; S Briskin; J R Wolpaw; H Bischof; G Schalk
Journal:  J Neural Eng       Date:  2010-09-21       Impact factor: 5.379

4.  A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.

Authors:  G Townsend; B K LaPallo; C B Boulay; D J Krusienski; G E Frye; C K Hauser; N E Schwartz; T M Vaughan; J R Wolpaw; E W Sellers
Journal:  Clin Neurophysiol       Date:  2010-03-26       Impact factor: 3.708

5.  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
Journal:  Clin Neurophysiol       Date:  2008-02       Impact factor: 3.708

6.  Describing different brain computer interface systems through a unique model: a UML implementation.

Authors:  Lucia Rita Quitadamo; Maria Grazia Marciani; Gian Carlo Cardarilli; Luigi Bianchi
Journal:  Neuroinformatics       Date:  2008-07-08

7.  An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling.

Authors:  Mohammad Moghadamfalahi; Murat Akcakaya; Hooman Nezamfar; Jamshid Sourati; Deniz Erdogmus
Journal:  IEEE Trans Signal Process       Date:  2017-07-17       Impact factor: 4.931

8.  Whether generic model works for rapid ERP-based BCI calibration.

Authors:  Jing Jin; Eric W Sellers; Yu Zhang; Ian Daly; Xingyu Wang; Andrzej Cichocki
Journal:  J Neurosci Methods       Date:  2012-09-29       Impact factor: 2.390

9.  Bayesian approach to dynamically controlling data collection in P300 spellers.

Authors:  Chandra S Throckmorton; Kenneth A Colwell; David B Ryan; Eric W Sellers; Leslie M Collins
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-03-21       Impact factor: 3.802

10.  A tactile P300 brain-computer interface.

Authors:  Anne-Marie Brouwer; Jan B F van Erp
Journal:  Front Neurosci       Date:  2010-05-06       Impact factor: 4.677

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