Literature DB >> 20083463

BCI demographics: how many (and what kinds of) people can use an SSVEP BCI?

Brendan Allison1, Thorsten Luth, Diana Valbuena, Amir Teymourian, Ivan Volosyak, Axel Graser.   

Abstract

Brain-computer interface (BCI) systems enable communication without movement. It is unclear why some BCI approaches or parameters are less effective with some users. This study elucidates BCI demographics by exploring correlations among BCI performance, personal preferences, and different subject factors such as age or gender. Results showed that most people, despite having no prior BCI experience, could use the Bremen SSVEP BCI system in a very noisy field setting. Performance tended to be better in both young and female subjects. Most subjects stated that they did not consider the flickering stimuli annoying and would use or recommend this BCI system. These and other demographic analyses may help identify the best BCI for each user.

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Year:  2010        PMID: 20083463     DOI: 10.1109/TNSRE.2009.2039495

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


  51 in total

1.  The performance of 9-11-year-old children using an SSVEP-based BCI for target selection.

Authors:  James J S Norton; Jessica Mullins; Birgit E Alitz; Timothy Bretl
Journal:  J Neural Eng       Date:  2018-06-28       Impact factor: 5.379

2.  Heading for new shores! Overcoming pitfalls in BCI design.

Authors:  Ricardo Chavarriaga; Melanie Fried-Oken; Sonja Kleih; Fabien Lotte; Reinhold Scherer
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2016-12-30

3.  Critiquing the Concept of BCI Illiteracy.

Authors:  Margaret C Thompson
Journal:  Sci Eng Ethics       Date:  2018-08-16       Impact factor: 3.525

4.  Novel non-contact control system of electric bed for medical healthcare.

Authors:  Chi-Chun Lo; Shang-Ho Tsai; Bor-Shyh Lin
Journal:  Med Biol Eng Comput       Date:  2016-06-15       Impact factor: 2.602

5.  EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy.

Authors:  Min-Ho Lee; O-Yeon Kwon; Yong-Jeong Kim; Hong-Kyung Kim; Young-Eun Lee; John Williamson; Siamac Fazli; Seong-Whan Lee
Journal:  Gigascience       Date:  2019-05-01       Impact factor: 6.524

6.  Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

Authors:  Jonathan S Brumberg; Anh Nguyen; Kevin M Pitt; Sean D Lorenz
Journal:  Disabil Rehabil Assist Technol       Date:  2018-01-31

7.  Practical real-time MEG-based neural interfacing with optically pumped magnetometers.

Authors:  Marc M Van Hulle; Richard Bowtell; Matthew J Brookes; Benjamin Wittevrongel; Niall Holmes; Elena Boto; Ryan Hill; Molly Rea; Arno Libert; Elvira Khachatryan
Journal:  BMC Biol       Date:  2021-08-10       Impact factor: 7.431

8.  Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains.

Authors:  Seung-Schik Yoo; Hyungmin Kim; Emmanuel Filandrianos; Seyed Javid Taghados; Shinsuk Park
Journal:  PLoS One       Date:  2013-04-03       Impact factor: 3.240

9.  How Many People Could Use an SSVEP BCI?

Authors:  Christoph Guger; Brendan Z Allison; Bernhard Großwindhager; Robert Prückl; Christoph Hintermüller; Christoph Kapeller; Markus Bruckner; Gunther Krausz; Günter Edlinger
Journal:  Front Neurosci       Date:  2012-11-19       Impact factor: 4.677

10.  Age-specific mechanisms in an SSVEP-based BCI scenario: evidences from spontaneous rhythms and neuronal oscillators.

Authors:  Jan Ehlers; Diana Valbuena; Anja Stiller; Axel Gräser
Journal:  Comput Intell Neurosci       Date:  2012-12-06
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