Cited by 344EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyEmotion and Mood Recognition

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BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI?

Allison, Brendan, Luth, Thorsten, Valbuena, Diana, Teymourian, Amir, Volosyak, Ivan, Graser, Axel·2010·IEEE Transactions on Neural Systems and Rehabilitation Engineering

DOI 10.1109/tnsre.2009.2039495

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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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Authors: Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A.
Affiliations: Graz University of Technology (AT)
University of Bremen (DE)
ORCID: Allison, Brendan
Volosyak, Ivan
Venue: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2010-04
Volume: 18
Issue: 2
Pages: 107-116
ISSN: 1534-4320 (ISSN-L)
1558-0210
DOI: 10.1109/tnsre.2009.2039495
OpenAlex: W2137237725
Cited by: 344
References: 43
Citations by year: 2026: 3 · 2025: 13 · 2024: 12 · 2023: 14 · 2022: 21 · 2021: 13 · 2020: 18 · 2019: 23 · 2018: 35 · 2017: 25 · 2016: 24 · 2015: 23 · 2014: 27 · 2013: 33 · 2012: 22
FWCI: 17.26
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
PubMed (publishedVersion)
PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords: brain-computer interface, steady-state visual evoked potentials, BCI usability, user demographics, age differences, gender differences, user preferences
MeSH terms: Adolescent, Adult, Aged, Aging, Brain, Communication, Communication Aids for Disabled, Communication Devices for People with Disabilities, Computer Literacy, Demography, Electroencephalography, Evoked Potentials, Female, Humans, Individuality, Male, Middle Aged, Photic Stimulation, Sex Characteristics, Software, User-Computer Interface, Young Adult

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BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI?

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