The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index.
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI?
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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“As a result, the general concerns about the non-universal usability, robustness, and, especially, the role of training raised by able-bodied user studies [3–7] are even more pressing with regard to end-user populations.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [7]
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Self-citation
Metadata
- Authors
- Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A.
- Affiliations
- Graz University of Technology (AT)
- University of Bremen (DE)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2010-04
- Volume
- 18
- Issue
- 2
- Pages
- 107-116
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- 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
- 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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