How Many People Could Use an SSVEP BCI?
Open accessAbstract
Brain-computer interfaces (BCI) are communication systems that allow people to send messages or commands without movement. BCIs rely on different types of signals in the electroencephalogram (EEG), typically P300s, steady-state visually evoked potentials (SSVEP), or event-related desynchronization. Early BCI systems were often evaluated with a selected group of subjects. Also, many articles do not mention data from subjects who performed poorly. These and other factors have made it difficult to estimate how many people could use different BCIs. The present study explored how many subjects could use an SSVEP BCI. We recorded data from 53 subjects while they participated in 1-4 runs that were each 4 min long. During these runs, the subjects focused on one of four LEDs that each flickered at a different frequency. The eight channel EEG data were analyzed with a minimum energy parameter estimation algorithm and classified with linear discriminant analysis into one of the four classes. Online results showed that SSVEP BCIs could provide effective communication for all 53 subjects, resulting in a grand average accuracy of 95.5%. About 96.2% of the subjects reached an accuracy above 80%, and nobody was below 60%. This study showed that SSVEP based BCI systems can reach very high accuracies after only a very short training period. The SSVEP approach worked for all participating subjects, who attained accuracy well above chance level. This is important because it shows that SSVEP BCIs could provide communication for some users when other approaches might not work for them.
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General concerns about non-universal usability, robustness, and the role of training raised by able-bodied user studies are even more pressing with regard to end-user populations.
“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 [6]
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References 31 · Cited by 176
- Authors
- Guger, C., Allison, B. Z., Großwindhager, B., Prückl, R., Hintermüller, C., Kapeller, C., Bruckner, M., Krausz, G., & Edlinger, G.
- Affiliations
- Guger Technologies (Austria) (AT)
- University of California San Diego (US)
- Institute for Infocomm Research (SG)
- Corresponding author
- Guger, Christoph
- ORCID
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2012
- Volume
- 6
- Pages
- 169-169
- ISSN
- 1662-453X (ISSN-L)
- 1662-4548
- PMC
- PMC3500831
- OpenAlex
- W2023666981
- Cited by
- 176
- References
- 31
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 9 · 2023: 6 · 2022: 11 · 2021: 22 · 2020: 8 · 2019: 16 · 2018: 16 · 2017: 26 · 2016: 13 · 2015: 15 · 2014: 15 · 2013: 9
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- steady-state visual evoked potentials, brain-computer interface, EEG classification, linear discriminant analysis, training duration
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