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A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns
Open accessAbstract
Objective An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988).Methods Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data.Results Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP.Conclusions These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability.Significance The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.
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“EEG spellers based on oddball potentials or motor imagery typically achieve 1–5 characters per minute28–32.”
From High-performance brain-to-text communication via handwriting (2021) · cites this paper as [30]
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Bittencourt-Villalpando, M. (2018) · doi:10.11606/d.3.2018.tde-19032018-090128 · Cited by 1
Information-Theoretic Analysis of Refractory Effects in the P300 SpellerPreprint
Mayya, V., Mainsah, B. O., & Reeves, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.03313 · Cited by 1
Simple deep neural networks show state-of-the-art performance in ERP-based BCI
Delobel, L., Maby, E., & Mattout, J. (2020) · TUGraz OPEN Library (Graz University of Technology) · doi:10.3217/978-3-85125-682-6-49 · Cited by 1
Noninvasive Brain-Computer Interfaces
Allison, B. Z., & Krusienski, D. J. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6675-8_707 · Cited by 1
Self-citationSchwartz, N. E. (2010) · Digital Commons - East Tennessee State University (East Tennessee State University)
Self-citationCasagrande, A., Jarmolowska, J., Turconi, M. M., Fabris, F., Busan, P., & Battaglini, P. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04485
Metadata
- Authors
- Townsend, G., LaPallo, B. K., Boulay, C. B., Krusienski, D. J., Frye, G. E., Hauser, C. K., Schwartz, N. E., Vaughan, T. M., Wolpaw, J. R., & Sellers, E. W.
- Affiliations
- Algoma University (CA)
- New York State Department of Health (US)
- University of North Florida (US)
- East Tennessee State University (US)
- Corresponding author
- Sellers, E.W.
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2010-07
- Volume
- 121
- Issue
- 7
- Pages
- 1109-1120
- ISSN
- 1388-2457 (ISSN-L)
- PMC
- PMC2879474
- OpenAlex
- W2158655338
- Cited by
- 570
- References
- 49
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 17 · 2023: 17 · 2022: 23 · 2021: 36 · 2020: 39 · 2019: 35 · 2018: 41 · 2017: 42 · 2016: 40 · 2015: 47 · 2014: 60 · 2013: 51 · 2012: 47
- FWCI
- 29.48
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Commons - East Tennessee State University (East Tennessee State University) (submittedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (EB 00856, R01EB000856)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030146, HD30146)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- P300, brain-computer interface, amyotrophic lateral sclerosis, neuromuscular disorders, communication aids, data rate, linear discriminant analysis, user acceptance
- MeSH terms
- Brain, Electroencephalography, Female, Humans, Male, Nervous System Diseases, Pattern Recognition, Visual, Photic Stimulation, User-Computer Interface, Event-Related Potentials, P300
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