The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces
Abstract
OBJECTIVE: Today, the brain-computer interface (BCI) community lacks a standard method to evaluate an online BCI's performance. Even the most commonly used metric, the information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which is not conducive to BCI research. This paper aims to point out many of the existing problems and give some suggestions and methods to overcome these problems. APPROACH: First, the preconditions inherent in ITR calculation based on Wolpaw's definition are summarized and several incorrect ITR calculations, which go against the preconditions, are indicated. Then, the issues affecting ITR estimation during the test of online BCI systems are discussed in detail. Finally, a task-oriented online BCI test platform was proposed, which may help BCI evaluations in real-world applications. MAIN RESULTS: The guidelines for ITR calculation in online BCIs testing are proposed. The platform executed in the Beijing BCI Competition 2010 shows that it can be used as a common way to compare the online performances (including the ITR) of existing BCI paradigms. SIGNIFICANCE: The proposed guidelines and task-oriented test platform may reduce the uncertainty and artifacts of online BCI performance evaluation; they provide a relatively objective way to compare different BCI's performances in real-world BCI applications, which is a forward step toward developing standards for BCI performance evaluation.
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Citations
Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Motor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
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. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.01.030 · Cited by 570 · cited 1×
“Some articles estimate the ITR both with and without t 1 (Townsend et al 2010, Jin et al 2011).”
BackgroundHuggins, J. E., Wren, P. A., & Gruis, K. L. (2011) · Amyotrophic Lateral Sclerosis · doi:10.3109/17482968.2011.572978 · Cited by 215
Cited by
LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & He, B. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046003 · Cited by 583
Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review
Congedo, M., Barachant, A., & Bhatia, R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2017.1297192 · Cited by 456
Visual and Auditory Brain–Computer Interfaces
Gao, S., Wang, Y., Gao, X., & Hong, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300164 · Cited by 449
Self-citationRestoring sensorimotor function through intracortical interfaces: progress and looming challenges
Bensmaia, S. J., & Miller, L. E. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3724 · Cited by 368
A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller
Yin, E., Zhou, Z., Jiang, J., Yu, Y., & Hu, D. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2320948 · Cited by 254
Metadata
- Authors
- Yuan, P., Gao, X., Allison, B., Wang, Y., Bin, G., & Gao, S.
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2013-02-28
- Volume
- 10
- Issue
- 2
- Pages
- 026014
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2080597220
- Cited by
- 160
- References
- 5
- FWCI
- 8.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yuan, P., Gao, X., Allison, B., Wang, Y., Bin, G., & Gao, S. (2013). A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces. Journal of Neural Engineering. https://doi.org/10.1088/1741-2560/10/2/026014
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