The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method
Open accessAbstract
In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 +/- 9.6 bit min(-1). The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.
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Cites
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Brain-computer interface technology: a review of the first international meeting
Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., Donchin, E., Quatrano, L. A., Robinson, C. J., & Vaughan, T. M. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/tre.2000.847807 · Cited by 2,324
Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs
Lin, Z., Zhang, C., Wu, W., & Gao, X. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.889197 · Cited by 867
Self-citationDesign and implementation of a brain-computer interface with high transfer rates
Cheng, M., Gao, X., Gao, S., & Xu, D. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.803536 · Cited by 818
Self-citationFrequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
Lin, Z., Zhang, C., Wu, W., & Gao, X. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.886577 · Cited by 724
Self-citationBrain-computer interfaces based on the steady-state visual-evoked response
Middendorf, M., McMillan, G., Calhoun, G., & Jones, K. S. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847819 · Cited by 685
A practical VEP-based brain-computer interface
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Cited by
Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives
Vialatte, F.-B., Maurice, M., Dauwels, J., & Cichocki, A. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2009.11.005 · Cited by 1,066
A Survey of Stimulation Methods Used in SSVEP-Based BCIs
Zhu, D., Bieger, J., Garcia Molina, G., & Aarts, R. M. (2010) · Computational Intelligence and Neuroscience · doi:10.1155/2010/702357 · Cited by 654
Nakanishi, M., Wang, Y., Wang, Y.-T., & Jung, T.-P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140703 · Cited by 376
FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS
ZHANG, Y., ZHOU, G., JIN, J., WANG, X., & CICHOCKI, A. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714500130 · Cited by 357
Waytowich, N., Lawhern, V. J., Garcia, J. O., Cummings, J., Faller, J., Sajda, P., & Vettel, J. M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae5d8 · Cited by 262
Metadata
- Authors
- Bin, G., Gao, X., Yan, Z., Hong, B., & Gao, S.
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2009-06-03
- Volume
- 6
- Issue
- 4
- Pages
- 046002
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2105478324
- Cited by
- 773
- References
- 23
- FWCI
- 13.25
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Bin, G., Gao, X., Yan, Z., Hong, B., & Gao, S. (2009). An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. Journal of Neural Engineering. https://doi.org/10.1088/1741-2560/6/4/046002
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