The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller
Abstract
The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain-computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications.
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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
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
BCI2000: A General-Purpose Brain-Computer Interface (BCI) System
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827072 · Cited by 3,024
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
The mental prosthesis: assessing the speed of a P300-based brain-computer interface
Donchin, E., Spencer, K. M., & Wijesinghe, R. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847808 · Cited by 1,222
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
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
Design 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
Bin, G., Gao, X., Yan, Z., Hong, B., & Gao, S. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/4/046002 · Cited by 773
Toward enhanced p300 speller performance
Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.07.017 · Cited by 749
Cited by
Brain–Computer Interface Spellers: A Review
Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., & Volosyak, I. (2018) · Brain Sciences · doi:10.3390/brainsci8040057 · Cited by 391
Stocco, A., Prat, C. S., Losey, D. M., Cronin, J. A., Wu, J., Abernethy, J. A., & Rao, R. P. N. (2015) · PLOS ONE · doi:10.1371/journal.pone.0137303 · Cited by 57
“A larger difference between the frequencies would not have necessarily improved accuracy as many previous experiments have demonstrated effective SSVEP-based BCIs with frequency differences less than or equal to 1 Hz [14,15], with some using frequency differences as small as 0.”
Metadata
- Authors
- Yin, E., Zhou, Z., Jiang, J., Yu, Y., & Hu, D.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2015-06
- Volume
- 62
- Issue
- 6
- Pages
- 1447-1456
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2004959199
- Cited by
- 254
- References
- 44
- FWCI
- 7.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yin, E., Zhou, Z., Jiang, J., Yu, Y., & Hu, D. (2015). A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2014.2320948
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