The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP
Abstract
This study proposes a novel hybrid brain-computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers.
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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
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 · cited 1×
“The SSVEP is a periodic neural response evoked by certain visual repetitive stimuli [8].”
BackgroundTownsend, 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×
“are referred to as “adjacency-distraction errors” [13].”
BCI demographics: how many (and what kinds of) people can use an SSVEP BCI?
Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039495 · Cited by 344
Cited by
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
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
EEG source imaging enhances the decoding of complex right-hand motor imagery tasks
Edelman, B. J., Baxter, B., & He, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2467312 · Cited by 370
Hong, K.-S., & Khan, M. J. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00035 · Cited by 267
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
Self-citation
Metadata
- Authors
- Yin, E., Zhou, Z., Jiang, J., Chen, F., Liu, Y., & Hu, D.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2014-02
- Volume
- 61
- Issue
- 2
- Pages
- 473-483
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W1994744369
- Cited by
- 167
- References
- 4
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- closed
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