The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
(C)overt attention and visual speller design in an ERP-based brain-computer interface
Open accessAbstract
BACKGROUND: In a visual oddball paradigm, attention to an event usually modulates the event-related potential (ERP). An ERP-based brain-computer interface (BCI) exploits this neural mechanism for communication. Hitherto, it was unclear to what extent the accuracy of such a BCI requires eye movements (overt attention) or whether it is also feasible for targets in the visual periphery (covert attention). Also unclear was how the visual design of the BCI can be improved to meet peculiarities of peripheral vision such as low spatial acuity and crowding. METHOD: Healthy participants (N = 13) performed a copy-spelling task wherein they had to count target intensifications. EEG and eye movements were recorded concurrently. First, (c)overt attention was investigated by way of a target fixation condition and a central fixation condition. In the latter, participants had to fixate a dot in the center of the screen and allocate their attention to a target in the visual periphery. Second, the effect of visual speller layout was investigated by comparing the symbol Matrix to an ERP-based Hex-o-Spell, a two-levels speller consisting of six discs arranged on an invisible hexagon. RESULTS: We assessed counting errors, ERP amplitudes, and offline classification performance. There is an advantage (i.e., less errors, larger ERP amplitude modulation, better classification) of overt attention over covert attention, and there is also an advantage of the Hex-o-Spell over the Matrix. Using overt attention, P1, N1, P2, N2, and P3 components are enhanced by attention. Using covert attention, only N2 and P3 are enhanced for both spellers, and N1 and P2 are modulated when using the Hex-o-Spell but not when using the Matrix. Consequently, classifiers rely mainly on early evoked potentials in overt attention and on later cognitive components in covert attention. CONCLUSIONS: Both overt and covert attention can be used to drive an ERP-based BCI, but performance is markedly lower for covert attention. The Hex-o-Spell outperforms the Matrix, especially when eye movements are not permitted, illustrating that performance can be increased if one accounts for peculiarities of peripheral vision.
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Posner, M. I. (1980) · Quarterly Journal of Experimental Psychology · doi:10.1080/00335558008248231 · Cited by 9,670 · cited 1×
“But, since the neural systems underlying overt attention shifts and covert attention shifts are not identical [52], such patients might still be able to use covert attention.”
Näätänen, R., & Picton, T. (1987) · Psychophysiology · doi:10.1111/j.1469-8986.1987.tb00311.x · Cited by 3,321
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 1×
“This confirms earlier conjectures [38,49] and it is also in accordance with [50] who showed that classifying on posterior electrodes in addition to the classical P3 sites improves classification performance.”
ResultA P300-based brain–computer interface for people with amyotrophic lateral sclerosis
Nijboer, F., Sellers, E. W., Mellinger, J., Jordan, M. A., Matuz, T., Furdea, A., Halder, S., Mochty, U., Krusienski, D. J., Vaughan, T. M., et al. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.03.034 · Cited by 633 · cited 2×
“Second, successful communication using ERP-based visual spellers has been demonstrated in ALS patients [21,22], but in progressed stages of the disease, oculomotor control can deteriorate.”
BackgroundMethodInfluentialBCI competition III: Dataset II—Ensemble of SVMs for BCI P300 speller
Rakotomamonjy, A., & Guigue, V. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.915728 · Cited by 512
A Note on Brain Actuated Spelling with the Berlin Brain-Computer Interface
Blankertz, B., Krauledat, M., Dornhege, G., Williamson, J., Murray-Smith, R., & Müller, K.-R. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-73281-5_83 · Cited by 106
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Cited by
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
Single-trial analysis and classification of ERP components — A tutorial
Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K.-R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.048 · Cited by 1,138
Self-citationHigh-speed spelling with a noninvasive brain–computer interface
Chen, X., Wang, Y., Nakanishi, M., Gao, X., Jung, T.-P., & Gao, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508080112 · Cited by 969
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
Metadata
- Authors
- Treder, M. S., & Blankertz, B.
- Venue
- Behavioral and Brain Functions
- Published
- 2010
- Volume
- 6
- Issue
- 1
- Pages
- 28
- ISSN
- 1744-9081 (ISSN-L)
- OpenAlex
- W2098735891
- Cited by
- 362
- References
- 6
- FWCI
- 25.71
- Type
- journal article
- Language
- en
- Open access
- gold
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