The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Visual Spatial Attention Control in an Independent Brain-Computer Interface
Open accessAbstract
This paper presents a novel brain computer interface (BCI) design employing visual evoked potential (VEP) modulations in a paradigm involving no dependency on peripheral muscles or nerves. The system utilizes electrophysiological correlates of visual spatial attention mechanisms, the self-regulation of which is naturally developed through continuous application in everyday life. An interface involving real-time biofeedback is described, demonstrating reduced training time in comparison to existing BCIs based on self-regulation paradigms. Subjects were cued to covertly attend to a sequence of letters superimposed on a flicker stimulus in one visual field while ignoring a similar stimulus of a different flicker frequency in the opposite visual field. Classification of left/right spatial attention is achieved by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli. Six out of eleven physically and neurologically healthy subjects demonstrate reliable control in binary decision-making, achieving at least 75% correct selections in at least one of only five sessions, each of approximately 12-min duration. The highest-performing subject achieved over 90% correct selections in each of four sessions. This independent BCI may provide a new method of real-time interaction for those with little or no peripheral control, with the added advantage of requiring only brief training.
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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
On the use of windows for harmonic analysis with the discrete Fourier transform
Harris, F. J. (1978) · Proceedings of the IEEE · doi:10.1109/proc.1978.10837 · Cited by 7,326
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
Event-related brain potentials in the study of visual selective attention
Hillyard, S. A., & Anllo-Vento, L. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.3.781 · Cited by 1,525
The thought translation device (TTD) for completely paralyzed patients
Birbaumer, N., Kubler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., Iversen, I., Kotchoubey, B., Neumann, N., & Flor, H. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847812 · Cited by 493
Cited by
Brain–machine interfaces: past, present and future
Lebedev, M. A., & Nicolelis, M. A. L. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.07.004 · Cited by 1,875
High-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
Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r03 · Cited by 907
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
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
Metadata
- Authors
- Kelly, S. P., Lalor, E. C., Finucane, C., McDarby, G., & Reilly, R. B.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2005-09
- Volume
- 52
- Issue
- 9
- Pages
- 1588-1596
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2133022542
- Cited by
- 176
- References
- 5
- FWCI
- 4.36
- Type
- journal article
- Language
- en
- Open access
- green
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