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Parallel man-machine training in development of EEG-based cursor control
Abstract
A new parallel man-machine training approach to brain-computer interface (BCI) succeeded through a unique application of machine learning methods. The BCI system could train users to control an animated cursor on the computer screen by voluntary electroencephalogram (EEG) modulation. Our BCI system requires only two to four electrodes, and has a relatively short training time for both the user and the machine. Moving the cursor in one dimension, our subjects were able to hit 100% of randomly selected targets, while in two dimensions, accuracies of approximately 63% and 76% was achieved with our two subjects.
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Cited by
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
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Wolpaw, J. R., & McFarland, D. J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403504101 · Cited by 1,512
Brain–computer interfaces in neurological rehabilitation
Daly, J. J., & Wolpaw, J. R. (2008) · The Lancet Neurology · doi:10.1016/s1474-4422(08)70223-0 · Cited by 1,149
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
Metadata
- Authors
- Kostov, A., & Polak, M.
- Venue
- IEEE Transactions on Rehabilitation Engineering
- Published
- 2000-06
- Volume
- 8
- Issue
- 2
- Pages
- 203-205
- ISSN
- 1063-6528 (ISSN-L)
- OpenAlex
- W2135454142
- Cited by
- 174
- References
- 0
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
