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BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm
Open accessAbstract
We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.
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A comparison of classification techniques for the P300 Speller
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Toward enhanced p300 speller performance
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“Our analyses focused on four electrodes, (Cz, Pz, Po7, and Po8), since these reliably capture most of the P300 energy in BCI applications (Kaper et al., 2004; Krusienski et al., 2008).”
BackgroundMethodBCI competition III: Dataset II—Ensemble of SVMs for BCI P300 speller
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An improved P300-based brain-computer interface
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EEG feature extraction based on wavelet packet decomposition for brain computer interface
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The wadsworth BCI research and development program: at home with BCI
Vaughan, T. M., McFarland, D. J., Schalk, G., Sarnacki, W. A., Krusienski, D. J., Sellers, E. W., & Wolpaw, J. R. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875577 · Cited by 334
A Comprehensive Survey of Brain Interface Technology Designs
Mason, S. G., Bashashati, A., Fatourechi, M., Navarro, K. F., & Birch, G. E. (2007) · Annals of Biomedical Engineering · doi:10.1007/s10439-006-9170-0 · Cited by 299
Sparse Bayesian Classification of EEG for Brain–Computer Interface
Zhang, Y., Zhou, G., Jin, J., Zhao, Q., Wang, X., & Cichocki, A. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2476656 · Cited by 264
Heartbeat Time Series Classification With Support Vector Machines
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Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI
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Unsupervised Brain Computer Interface Based on Intersubject Information and Online Adaptation
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Capsule Network for ERP Detection in Brain-Computer Interface
Ma, R., Yu, T., Zhong, X., Yu, Z. L., Li, Y., & Gu, Z. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3070327 · Cited by 47
Performance Enhancement of P300 Detection by Multiscale-CNN
Wang, H., Pei, Z., Xu, L., Xu, T., Bezerianos, A., Sun, Y., & Li, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3067943 · Cited by 35
P300 event-related potential detection using one-dimensional convolutional capsule networks
Liu, X., Xie, Q., Lv, J., Huang, H., & Wang, W. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114701 · Cited by 23
P300 Detection Using Ensemble of SVM for Brain-Computer Interface Application
Kundu, S., & Ari, S. (2018) · 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2018.8493903 · Cited by 8
Metadata
- Authors
- Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., & Ritter, H.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-06
- Volume
- 51
- Issue
- 6
- Pages
- 1073-1076
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2100470503
- Cited by
- 496
- References
- 2
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- green
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