The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
BCI Competition 2003—Data Sets Ib and IIb: Feature Extraction From Event-Related Brain Potentials With the Continuous Wavelet Transform and the<tex>$hboxtt t$</tex>-Value Scalogram
Abstract
The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain-computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.
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Cites
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BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm
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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
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A comparison of classification techniques for the P300 Speller
Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/4/007 · Cited by 762
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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
Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., et al. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.826692 · Cited by 670
The wadsworth BCI research and development program: at home with BCI
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A Comprehensive Survey of Brain Interface Technology Designs
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Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
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Deep learning based on Batch Normalization for P300 signal detection
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A systematic deep learning model selection for p300-based brain-computer interfaces
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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
Metadata
- Authors
- Bostanov, V.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-06
- Volume
- 51
- Issue
- 6
- Pages
- 1057-1061
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2159318219
- Cited by
- 346
- References
- 17
- FWCI
- 7.36
- Type
- journal article
- Language
- en
- Open access
- closed
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