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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A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
The mental prosthesis: assessing the speed of a P300-based brain-computer interface
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BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm
Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., & Ritter, H. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.826698 · Cited by 496
Multivariate statistical inference and applications
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Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial
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Slow cortical potentials and behavior
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Cited by
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
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
Review of the BCI Competition IV
Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., et al. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00055 · Cited by 1,288
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
An efficient P300-based brain–computer interface for disabled subjects
Hoffmann, U., Vesin, J.-M., Ebrahimi, T., & Diserens, K. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.005 · Cited by 859
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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- CoChat. (2026). Citation check: 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. https://app.cochat.ai/papers/10.1109/tbme.2004.826702
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- Bostanov, V. (2004). 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. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2004.826702
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