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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
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
Donchin, E., Spencer, K. M., & Wijesinghe, R. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847808 · Cited by 1,222
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
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
(1998) · Choice Reviews Online · doi:10.5860/choice.35-5714 · Cited by 359
Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial
Samar, V. J., Bopardikar, A., Rao, R., & Swartz, K. (1999) · Brain and Language · doi:10.1006/brln.1998.2024 · Cited by 355
Slow cortical potentials and behavior
Rockstroh, B. S. (1989) · Urban & Schwarzenberg eBooks · Cited by 331
Hinterberger, T., Kübler, A., Kaiser, J., Neumann, N., & Birbaumer, N. (2003) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00411-x · Cited by 164
Bostanov, V., & Kotchoubey, B. (2004) · Psychophysiology · doi:10.1111/j.1469-8986.2003.00142.x · Cited by 164
Self-citationEffects of task difficulty on evoked gamma activity and ERPs in a visual discrimination task
Senkowski, D., & Herrmann, C. S. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00266-3 · Cited by 153
Time–Frequency Analysis of Single-Sweep Event-Related Potentials by Means of Fast Wavelet Transform
Demıralp, T., Yordanova, J., Kolev, V., Ademoğlu, A., Devrim, M., & Samar, V. J. (1999) · Brain and Language · doi:10.1006/brln.1998.2028 · Cited by 111
Improving Transfer Rates in Brain Computer Interfacing: A Case Study
Meinicke, P., Kaper, M., Hoppe, F., Heumann, M., & Ritter, H. J. (2002) · Cited by 89
Heinrich, H., Dickhaus, H., Rothenberger, A., Heinrich, V., & Moll, G. H. (1999) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.771199 · Cited by 52
A comparison of different methods for estimating single-trial P300 latencies
Smulders, F. T. Y., Kenemans, J. L., & Kok, A. (1994) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/0168-5597(94)90051-5 · Cited by 52
(1998) · Cited by 52
The Thought Translation Device: structure of a multimodal brain-computer communication system
Hinterberger, T., Mellinger, J., & Birbaumer, N. (2004) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/cne.2003.1196900 · Cited by 32
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
