The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms
Open accessAbstract
Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the pre-movement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances.
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
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
An Introduction to the Theory of Statistics
(1911) · Nature · doi:10.1038/087345a0 · Cited by 4,394
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184
An introduction to kernel-based learning algorithms
Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., & Scholkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,473
Self-citationOptimal spatial filtering of single trial EEG during imagined hand movement
Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895946 · Cited by 2,457
Regularized Discriminant Analysis
Friedman, J. H. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478752 · Cited by 2,421
Brain-computer interface technology: a review of the first international meeting
Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., Donchin, E., Quatrano, L. A., Robinson, C. J., & Vaughan, T. M. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/tre.2000.847807 · Cited by 2,324
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
Rätsch, G., Onoda, T., & Müller, K.-R. (2001) · Machine Learning · doi:10.1023/a:1007618119488 · Cited by 1,299
Coupled hidden Markov models for complex action recognition
Brand, M., Oliver, N., & Pentland, A. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609450 · Cited by 1,052
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
Allwein, E. L., Schapire, R. E., & Singer, Y. (2000) · Cited by 914
Jacobi Angles for Simultaneous Diagonalization
Cardoso, J.-F., & Souloumiac, A. (1996) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479893259546 · Cited by 870
Designing optimal spatial filters for single-trial EEG classification in a movement task
Müller-Gerking, J., Pfurtscheller, G., & Flyvbjerg, H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(98)00038-8 · Cited by 846
Introduction to The Theory of Statistics.
Theobald, C. M., & Larson, H. J. (1974) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2345153 · Cited by 762
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
Self-citationDirect control of a computer from the human central nervous system
Kennedy, P. R., Bakay, R. A. E., Moore, M. M., Adams, K., & Goldwaithe, J. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847815 · Cited by 573
Classifying Single Trial EEG: Towards Brain Computer Interfacing
Blankertz, B., Curio, G., & Müller, K.-R. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0025 · Cited by 467
Self-citationBrain-computer interface research at the Wadsworth Center
Wolpaw, J. R., McFarland, D. J., & Vaughan, T. M. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847823 · Cited by 440
Linear and nonlinear methods for brain-computer interfaces
Muller, K.-R., Anderson, C. W., & Birch, G. E. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814484 · Cited by 360
Self-citationBabiloni, C., Carducci, F., Cincotti, F., Rossini, P. M., Neuper, C., Pfurtscheller, G., & Babiloni, F. (1999) · NeuroImage · doi:10.1006/nimg.1999.0504 · Cited by 353
Information transfer rate in a five-classes brain-computer interface
Obermaier, B., Neuper, C., Guger, C., & Pfurtscheller, G. (2001) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/7333.948456 · Cited by 337
Event-related desynchronization and movement-related cortical potentials on the ECoG and EEG
Toro, C., Deuschl, G., Thatcher, R., Sato, S., Kufta, C., & Hallett, M. (1994) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/0168-5597(94)90126-0 · Cited by 316
Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task
Laubach, M., Wessberg, J., & Nicolelis, M. A. L. (2000) · Nature · doi:10.1038/35014604 · Cited by 272
Event-related desynchronization and movement-related cortical potentials on the ECoG and EEG
TORO, C., DEUSCHL, G., THATCHER, R., SATO, S., KUFTA, C., & HALLETT, M. (1994) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(94)00177-m · Cited by 255
Blankertz, B., Dornhege, G., Schafer, C., Krepki, R., Kohlmorgen, J., Muller, K.-R., Kunzmann, V., Losch, F., & Curio, G. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814456 · Cited by 226
Self-citationMap learning and high-speed navigation in RHINO
Thrun, S., Bücken, A., Burgard, W., Fox, D., Fröhlinghaus, T., Hennig, D., Hofmann, T., Krell, M. B., & Schmidt, T. T. (1998) · Cited by 214
Morgan, N., & Bourlard, H. (1995) · IEEE Signal Processing Magazine · doi:10.1109/79.382443 · Cited by 212
Pineda, J. A., Allison, B. Z., & Vankov, A. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847822 · Cited by 208
Linear Spatial Integration for Single-Trial Detection in Encephalography
Parra, L., Alvino, C., Tang, A., Pearlmutter, B., Yeung, N., Osman, A., & Sajda, P. (2002) · NeuroImage · doi:10.1006/nimg.2002.1212 · Cited by 194
Mental representations of movements. Brain potentials associated with imagination of eye movements
Höllinger, P., Beisteiner, R., Lang, W., Lindinger, G., & Berthoz, A. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(98)00042-x · Cited by 193
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Thrun, S., Burgard, W., & Fox, D. (1998) · Autonomous Robots · doi:10.1023/a:1008806205438 · Cited by 189
Mental representations of movements. Brain potentials associated with imagination of hand movements
Beisteiner, R., Höllinger, P., Lindinger, G., Lang, W., & Berthoz, A. (1995) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/0168-5597(94)00226-5 · Cited by 174
On the Relationship Between Joint Angular Velocity and Motor Cortical Discharge During Reaching
Reina, G. A., Moran, D. W., & Schwartz, A. B. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.85.6.2576 · Cited by 145
Cognitive tasks for driving a brain-computer interfacing system: a pilot study
Curran, E., Sykacek, P., Stokes, M. J., Roberts, S., Penny, W. D., Johnsrude, I. S., & Owen, A. M. (2004) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.821372 · Cited by 130
Automatic differentiation of multichannel EEG signals
Peters, B. O., Pfurtscheller, G., & Flyvbjerg, H. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.900270 · Cited by 123
Dornhege, G., Blankertz, B., Curio, G., & Müller, K. (2002) · Cited by 63
Self-citationDornhege, G., Blankertz, B., & Curio, G. (2003) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/cne.2003.1196898 · Cited by 51
Self-citationA LINEAR LEAST-SQUARES ALGORITHM FOR JOINT DIAGONALIZATION
Ziehe, A., & Laskov, P. (2003) · Cited by 37
Cited by
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080
Self-citationFilter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008) · 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) · doi:10.1109/ijcnn.2008.4634130 · Cited by 1,514
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
Self-citationFilter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b
Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00039 · Cited by 1,264
Metadata
- Authors
- Dornhege, G., Blankertz, B., Curio, G., & Muller, K.-R.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-06
- Volume
- 51
- Issue
- 6
- Pages
- 993-1002
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2163756472
- Cited by
- 631
- References
- 38
- FWCI
- 12.32
- Type
- journal article
- Language
- en
- Open access
- green
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.
