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Covariance matrices of noisy multichannel electroencephalogram time series data are hard to estimate due to high dimensionality.
Stationarity of the human electroencephalogram
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A review of classification algorithms for EEG-based brain–computer interfaces
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An efficient P300-based brain–computer interface for disabled subjects
Hoffmann, U., Vesin, J.-M., Ebrahimi, T., and Diserens, K · 2008
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Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., and Edlinger, G · 2009
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xDAWN algorithm to enhance evoked potentials: application to brain–computer interface
Rivet, B., Souloumiac, A., Attina, V., and Gibert, G · 2009
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Motivation modulates the P300 amplitude during brain–computer interface use
Kleih, S., Nijboer, F., Halder, S., and Kübler, A · 2010
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A new auditory multi-class brain-computer interface paradigm: Spatial hearing as an informative cue
Schreuder, M., Blankertz, B., and Tangermann, M · 2010
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Blankertz, B., Lemm, S., Treder, M., Haufe, S., and Müller, K.-R · 2011
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Natural stimuli improve auditory BCIs with respect to ergonomics and performance
Höhne, J., Krenzlin, K., Dähne, S., and Tangermann, M · 2012
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A Bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI
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A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder
Lim, C. G., Lee, T. S., Guan, C., Fung, D. S. S., Zhao, Y., Teng, S. S. W., Zhang, H., and Krishnan, K. R. R · 2012
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Electric and magnetic fields produced by the brain
Nunez, P. L · 2012
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BCIs that use P300 event-related potentials
Sellers, E. W., Arbel, Y., and Donchin, E · 2012
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Covariance matrix estimation for stationary time series
Xiao, H., Wu, W. B., et al · 2012
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Brain-computer interface in stroke rehabilitation
Ang, K. K. and Guan, C · 2013
Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees
Hübner, D., Verhoeven, T., Schmid, K., Müller, K.-R., Tangermann, M., and Kindermans, P.-J · 2017
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Oracle approximating shrinkage estimator based cooperative spectrum sensing for dense cognitive small cell network
Zhao, M., Guo, C., Feng, C., and Chen, S · 2017
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Unsupervised learning for brain-computer interfaces based on event-related potentials: Review and online comparison
Hübner, D., Verhoeven, T., Müller, K.-R., Kindermans, P.-J., and Tangermann, M · 2018
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MOABB: trustworthy algorithm benchmarking for BCIs
Jayaram, V. and Barachant, A · 2018
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Guess what I attend: Interface-free object selection using brain signals
Kolkhorst, H., Tangermann, M., and Burgard, W · 2018
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Shrinkage approach for spatiotemporal EEG covariance matrix estimation
Beltrachini, L., von Ellenrieder, N., and Muravchik, C. H · 2013
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Covariance and precision matrix estimation for high-dimensional time series
Chen, X., Xu, M., Wu, W. B., et al · 2013
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Matrix Computations
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Banding, Tapering and Thresholding , volume 882, chapter 6, pp. 141–151
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Influence of P300 latency jitter on event related potential-based brain–computer interface performance
Aricò, P., Aloise, F., Schettini, F., Salinari, S., Mattia, D., and Cincotti, F · 2014
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A plug&play P300 BCI using information geometry
Barachant, A. and Congedo, M · 2014
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A novel P300 BCI speller based on the triple RSVP paradigm
Lin, Z., Zhang, C., Zeng, Y., Tong, L., and Yan, B · 2018
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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., and Yger, F · 2018
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Time-variant linear discriminant analysis improves hand gesture and finger movement decoding for invasive brain-computer interfaces
Gruenwald, J., Znobishchev, A., Kapeller, C., Kamada, K., Scharinger, J., and Guger, C · 2019
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EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
Lee, M.-H., Kwon, O.-Y., Kim, Y.-J., Kim, H.-K., Lee, Y.-E., Williamson, J., Fazli, S., and Lee, S.-W · 2019
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Spatial filters for auditory evoked potentials transfer between different experimental conditions
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From Supervised to Unsupervised Machine Learning Methods for Brain-Computer Interfaces and Their Application in Language Rehabilitation
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Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
Hashemi, A., Gao, Y., Cai, C., Ghosh, S., Müller, K.-R., Nagarajan, S., and Haufe, S · 2021
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Who willed it? Decreasing frustration by manipulating perceived control through fabricated input for stroke rehabilitation BCI games
Hougaard, B. I., Rossau, I. G., Czapla, J. J., Miko, M. A., Skammelsen, R. B., Knoche, H., and Jochumsen, M · 2021
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Improving covariance matrices derived from tiny training datasets for the classification of event-related potentials with linear discriminant analysis
Sosulski, J., Kemmer, J.-P., and Tangermann, M · 2021
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Aphasia recovery by language training using a brain-computer interface – a proof-of-concept study
Musso, M., Hübner, D., Schwarzkopf, S., Bernodusson, M., LeVan, P., Weiller, C., and Tangermann, M · 2022
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