ZERO-TRAIN-BCI
Combining constrained based learning and transfer learning to facilitate Zero-training Brain-Computer Interfacing
Total Cost €
0EC-Contrib. €
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Project "ZERO-TRAIN-BCI" data sheet
The following table provides information about the project.
| Coordinator |
TECHNISCHE UNIVERSITAT BERLIN
Organization address contact info |
| Coordinator Country | Germany [DE] |
| Total cost | 159˙460 € |
| EC max contribution | 159˙460 € (100%) |
| Programme |
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility) |
| Code Call | H2020-MSCA-IF-2014 |
| Funding Scheme | MSCA-IF-EF-ST |
| Starting year | 2015 |
| Duration (year-month-day) | from 2015-04-01 to 2017-03-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | TECHNISCHE UNIVERSITAT BERLIN | DE (BERLIN) | coordinator | 159˙460.00 |
Map
Project objective
Brain-Computer Interfaces (BCI) enable the user to control a computer or external device directly through his or her brain signals. This interface can be used for restoring communication for completely paralysed patients, to restore motor function through prostheses but also for non-medical applications such as gaming. The initial BCI prototypes relied on voluntary modulation of the brain signals to control the computer. Nowadays, it is the computer that is taught via machine learning algorithms how to interpret the brain signals and this reduced the training times to 15-30 minutes for a calibration session. During such a calibration session, the user is instructed to perform specific mental tasks, such that the recorded brain signals can be labelled with the user’s intention. This labelled data-set is then used to train the machine learning algorithm. Unfortunately, due to non-stationarity in the observed brain signals, re-calibration is often required to ensure the accuracy of the interface. Obviously, frequent (re-)calibration is not desired. Especially for patients with a limited attention span, it must be reduced to a minimum. The BCI community has invested much effort in reducing the need for calibration data. However, despite this effort, true zero-training BCIs that do not require calibration are rather rare. For the Event Related Potential (ERP) based BCI, we were able to develop such a true zero-training BCI based on the concepts of constraint based learning and transfer learning. That decoder was designed specifically for the ERP based BCI and cannot be ported directly to other paradigms. Hence, the goal in this project is to expand on this idea and to develop a true-zero training Motor Imagery (MI) based BCI by investigating novel machine learning methods based on constraint based learning and transfer learning.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2017 |
Jane E. Huggins, Christoph Guger, Mounia Ziat, Thorsten O. Zander, Denise Taylor, Michael Tangermann, Aureli Soria-Frisch, John Simeral, Reinhold Scherer, Rüdiger Rupp, Giulio Ruffini, Douglas K. R. Robinson, Nick F. Ramsey, Anton Nijholt, Gernot Müller-Putz, Dennis J. McFarland, Donatella Mattia, Brent J. Lance, Pieter-Jan Kindermans, Iñaki Iturrate, Christian Herff, Disha Gupta, An H. Do, Jennifer L. Collinger, Ricardo Chavarriaga, Steven M. Chase, Martin G. Bleichner, Aaron Batista, Charles W. Anderson, Erik J. Aarnoutse Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future published pages: 1-34, ISSN: 2326-263X, DOI: 10.1080/2326263X.2016.1275488 |
Brain-Computer Interfaces | 2019-07-24 |
| 2017 |
Pieter-Jan Kindermans, David Hübner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert Müller, Michael Tangermann Making Brain-Computer Interfaces robust, reliable and adaptive with Learning from Label Proportions published pages: , ISSN: , DOI: |
NIPS Workshop on Reliable Machine Learning in the Wild | 2019-07-24 |
| 2016 |
T. Verhoeven, P.J. Kindermans, S. Vandenberghe, J. Dambre Reducing BCI calibration time with transfer learning: a shrinkage approach published pages: , ISSN: , DOI: 10.3217/978-3-85125-467-9-133 |
Proceedings of the 6th International Brain-Computer Interface Meeting, organized by the BCI Society | 2019-07-24 |
| 2017 |
Iryna Korshunova, Pieter-Jan Kindermans, Jonas Degrave, Thibault Verhoeven, Benjamin H. Brinkmann, Joni Dambre Towards improved design and evaluation of epileptic seizure predictors published pages: 1-1, ISSN: 0018-9294, DOI: 10.1109/TBME.2017.2700086 |
IEEE Transactions on Biomedical Engineering | 2019-07-24 |
| 2017 |
Hübner D., Verhoeven T., Schmid K., Müller K.-R., Tangermann M., Kindermans P.-J Learning from label proportions in BCI -- A symbiotic design for stimulus presentation and signal decoding published pages: , ISSN: , DOI: |
1st Neuroadaptive Technology Conference Berlin | 2019-07-24 |
| 2015 |
T Verhoeven, P Buteneers, JR Wiersema, J Dambre, PJ Kindermans Towards a symbiotic brain–computer interface: exploring the application–decoder interaction published pages: 66027, ISSN: 1741-2560, DOI: 10.1088/1741-2560/12/6/066027 |
Journal of Neural Engineering 12/6 | 2019-07-24 |
| 2017 |
Hübner D., Kindermans P.-J., Verhoeven T., Tangermann M. Improving learning from label proportions by reducing the feature dimensionality published pages: , ISSN: , DOI: |
Seventh International Brain-Computer Interface Conference 2017 | 2019-07-24 |
| 2017 |
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Sven Dähne PatternNet and PatternLRP -- Improving the interpretability of neural networks published pages: , ISSN: , DOI: |
Submitted to NIPS 2017 | 2019-07-24 |
| 2016 |
Di Wu, Lionel Pigou, Pieter-Jan Kindermans, Nam Do-Hoang Le, Ling Shao, Joni Dambre, Jean-Marc Odobez Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition published pages: 1583-1597, ISSN: 0162-8828, DOI: 10.1109/TPAMI.2016.2537340 |
IEEE Transactions on Pattern Analysis and Machine Intelligence 38/8 | 2019-07-24 |
| 2017 |
T Verhoeven, D Hübner, M Tangermann, K R Müller, J Dambre, P J Kindermans Improving zero-training brain-computer interfaces by mixing model estimators published pages: 36021, ISSN: 1741-2560, DOI: 10.1088/1741-2552/aa6639 |
Journal of Neural Engineering 14/3 | 2019-07-24 |
| 2017 |
David Hübner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert Müller, Michael Tangermann, Pieter-Jan Kindermans Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees published pages: e0175856, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0175856 |
PLOS ONE 12/4 | 2019-07-24 |
| 2017 |
Hübner D., Verhoeven T., Kindermans P.-J., Tangermann M. Mixing Two Unsupervised Estimators for Event-Related Potential decoding: An Online Evaluation published pages: , ISSN: , DOI: |
Seventh International Brain-Computer Interface Conference 2017 | 2019-07-24 |
| 2016 |
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Sven Dähne Investigating the influence of noise and distractors on the interpretation of neural networks published pages: , ISSN: , DOI: |
NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems | 2019-07-24 |
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