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ZERO-TRAIN-BCI

Combining constrained based learning and transfer learning to facilitate Zero-training Brain-Computer Interfacing

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

ZERO-TRAIN-BCI project word cloud

Explore the words cloud of the ZERO-TRAIN-BCI project. It provides you a very rough idea of what is the project "ZERO-TRAIN-BCI" about.

algorithms erp recorded paradigms paralysed patients imagery interface intention accuracy reducing interfaces transfer despite data gaming prototypes restore device brain decoder community limited nowadays restoring prostheses mental desired relied true expand function taught voluntary unfortunately labelled interpret session invested calibration mi modulation obviously rare ported event external 15 bci signals times span computer learning minutes bcis minimum initial frequent perform machine motor hence zero constraint completely effort stationarity train algorithm instructed training communication re medical designed idea

Project "ZERO-TRAIN-BCI" data sheet

The following table provides information about the project.

Coordinator	TECHNISCHE UNIVERSITAT BERLIN Organization address address: STRASSE DES 17 JUNI 135 city: BERLIN postcode: 10623 website: www.tu-berlin.de contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
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.

#	participants	country	role	EC contrib. [€]
1	TECHNISCHE UNIVERSITAT BERLIN TECHNISCHE UNIVERSITAT BERLIN Organization address address: STRASSE DES 17 JUNI 135 city: BERLIN postcode: 10623 website: www.tu-berlin.de contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	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

List of 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, RÃ¼diger Rupp, Giulio Ruffini, Douglas K. R. Robinson, Nick F. Ramsey, Anton Nijholt, Gernot MÃ¼ller-Putz, Dennis J. McFarland, Donatella Mattia, Brent J. Lance, Pieter-Jan Kindermans, IÃ±aki 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 Brainâ€“Computer Interface Meeting: brainâ€“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 HÃ¼bner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert MÃ¼ller, 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	HÃ¼bner D., Verhoeven T., Schmid K., MÃ¼ller 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 brainâ€“computer interface: exploring the applicationâ€“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	HÃ¼bner 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. SchÃ¼tt, Maximilian Alber, Klaus-Robert MÃ¼ller, Sven DÃ¤hne 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 HÃ¼bner, M Tangermann, K R MÃ¼ller, 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 HÃ¼bner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert MÃ¼ller, 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	HÃ¼bner 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 SchÃ¼tt, Klaus-Robert MÃ¼ller, Sven DÃ¤hne 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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