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ZERO-TRAIN-BCI

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

Total Cost €

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EC-Contrib. €

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Partnership

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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 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
List of publications.
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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