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Authors: Eduardo G. Ponferrada 1 ; Anastasia Sylaidi 1 and A. Aldo Faisal 2

Affiliations: 1 Department of Bioengineering, Imperial College London, London and U.K. ; 2 Department of Bioengineering, Imperial College London, London, U.K., Department of Computing, Imperial College London, London, U.K., Data Science Institute, London and U.K.

ISBN: 978-989-758-326-1

Keyword(s): Brain-Computer Interfaces, Machine Learning, Cybathlon.

Related Ontology Subjects/Areas/Topics: Applications ; Assistive Technologies ; Biomedical Engineering ; Biomedical Instruments and Devices ; Brain-Computer Interfaces ; Devices ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Neural Rehabilitation ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Software Engineering

Abstract: Neuromotor diseases such as Amyotrophic Lateral Sclerosis or Multiple Sclerosis affect millions of people throughout the globe by obstructing body movement and thereby any instrumental interaction with the world. Brain Computer Interfaces (BCIs) hold the premise of re-routing signals around the damaged parts of the nervous system to restore control. However, the field still faces open challenges in training and practical implementation for real-time usage which hampers its impact on patients. The Cybathlon Brain-Computer Interface Race promotes the development of practical BCIs to facilitate clinical adoption. In this work we present a competitive and data-efficient BCI system to control the Cybathlon video game using motor imageries. The platform achieves substantial performance while requiring a relatively small amount of training data, thereby accelerating the training phase. We employ a static band-pass filter and Common Spatial Patterns learnt using supervised machine learning te chniques to enable the discrimination between different motor imageries. Log-variance features are extracted from the spatio-temporally filtered EEG signals to fit a Logistic Regression classifier, obtaining satisfying levels of decoding accuracy. The systems performance is evaluated online, on the first version of the Cybathlon Brain Runners game, controlling 3 commands with up to 60.03% accuracy using a two-step hierarchical classifier. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ponferrada, E.; Sylaidi, A. and Faisal, A. (2018). Data-efficient Motor Imagery Decoding in Real-time for the Cybathlon Brain-Computer Interface Race. In Proceedings of the 6th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-758-326-1, pages 21-32. DOI: 10.5220/0006962400210032

@conference{neurotechnix18,
author={Eduardo G. Ponferrada. and Anastasia Sylaidi. and A. Aldo Faisal.},
title={Data-efficient Motor Imagery Decoding in Real-time for the Cybathlon Brain-Computer Interface Race},
booktitle={Proceedings of the 6th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2018},
pages={21-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006962400210032},
isbn={978-989-758-326-1},
}

TY - CONF

JO - Proceedings of the 6th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - Data-efficient Motor Imagery Decoding in Real-time for the Cybathlon Brain-Computer Interface Race
SN - 978-989-758-326-1
AU - Ponferrada, E.
AU - Sylaidi, A.
AU - Faisal, A.
PY - 2018
SP - 21
EP - 32
DO - 10.5220/0006962400210032

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