Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Ponferrada:2018:10.5220/0006962400210032,
author = {Ponferrada, EG and Sylaidi, A and Aldo, Faisal A},
doi = {10.5220/0006962400210032},
pages = {21--32},
title = {Data-efficient motor imagery decoding in real-time for the cybathlon brain-computer interface race},
url = {http://dx.doi.org/10.5220/0006962400210032},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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 techniques 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.
AU  - Ponferrada,EG
AU  - Sylaidi,A
AU  - Aldo,Faisal A
DO  - 10.5220/0006962400210032
EP  - 32
PY  - 2018///
SP  - 21
TI  - Data-efficient motor imagery decoding in real-time for the cybathlon brain-computer interface race
UR  - http://dx.doi.org/10.5220/0006962400210032
ER  -
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