Imperial College London
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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{Ortega:2018:10.1109/BIOROB.2018.8487644,
author = {Ortega, San Miguel P and Colas, C and Faisal, A},
doi = {10.1109/BIOROB.2018.8487644},
publisher = {IEEE},
title = {Compact convolutional neural networks for multi-class, personalised, Closed-loop EEG-BCI},
url = {http://dx.doi.org/10.1109/BIOROB.2018.8487644},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - For many people suffering from motor disabilities,assistive  devices  controlled  with  only  brain  activity  are  theonly   way   to   interact   with   their   environment   [1].   Naturaltasks  often  require  different  kinds  of  interactions,  involvingdifferent  controllers  the  user  should  be  able  to  select  in  aself-paced   way.   We   developed   a   Brain-Computer   Interface(BCI)  allowing  users  to  switch  between  four  control  modesin  a  self-paced  way  in  real-time.  Since  the  system  is  devisedto  be  used  in  domestic  environments  in  a  user-friendly  way,we selected non-invasive electroencephalographic (EEG) signalsand  convolutional  neural  networks  (CNNs),  known  for  theirability  to  find  the  optimal  features  in  classification  tasks.  Wetested  our  system  using  the  Cybathlon  BCI  computer  game,which embodies all the challenges inherent to real-time control.Our  preliminary  results  show  that  an  efficient  architecture(SmallNet),  with  only  one  convolutional  layer,  can  classify  4mental activities chosen by the user. The BCI system is run andvalidated online. It is kept up-to-date through the use of newlycollected  signals  along  playing,  reaching  an  online  accuracyof47.6%where  most  approaches  only  report  results  obtainedoffline. We found that models trained with data collected onlinebetter predicted the behaviour of the system in real-time. Thissuggests  that  similar  (CNN  based)  offline  classifying  methodsfound in the literature might experience a drop in performancewhen  applied  online.  Compared  to  our  previous  decoder  ofphysiological signals relying on blinks, we increased by a factor2  the  amount  of  states  among  which  the  user  can  transit,bringing  the  opportunity  for  finer  control  of  specific  subtaskscomposing  natural  grasping  in  a  self-paced  way.  Our  resultsare  comparable  to  those  showed  at  the  Cybathlon’s  BCI  Racebut  further  imp
AU  - Ortega,San Miguel P
AU  - Colas,C
AU  - Faisal,A
DO  - 10.1109/BIOROB.2018.8487644
PB  - IEEE
PY  - 2018///
TI  - Compact convolutional neural networks for multi-class, personalised, Closed-loop EEG-BCI
UR  - http://dx.doi.org/10.1109/BIOROB.2018.8487644
UR  - http://hdl.handle.net/10044/1/64042
ER  -
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