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{Denghao:2021:10.1109/NER49283.2021.9441077,
author = {Denghao, L and Ortega, San Miguel P and Wei, X and Faisal, AA},
doi = {10.1109/NER49283.2021.9441077},
publisher = {IEEE},
title = {Model-agnostic meta-learning for EEG motor imagery decoding in brain-computer-interfacing},
url = {http://dx.doi.org/10.1109/NER49283.2021.9441077},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We  introduce  here  the  idea  of  Meta  Learning for  training  EEG  BCI  decoders.  Meta  Learning  is  a  way  of training  machine  learning  systems  so  they  learn  to  learn.  We apply  here  meta  learning  to  a  simple  Deep  Learning  BCI architecture  and  compare  it  to  transfer  learning  on  the  same architecture.  Our  Meta  learning  strategy  operates  by  finding optimal parameters for the BCI decoder so that it can quickly generalise  between  different  users  and  recording  sessions  –thereby also generalising to new users or new sessions quickly. We tested our algorithm on the Physionet EEG motor imagery dataset.  Our  approach  increased  motor  imagery  classification accuracy  between  60  to  80%,  outperforming  other  algorithms under the little-data condition. We believe that establishing the meta  learning  or  learning-to-learn  approach  will  help  neural engineering   and   human   interfacing   with   the   challenges   of quickly  setting  up  decoders  of  neural  signals  to  make  them more  suitable  for  daily-life.
AU  - Denghao,L
AU  - Ortega,San Miguel P
AU  - Wei,X
AU  - Faisal,AA
DO  - 10.1109/NER49283.2021.9441077
PB  - IEEE
PY  - 2021///
TI  - Model-agnostic meta-learning for EEG motor imagery decoding in brain-computer-interfacing
UR  - http://dx.doi.org/10.1109/NER49283.2021.9441077
UR  - https://ieeexplore.ieee.org/document/9441077
UR  - http://hdl.handle.net/10044/1/87614
ER  -
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