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{Wei:2021:10.1109/NER49283.2021.9441085,
author = {Wei, X and Ortega, P and Faisal, A},
doi = {10.1109/NER49283.2021.9441085},
pages = {1--4},
publisher = {IEEE},
title = {Inter-subject deep transfer learning for motor imagery EEG decoding},
url = {http://dx.doi.org/10.1109/NER49283.2021.9441085},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Convolutional  neural  networks  (CNNs)  have  be-come  a  powerful  technique  to  decode  EEG  and  have  become the   benchmark   for   motor   imagery   EEG   Brain-Computer-Interface  (BCI)  decoding.  However,  it  is  still  challenging  to train  CNNs  on  multiple  subjects’  EEG  without  decreasing individual performance. This is known as the negative transfer problem,   i.e.   learning   from   dissimilar   distributions   causes CNNs  to  misrepresent  each  of  them  instead  of  learning  a richer  representation.  As  a  result,  CNNs  cannot  directly  use multiple subjects’ EEG to enhance model performance directly. To  address  this  problem,  we  extend  deep  transfer  learning techniques to the EEG multi-subject training case. We propose a multi-branch deep transfer network, the Separate-Common-Separate  Network  (SCSN)  based  on  splitting  the  network’s feature  extractors  for  individual  subjects.  We  also  explore  the possibility of applying Maximum-mean discrepancy (MMD) to the SCSN (SCSN-MMD) to better align distributions of features from  individual  feature  extractors.  The  proposed  network  is evaluated  on  the  BCI  Competition  IV  2a  dataset  (BCICIV2adataset)  and  our  online  recorded  dataset.  Results  show  that the  proposed  SCSN  (81.8%,  53.2%)  and  SCSN-MMD  (81.8%,54.8%) outperformed the benchmark CNN (73.4%, 48.8%) on both  datasets  using  multiple  subjects.  Our  proposed  networks show  the  potential  to  utilise  larger  multi-subject  datasets  to train  an  EEG  decoder  without  being  influenced  by  negative transfer.
AU  - Wei,X
AU  - Ortega,P
AU  - Faisal,A
DO  - 10.1109/NER49283.2021.9441085
EP  - 4
PB  - IEEE
PY  - 2021///
SP  - 1
TI  - Inter-subject deep transfer learning for motor imagery EEG decoding
UR  - http://dx.doi.org/10.1109/NER49283.2021.9441085
UR  - http://hdl.handle.net/10044/1/88239
ER  -
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