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{Ortega:2021:10.1109/NER49283.2021.9441409,
author = {Ortega, San Miguel P and Zhao, T and Faisal, AA},
doi = {10.1109/NER49283.2021.9441409},
publisher = {IEEE},
title = {Deep real-time decoding of bimanual grip force from EEG & fNIRS},
url = {http://dx.doi.org/10.1109/NER49283.2021.9441409},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Non-invasive  cortical  neural  interfaces  have  only achieved   modest   performance   in   cortical   decoding   of   limb movements   and   their   forces,   compared   to   invasive   brain-computer  interfaces  (BCIs).  While  non-invasive  methodologies    are  safer,  cheaper  and  vastly  more  accessible  technologies,       signals suffer from either poor resolution in the space domain(EEG) or the temporal domain (BOLD signal of functional Near Infrared Spectroscopy, fNIRS). The non-invasive BCI decoding of  bimanual  force  generation  and  the  continuous  force  signal has not been realised before and so we introduce an isometric grip  force  tracking  task  to  evaluate  the  decoding.  We  find that  combining  EEG  and  fNIRS  using  deep  neural  networks   works better than linear models to decode continuous grip force modulations produced by the left and the right hand. Our multi-modal  deep  learning  decoder  achieves  55.2  FVAF[%]  in  force reconstruction  and  improves  the  decoding  performance  by  at least  15%  over  each  individual  modality.  Our  results  show  away  to  achieve  continuous  hand  force  decoding  using  cortical signals  obtained  with  non-invasive  mobile  brain  imaging  has immediate impact for rehabilitation, restoration and consumer applications.
AU  - Ortega,San Miguel P
AU  - Zhao,T
AU  - Faisal,AA
DO  - 10.1109/NER49283.2021.9441409
PB  - IEEE
PY  - 2021///
TI  - Deep real-time decoding of bimanual grip force from EEG & fNIRS
UR  - http://dx.doi.org/10.1109/NER49283.2021.9441409
UR  - http://hdl.handle.net/10044/1/87608
ER  -
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