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:2021:10.1109/NER49283.2021.9441323,
author = {Ortega, San Miguel P and Faisal, AA},
doi = {10.1109/NER49283.2021.9441323},
publisher = {IEEE},
title = {HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks},
url = {http://dx.doi.org/10.1109/NER49283.2021.9441323},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN’s decoding capabilities to decode in a streaming way the hand, left or  right,  from  fNIRS  data.  HemCNN  learned  to  detect  which hand  executed  a  grasp  at  a  naturalistic  hand  action  speed  of1Hz,  outperforming  standard  methods.  Since  HemCNN  does not  require  baseline  correction  and  the  convolution  operation is invariant to time translations, our method can help to unlock fNIRS  for  a  variety  of  real-time  tasks.  Mobile  brain  imaging and mobile brain machine interfacing can benefit from this to develop  real-world  neuroscience  and  practical  human  neural interfacing  based  on  BOLD-like  signals  for  the  evaluation, assistance and rehabilitation of force generation, such as fusion of  fNIRS  with  EEG  signals.
AU  - Ortega,San Miguel P
AU  - Faisal,AA
DO  - 10.1109/NER49283.2021.9441323
PB  - IEEE
PY  - 2021///
TI  - HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
UR  - http://dx.doi.org/10.1109/NER49283.2021.9441323
UR  - https://ieeexplore.ieee.org/document/9441323
UR  - http://hdl.handle.net/10044/1/87609
ER  -
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