Imperial College London
Alternate

ProfessorChristos-SavvasBouganis

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Intelligent Digital Systems
 
 
 
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Contact

 

+44 (0)20 7594 6144christos-savvas.bouganis Website

 
 
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Location

 

904Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Ahmadi:2019:10.1109/BIOCAS.2019.8919131,
author = {Ahmadi, N and Constandinou, TG and Bouganis, C-S},
doi = {10.1109/BIOCAS.2019.8919131},
pages = {1--4},
publisher = {IEEE},
title = {End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network},
url = {http://dx.doi.org/10.1109/BIOCAS.2019.8919131},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In  recent  years,  local  field  potentials  (LFPs)  haveemerged   as   a   promising   alternative   input   signal   for   brain-machine  interfaces  (BMIs).  Several  studies  have  demonstratedthat LFP-based BMIs could provide long-term recording stabilityand  comparable  decoding  performance  to  their  spike  counter-parts.  Despite  the  compelling  results,  however,  most  LFP-basedBMIs  still  make  use  of  hand-crafted  features  which  can  betime-consuming  and  suboptimal.  In  this  paper,  we  propose  anend-to-end  system  approach  based  on  temporal  convolutionalnetwork  (TCN)  to  automatically  extract  features  and  decodekinematics  of  hand  movements  directly  from  raw  LFP  signals.We   benchmark   its   decoding   performance   against   traditionalapproach  incorporating  long  short-term  memory  (LSTM)  de-coders  driven  by  hand-crafted  LFP  features.  Experimental  re-sults  demonstrate  significant  performance  improvement  of  theproposed  approach  compared  to  the  traditional  approach.  Thissuggests  the  suitability  of  TCN-based  end-to-end  system  and  itspotential  for  providng  stable  and  high  decoding  performanceLFP-based  BMIs.
AU  - Ahmadi,N
AU  - Constandinou,TG
AU  - Bouganis,C-S
DO  - 10.1109/BIOCAS.2019.8919131
EP  - 4
PB  - IEEE
PY  - 2019///
SN  - 2163-4025
SP  - 1
TI  - End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network
UR  - http://dx.doi.org/10.1109/BIOCAS.2019.8919131
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000521751500108&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/abstract/document/8919131
UR  - http://hdl.handle.net/10044/1/74988
ER  -
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