Imperial College London
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ProfessorSimonSchultz

Faculty of Engineering, Department of Bioengineering

Professor of Neurotechnology
 
 
 
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Contact

 

s.schultz Website

 
 
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Location

 

4.11Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Lubba:2019:10.1109/NER.2019.8717069,
author = {Lubba, CH and Fulcher, BD and Schultz, SR and Jones, NS},
doi = {10.1109/NER.2019.8717069},
pages = {179--182},
publisher = {IEEE},
title = {Efficient peripheral nerve firing characterisation through massive feature extraction},
url = {http://dx.doi.org/10.1109/NER.2019.8717069},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Peripheral nerve decoding algorithms form an important component of closed-loop bioelectronic medicines devices. For any decoding method, meaningful properties need to be extracted from the peripheral nerve signal as the first step. Simple measures such as signal amplitude and features of the Fourier power spectrum are most typically used, leaving open whether important information is encoded in more subtle properties of the dynamics. We here propose a feature-based analysis method that identifies changes in firing characteristics across recording sections by unsupervised dimensionality reduction in a high-dimensional feature-space and selects single efficiently implementable estimators for each characteristic to be used as the basis for a better decoding in future bioelectronic medicines devices.
AU  - Lubba,CH
AU  - Fulcher,BD
AU  - Schultz,SR
AU  - Jones,NS
DO  - 10.1109/NER.2019.8717069
EP  - 182
PB  - IEEE
PY  - 2019///
SN  - 1948-3546
SP  - 179
TI  - Efficient peripheral nerve firing characterisation through massive feature extraction
UR  - http://dx.doi.org/10.1109/NER.2019.8717069
UR  - http://hdl.handle.net/10044/1/71258
ER  -
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