Imperial College London


Faculty of EngineeringDepartment of Bioengineering

Professor of Neurotechnology



+44 (0)20 7594 1533s.schultz Website




4.11Royal School of MinesSouth Kensington Campus






BibTex format

author = {Lubba, CH and Fulcher, BD and Schultz, SR and Jones, NS},
doi = {10.1101/508341},
publisher = {Cold Spring Harbor Laboratory},
title = {Efficient peripheral nerve firing characterisation through massive feature extraction},
url = {},
year = {2018}

RIS format (EndNote, RefMan)

AB - <jats:p>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.</jats:p>
AU - Lubba,CH
AU - Fulcher,BD
AU - Schultz,SR
AU - Jones,NS
DO - 10.1101/508341
PB - Cold Spring Harbor Laboratory
PY - 2018///
TI - Efficient peripheral nerve firing characterisation through massive feature extraction
UR -
ER -