Nonparametric Discriminant Projections for Improved Myoelectric Classification

Ernest N. Kamavuako, Erik J. Scheme, Kevin B. Englehart

Abstract

Linear discriminant analysis (LDA) is widely used for classification of myoelectric signals and it has been shown to outperform simple classifiers such as k-Nearest Neighbour (kNN). However the normality assumption of the LDA may cause its performance to decrease when the distribution of the feature space is far from Gaussian. In this study we investigate whether nonparametric discriminant (NDA) projections in combination with kNN classifiers can significantly decrease the classification error. Data sets based on both surface and intramuscular electromyography (EMG) were used in order to solve classification problems of up to 9 classes, including simultaneous movements. Results showed that in all data sets, the classification error was significantly lower when using NDA projections compared with LDA.

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Paper Citation


in Harvard Style

N. Kamavuako E., J. Scheme E. and B. Englehart K. (2014). Nonparametric Discriminant Projections for Improved Myoelectric Classification . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 127-132. DOI: 10.5220/0004732901270132


in Bibtex Style

@conference{biosignals14,
author={Ernest N. Kamavuako and Erik J. Scheme and Kevin B. Englehart},
title={Nonparametric Discriminant Projections for Improved Myoelectric Classification},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={127-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004732901270132},
isbn={978-989-758-011-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Nonparametric Discriminant Projections for Improved Myoelectric Classification
SN - 978-989-758-011-6
AU - N. Kamavuako E.
AU - J. Scheme E.
AU - B. Englehart K.
PY - 2014
SP - 127
EP - 132
DO - 10.5220/0004732901270132