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Classification complexity in myoelectric pattern recognition
BACKGROUND: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject’s intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504674/ https://www.ncbi.nlm.nih.gov/pubmed/28693533 http://dx.doi.org/10.1186/s12984-017-0283-5 |
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author | Nilsson, Niclas Håkansson, Bo Ortiz-Catalan, Max |
author_facet | Nilsson, Niclas Håkansson, Bo Ortiz-Catalan, Max |
author_sort | Nilsson, Niclas |
collection | PubMed |
description | BACKGROUND: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject’s intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition. METHODS: CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback–Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec. RESULTS: NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers’ sensitivity to such redundancy. CONCLUSIONS: This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance. |
format | Online Article Text |
id | pubmed-5504674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55046742017-07-12 Classification complexity in myoelectric pattern recognition Nilsson, Niclas Håkansson, Bo Ortiz-Catalan, Max J Neuroeng Rehabil Research BACKGROUND: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject’s intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition. METHODS: CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback–Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec. RESULTS: NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers’ sensitivity to such redundancy. CONCLUSIONS: This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance. BioMed Central 2017-07-10 /pmc/articles/PMC5504674/ /pubmed/28693533 http://dx.doi.org/10.1186/s12984-017-0283-5 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Nilsson, Niclas Håkansson, Bo Ortiz-Catalan, Max Classification complexity in myoelectric pattern recognition |
title | Classification complexity in myoelectric pattern recognition |
title_full | Classification complexity in myoelectric pattern recognition |
title_fullStr | Classification complexity in myoelectric pattern recognition |
title_full_unstemmed | Classification complexity in myoelectric pattern recognition |
title_short | Classification complexity in myoelectric pattern recognition |
title_sort | classification complexity in myoelectric pattern recognition |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504674/ https://www.ncbi.nlm.nih.gov/pubmed/28693533 http://dx.doi.org/10.1186/s12984-017-0283-5 |
work_keys_str_mv | AT nilssonniclas classificationcomplexityinmyoelectricpatternrecognition AT hakanssonbo classificationcomplexityinmyoelectricpatternrecognition AT ortizcatalanmax classificationcomplexityinmyoelectricpatternrecognition |