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Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns

BACKGROUND: Most prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since H...

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Autores principales: Pan, Lizhi, Zhang, Dingguo, Jiang, Ning, Sheng, Xinjun, Zhu, Xiangyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668610/
https://www.ncbi.nlm.nih.gov/pubmed/26631105
http://dx.doi.org/10.1186/s12984-015-0102-9
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author Pan, Lizhi
Zhang, Dingguo
Jiang, Ning
Sheng, Xinjun
Zhu, Xiangyang
author_facet Pan, Lizhi
Zhang, Dingguo
Jiang, Ning
Sheng, Xinjun
Zhu, Xiangyang
author_sort Pan, Lizhi
collection PubMed
description BACKGROUND: Most prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA). METHODS: HD EMG signals acquired from the forearm of the subjects were used for pattern recognition-based myoelectric control in this study. Multiclass common spatial patterns (CSP) with two types of schemes, namely one versus one (CSP-OvO) and one versus rest (CSP-OvR), were used for feature extraction to improve the robustness against electrode shift for myoelectric control. Shift transversal (ST1 and ST2) and longitudinal (SL1 and SL2) to the direction of the muscle fibers were taken into consideration. We tested nine intact-limb subjects for eleven hand and wrist motions. The CSP features (CSP-OvO and CSP-OvR) were compared with three commonly used features, namely time-domain (TD) features, time-domain autoregressive (TDAR) features and variogram (Variog) features. RESULTS: Compared with the TD features, the CSP features significantly improved the CA over 10 % in all shift configurations (ST1, ST2, SL1 and SL2). Compared with the TDAR features, a. the CSP-OvO feature significantly improved the average CA over 5 % in all shift configurations; b. the CSP-OvR feature significantly improved the average CA in shift configurations ST1, SL1 and SL2. Compared with the Variog features, the CSP features significantly improved the average CA in longitudinal shift configurations (SL1 and SL2). CONCLUSION: The results demonstrated that the CSP features significantly improved the robustness against electrode shift for myoelectric control with respect to the commonly used features.
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spelling pubmed-46686102015-12-04 Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns Pan, Lizhi Zhang, Dingguo Jiang, Ning Sheng, Xinjun Zhu, Xiangyang J Neuroeng Rehabil Research BACKGROUND: Most prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA). METHODS: HD EMG signals acquired from the forearm of the subjects were used for pattern recognition-based myoelectric control in this study. Multiclass common spatial patterns (CSP) with two types of schemes, namely one versus one (CSP-OvO) and one versus rest (CSP-OvR), were used for feature extraction to improve the robustness against electrode shift for myoelectric control. Shift transversal (ST1 and ST2) and longitudinal (SL1 and SL2) to the direction of the muscle fibers were taken into consideration. We tested nine intact-limb subjects for eleven hand and wrist motions. The CSP features (CSP-OvO and CSP-OvR) were compared with three commonly used features, namely time-domain (TD) features, time-domain autoregressive (TDAR) features and variogram (Variog) features. RESULTS: Compared with the TD features, the CSP features significantly improved the CA over 10 % in all shift configurations (ST1, ST2, SL1 and SL2). Compared with the TDAR features, a. the CSP-OvO feature significantly improved the average CA over 5 % in all shift configurations; b. the CSP-OvR feature significantly improved the average CA in shift configurations ST1, SL1 and SL2. Compared with the Variog features, the CSP features significantly improved the average CA in longitudinal shift configurations (SL1 and SL2). CONCLUSION: The results demonstrated that the CSP features significantly improved the robustness against electrode shift for myoelectric control with respect to the commonly used features. BioMed Central 2015-12-02 /pmc/articles/PMC4668610/ /pubmed/26631105 http://dx.doi.org/10.1186/s12984-015-0102-9 Text en © Pan et al. 2015 Open Access This 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
Pan, Lizhi
Zhang, Dingguo
Jiang, Ning
Sheng, Xinjun
Zhu, Xiangyang
Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns
title Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns
title_full Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns
title_fullStr Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns
title_full_unstemmed Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns
title_short Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns
title_sort improving robustness against electrode shift of high density emg for myoelectric control through common spatial patterns
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668610/
https://www.ncbi.nlm.nih.gov/pubmed/26631105
http://dx.doi.org/10.1186/s12984-015-0102-9
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