Electromyography and mechanomyography signal recognition: Experimental analysis using multi-way array decomposition methods
In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance...
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| Published in | Biocybernetics and biomedical engineering Vol. 37; no. 1; pp. 103 - 113 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0208-5216 |
| DOI | 10.1016/j.bbe.2016.09.004 |
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| Abstract | In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the highest classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated. |
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| AbstractList | Abstract In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the highest classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated. In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the highest classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated. |
| Author | Wołczowski, Andrzej Zdunek, Rafał |
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| Cites_doi | 10.1109/TBME.2008.2007967 10.1016/j.eswa.2011.06.043 10.1016/j.bspc.2007.09.002 10.1348/000711000159132 10.1137/07070111X 10.1587/nolta.1.37 10.1016/j.bspc.2014.12.005 10.1016/j.compbiomed.2015.04.023 10.1111/j.1468-0394.2009.00526.x 10.1016/j.csda.2011.11.012 10.1007/BF02289464 10.1016/j.sigpro.2005.05.032 10.1137/S0895479896305696 10.1109/LSP.2014.2337276 10.1007/BF02310791 10.1038/44565 10.1016/j.eswa.2012.01.102 |
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| Keywords | Mechanomyography Biosignal processing Electromyography Supervised classification Multi-array decomposition method |
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| References | Kolda, Bader (bib0060) 2009; 51 Cichocki, Zdunek, Phan, Amari (bib0015) 2009 Kim, Kim, Lee, Park (bib0055) 2016 De Lathauwer, de Moor, Vandewalle (bib0020) 2000; 21 Josse, Husson (bib0050) 2012; 56 Lee, Kim, Park (bib0075) 2013 Alkan, Gunay (bib0005) 2012; 39 Harshman (bib0035) 1970; vol. 16 Xie, Song (bib0135) 2013 Kurzynski, Krysmann, Trajdos, Wolczowski (bib0065) 2016; 69 Oliveira, Gizzi, Farina, Kersting (bib0090) 2014; 8 Phan, Cichocki (bib0095) 2010; 1 Stephen (bib0105) December 2013 Carroll, Chang (bib0010) 1970; 35 Theis, Garcia (bib0110) 2006; 86 Lee, Seung (bib0070) 1999; 401 Phinyomark, Phukpattaranont, Limsakul (bib0100) 2012; 39 Jiang, Englehart, Parker (bib0040) 2009; 56 Ulfarsson, Solo (bib0125) 2015; 22 Yazama, Mitsukura, Fukumi, Akamatsu (bib0140) 2003 Lucas, Gaufriau, Pascual, Doncarli, Farina (bib0080) 2008; 3 Timmerman, Kiers (bib0115) 2000; 53 Gokgoz, Subasi (bib0025) 2015; 18 Jolliffe (bib0045) 2002 Niegowski, Zivanovic (bib0085) August 2014 Tucker (bib0120) 1966; 31 Wolczowski, Kurzynski (bib0130) 2010; 27 Kolda (10.1016/j.bbe.2016.09.004_bib0060) 2009; 51 Yazama (10.1016/j.bbe.2016.09.004_bib0140) 2003 Ulfarsson (10.1016/j.bbe.2016.09.004_bib0125) 2015; 22 Wolczowski (10.1016/j.bbe.2016.09.004_bib0130) 2010; 27 Carroll (10.1016/j.bbe.2016.09.004_bib0010) 1970; 35 Kurzynski (10.1016/j.bbe.2016.09.004_bib0065) 2016; 69 De Lathauwer (10.1016/j.bbe.2016.09.004_bib0020) 2000; 21 Gokgoz (10.1016/j.bbe.2016.09.004_bib0025) 2015; 18 Jiang (10.1016/j.bbe.2016.09.004_bib0040) 2009; 56 Oliveira (10.1016/j.bbe.2016.09.004_bib0090) 2014; 8 Tucker (10.1016/j.bbe.2016.09.004_bib0120) 1966; 31 Lee (10.1016/j.bbe.2016.09.004_bib0075) 2013 Harshman (10.1016/j.bbe.2016.09.004_bib0035) 1970; vol. 16 Cichocki (10.1016/j.bbe.2016.09.004_bib0015) 2009 Jolliffe (10.1016/j.bbe.2016.09.004_bib0045) 2002 Kim (10.1016/j.bbe.2016.09.004_bib0055) 2016 Xie (10.1016/j.bbe.2016.09.004_bib0135) 2013 Theis (10.1016/j.bbe.2016.09.004_bib0110) 2006; 86 Alkan (10.1016/j.bbe.2016.09.004_bib0005) 2012; 39 Lucas (10.1016/j.bbe.2016.09.004_bib0080) 2008; 3 Niegowski (10.1016/j.bbe.2016.09.004_bib0085) 2014 Phan (10.1016/j.bbe.2016.09.004_bib0095) 2010; 1 Phinyomark (10.1016/j.bbe.2016.09.004_bib0100) 2012; 39 Timmerman (10.1016/j.bbe.2016.09.004_bib0115) 2000; 53 Stephen (10.1016/j.bbe.2016.09.004_bib0105) 2013 Lee (10.1016/j.bbe.2016.09.004_bib0070) 1999; 401 Josse (10.1016/j.bbe.2016.09.004_bib0050) 2012; 56 |
| References_xml | – volume: 401 start-page: 788 year: 1999 end-page: 791 ident: bib0070 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature – volume: 21 start-page: 1253 year: 2000 end-page: 1278 ident: bib0020 article-title: A multilinear singular value decomposition publication-title: SIAM J Matrix Anal Appl – start-page: 671 year: 2013 end-page: 683 ident: bib0075 article-title: Classification of grip configuration using surface EMG publication-title: Proc. 13th International Conference on Control, Automation and Systems (ICCAS 2013) – volume: 8 year: 2014 ident: bib0090 article-title: Motor modules of human locomotion: influence of EMG averaging, concatenation and number of gait cycles publication-title: Front Hum Neurosci – volume: 39 start-page: 44 year: 2012 end-page: 47 ident: bib0005 article-title: Identification of EMG signals using discriminant analysis and SVM classifier publication-title: Expert Syst Appl – year: 2009 ident: bib0015 article-title: Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation – volume: 31 start-page: 279 year: 1966 end-page: 311 ident: bib0120 article-title: Some mathematical notes on three-mode factor analysis publication-title: Psychometrika – volume: 1 start-page: 37 year: 2010 end-page: 68 ident: bib0095 article-title: Tensor decompositions for feature extraction and classification of high dimensional datasets publication-title: IEICE Nonlinear Theory Appl – start-page: 322 year: 2013 end-page: 325 ident: bib0135 article-title: Multi-domain feature extraction from surface EMG signals using nonnegative tensor factorization publication-title: Proc. 2013 IEEE International Conference on Bioinformatics and Biomedicine – year: December 2013 ident: bib0105 article-title: EMG-EMG coherence analysis on the elbow and shoulder muscles (Master's thesis) – volume: 56 start-page: 1869 year: 2012 end-page: 1879 ident: bib0050 article-title: Selecting the number of components in principal component analysis using cross-validation approximations publication-title: Comput Stat Data Anal – volume: 53 start-page: 1 year: 2000 end-page: 16 ident: bib0115 article-title: Three mode principal components analysis: choosing the numbers of components and sensitivity to local optima publication-title: Br J Math Stat Psychol – year: 2002 ident: bib0045 article-title: Principal component analysis. Springer series in statistics – start-page: 671 year: 2016 end-page: 683 ident: bib0055 article-title: Robot hand synergy mapping using multi-factor model and EMG signal publication-title: Proc. 14-th International Symposium on Experimental Robotics (ISER 2014), volume 109 of Springer Tracts in Advanced Robotics – volume: 39 start-page: 7420 year: 2012 end-page: 7431 ident: bib0100 article-title: Feature reduction and selection for EMG signal classification publication-title: Expert Syst Appl – volume: 51 start-page: 455 year: 2009 end-page: 500 ident: bib0060 article-title: Tensor decompositions and applications publication-title: SIAM Rev – volume: 18 start-page: 138 year: 2015 end-page: 144 ident: bib0025 article-title: Comparison of decision tree algorithms for EMG signal classification using DWT publication-title: Biomed Signal Process Control – volume: 27 start-page: 53 year: 2010 end-page: 70 ident: bib0130 article-title: Human-machine interface in bioprosthesis control using EMG signal classification publication-title: Expert Syst – volume: 22 start-page: 239 year: 2015 end-page: 243 ident: bib0125 article-title: Selecting the number of principal components with SURE publication-title: IEEE Signal Process Lett – volume: 86 start-page: 603 year: 2006 end-page: 623 ident: bib0110 article-title: On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms publication-title: Signal Process – volume: 56 start-page: 1070 year: 2009 end-page: 1080 ident: bib0040 article-title: Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal publication-title: IEEE Trans Biomed Eng – start-page: 4212 year: August 2014 end-page: 4215 ident: bib0085 article-title: ECG-EMG separation by using enhanced non-negative matrix factorization. publication-title: Proc. 36-th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – volume: 69 start-page: 286 year: 2016 end-page: 297 ident: bib0065 article-title: Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand publication-title: Comput Biol Med – volume: 35 start-page: 283 year: 1970 end-page: 319 ident: bib0010 article-title: Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition publication-title: Psychometrika – start-page: 2130 year: 2003 end-page: 2133 ident: bib0140 article-title: Recognition system for EMG signals by using non-negative matrix factorization publication-title: Proc. International Joint Conference on Neural Networks, vol. 3 – volume: 3 start-page: 169 year: 2008 end-page: 174 ident: bib0080 article-title: Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization publication-title: Biomed Signal Process Control – volume: vol. 16 start-page: 1 year: 1970 end-page: 84 ident: bib0035 article-title: Foundations of the PARAFAC procedure: Models and conditions for an “explanatory”multimodal factor analysis publication-title: UCLA working papers in phonetics – volume: 56 start-page: 1070 issue: 4 year: 2009 ident: 10.1016/j.bbe.2016.09.004_bib0040 article-title: Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2008.2007967 – volume: 39 start-page: 44 issue: 1 year: 2012 ident: 10.1016/j.bbe.2016.09.004_bib0005 article-title: Identification of EMG signals using discriminant analysis and SVM classifier publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.06.043 – volume: 3 start-page: 169 issue: 2 year: 2008 ident: 10.1016/j.bbe.2016.09.004_bib0080 article-title: Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2007.09.002 – volume: 53 start-page: 1 issue: 1 year: 2000 ident: 10.1016/j.bbe.2016.09.004_bib0115 article-title: Three mode principal components analysis: choosing the numbers of components and sensitivity to local optima publication-title: Br J Math Stat Psychol doi: 10.1348/000711000159132 – start-page: 671 year: 2016 ident: 10.1016/j.bbe.2016.09.004_bib0055 article-title: Robot hand synergy mapping using multi-factor model and EMG signal – year: 2013 ident: 10.1016/j.bbe.2016.09.004_bib0105 – start-page: 322 year: 2013 ident: 10.1016/j.bbe.2016.09.004_bib0135 article-title: Multi-domain feature extraction from surface EMG signals using nonnegative tensor factorization – volume: 51 start-page: 455 issue: 3 year: 2009 ident: 10.1016/j.bbe.2016.09.004_bib0060 article-title: Tensor decompositions and applications publication-title: SIAM Rev doi: 10.1137/07070111X – start-page: 671 year: 2013 ident: 10.1016/j.bbe.2016.09.004_bib0075 article-title: Classification of grip configuration using surface EMG – volume: 1 start-page: 37 issue: 1 year: 2010 ident: 10.1016/j.bbe.2016.09.004_bib0095 article-title: Tensor decompositions for feature extraction and classification of high dimensional datasets publication-title: IEICE Nonlinear Theory Appl doi: 10.1587/nolta.1.37 – volume: 18 start-page: 138 year: 2015 ident: 10.1016/j.bbe.2016.09.004_bib0025 article-title: Comparison of decision tree algorithms for EMG signal classification using DWT publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2014.12.005 – volume: 69 start-page: 286 issue: 1 year: 2016 ident: 10.1016/j.bbe.2016.09.004_bib0065 article-title: Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2015.04.023 – year: 2009 ident: 10.1016/j.bbe.2016.09.004_bib0015 – volume: 27 start-page: 53 issue: 1 year: 2010 ident: 10.1016/j.bbe.2016.09.004_bib0130 article-title: Human-machine interface in bioprosthesis control using EMG signal classification publication-title: Expert Syst doi: 10.1111/j.1468-0394.2009.00526.x – volume: 8 issue: 335 year: 2014 ident: 10.1016/j.bbe.2016.09.004_bib0090 article-title: Motor modules of human locomotion: influence of EMG averaging, concatenation and number of gait cycles publication-title: Front Hum Neurosci – volume: 56 start-page: 1869 issue: 6 year: 2012 ident: 10.1016/j.bbe.2016.09.004_bib0050 article-title: Selecting the number of components in principal component analysis using cross-validation approximations publication-title: Comput Stat Data Anal doi: 10.1016/j.csda.2011.11.012 – volume: 31 start-page: 279 year: 1966 ident: 10.1016/j.bbe.2016.09.004_bib0120 article-title: Some mathematical notes on three-mode factor analysis publication-title: Psychometrika doi: 10.1007/BF02289464 – volume: vol. 16 start-page: 1 year: 1970 ident: 10.1016/j.bbe.2016.09.004_bib0035 article-title: Foundations of the PARAFAC procedure: Models and conditions for an “explanatory”multimodal factor analysis – volume: 86 start-page: 603 issue: 3 year: 2006 ident: 10.1016/j.bbe.2016.09.004_bib0110 article-title: On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms publication-title: Signal Process doi: 10.1016/j.sigpro.2005.05.032 – start-page: 2130 year: 2003 ident: 10.1016/j.bbe.2016.09.004_bib0140 article-title: Recognition system for EMG signals by using non-negative matrix factorization – volume: 21 start-page: 1253 year: 2000 ident: 10.1016/j.bbe.2016.09.004_bib0020 article-title: A multilinear singular value decomposition publication-title: SIAM J Matrix Anal Appl doi: 10.1137/S0895479896305696 – volume: 22 start-page: 239 issue: 2 year: 2015 ident: 10.1016/j.bbe.2016.09.004_bib0125 article-title: Selecting the number of principal components with SURE publication-title: IEEE Signal Process Lett doi: 10.1109/LSP.2014.2337276 – volume: 35 start-page: 283 year: 1970 ident: 10.1016/j.bbe.2016.09.004_bib0010 article-title: Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition publication-title: Psychometrika doi: 10.1007/BF02310791 – volume: 401 start-page: 788 year: 1999 ident: 10.1016/j.bbe.2016.09.004_bib0070 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature doi: 10.1038/44565 – volume: 39 start-page: 7420 issue: 8 year: 2012 ident: 10.1016/j.bbe.2016.09.004_bib0100 article-title: Feature reduction and selection for EMG signal classification publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2012.01.102 – start-page: 4212 year: 2014 ident: 10.1016/j.bbe.2016.09.004_bib0085 article-title: ECG-EMG separation by using enhanced non-negative matrix factorization. – year: 2002 ident: 10.1016/j.bbe.2016.09.004_bib0045 |
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| Snippet | In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several... Abstract In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals.... |
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| SubjectTerms | Advanced Basic Science Biosignal processing Electromyography Internal Medicine Mechanomyography Multi-array decomposition method Supervised classification |
| Title | Electromyography and mechanomyography signal recognition: Experimental analysis using multi-way array decomposition methods |
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