Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypot...

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Vydané v:	PloS one Ročník 10; číslo 6; s. e0127231
Hlavní autori:	Kauppi, Jukka-Pekka, Hahne, Janne, Müller, Klaus-Robert, Hyvärinen, Aapo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 03.06.2015 Public Library of Science (PLoS)
Predmet:	Arrays Artificial intelligence Brain research Classification Computer science Density Electrodes Electromyography Electromyography - methods Electronic Data Processing - methods Engineering Female Generalized linear models Hand Hand - physiology Humans Independent component analysis Information processing Learning algorithms Machine learning Male Movement - physiology Multivariate analysis Myoelectricity Neurophysiology Physiological aspects Physiology Preprocessing Principal components analysis Propagation Prostheses Regularization Signal processing Temporal variations Japan Germany Finland
ISSN:	1932-6203, 1932-6203
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Abstract	Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.
AbstractList	Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results. Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.
Audience	Academic
Author	Kauppi, Jukka-Pekka Hahne, Janne Hyvärinen, Aapo Müller, Klaus-Robert
AuthorAffiliation	6 Dept. of Dynamic Brain Imaging, Advanced Telecommunication Research Institute International (ATR), Kyoto, Japan 4 Dept. of Neurorehabilitation Engineering, Universitätsmedizin Göttingen, Göttingen, Germany 5 Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea University of Texas School of Public Health, UNITED STATES 1 Dept. of Computer Science/HIIT, University of Helsinki, Helsinki, Finland 2 Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, Espoo, Finland 3 Dept. of Computer Science, Machine Learning Group, Berlin Institute of Technology, Berlin, Germany
AuthorAffiliation_xml	– name: 5 Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea – name: 1 Dept. of Computer Science/HIIT, University of Helsinki, Helsinki, Finland – name: 3 Dept. of Computer Science, Machine Learning Group, Berlin Institute of Technology, Berlin, Germany – name: 2 Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, Espoo, Finland – name: 4 Dept. of Neurorehabilitation Engineering, Universitätsmedizin Göttingen, Göttingen, Germany – name: 6 Dept. of Dynamic Brain Imaging, Advanced Telecommunication Research Institute International (ATR), Kyoto, Japan – name: University of Texas School of Public Health, UNITED STATES
Author_xml	– sequence: 1 givenname: Jukka-Pekka surname: Kauppi fullname: Kauppi, Jukka-Pekka – sequence: 2 givenname: Janne surname: Hahne fullname: Hahne, Janne – sequence: 3 givenname: Klaus-Robert surname: Müller fullname: Müller, Klaus-Robert – sequence: 4 givenname: Aapo surname: Hyvärinen fullname: Hyvärinen, Aapo
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26039100$$D View this record in MEDLINE/PubMed
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CitedBy_id	crossref_primary_10_1007_s13534_025_00483_7 crossref_primary_10_1162_neco_a_01038 crossref_primary_10_3390_s25134004 crossref_primary_10_1109_TEMC_2019_2903232
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 Conceived and designed the experiments: JH KRM JPK. Performed the experiments: JH JPK. Analyzed the data: JPK. Contributed reagents/materials/analysis tools: JPK AH KRM JH. Wrote the paper: JPK AH KRM JH. Implemented the new analysis methods: JPK. Competing Interests: The authors have declared that no competing interests exist.
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Snippet	Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With...
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SubjectTerms	Arrays Artificial intelligence Brain research Classification Computer science Density Electrodes Electromyography Electromyography - methods Electronic Data Processing - methods Engineering Female Generalized linear models Hand Hand - physiology Humans Independent component analysis Information processing Learning algorithms Machine learning Male Movement - physiology Multivariate analysis Myoelectricity Neurophysiology Physiological aspects Physiology Preprocessing Principal components analysis Propagation Prostheses Regularization Signal processing Temporal variations
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Title	Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation
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