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Spatial Filtering for Robust Myoelectric Control
Pattern recognition techniques have been applied to extract information from electromyographic (EMG) signals that can be used to control electrical powered hand prostheses. In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, dif...
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| Published in: | IEEE transactions on biomedical engineering 2012-05, Vol.59 (5), p.1436-1443 |
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| container_issue | 5 |
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| container_title | IEEE transactions on biomedical engineering |
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| creator | Hahne, Janne Mathias Graimann, Bernhard Muller, Klaus-Robert |
| description | Pattern recognition techniques have been applied to extract information from electromyographic (EMG) signals that can be used to control electrical powered hand prostheses. In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, different multiclass extensions of the common spatial patterns algorithm are applied to high-density surface EMG signals acquired from the forearms of ten healthy subjects. Visualization of the obtained filter coefficients provides insight into the physiology of the muscles related to the performed contractions. The CSP methods are compared with a commonly used pattern recognition approach in a six-class classification task. Cross-validation results show a significant improvement in performance and a higher robustness against noise than commonly used pattern recognition methods. |
| doi_str_mv | 10.1109/TBME.2012.2188799 |
| format | article |
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In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, different multiclass extensions of the common spatial patterns algorithm are applied to high-density surface EMG signals acquired from the forearms of ten healthy subjects. Visualization of the obtained filter coefficients provides insight into the physiology of the muscles related to the performed contractions. The CSP methods are compared with a commonly used pattern recognition approach in a six-class classification task. Cross-validation results show a significant improvement in performance and a higher robustness against noise than commonly used pattern recognition methods.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial Limbs</subject><subject>Common spatial pattern (csp)</subject><subject>Covariance matrix</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Electrodes</subject><subject>Electromyography</subject><subject>Electromyography - methods</subject><subject>Exact sciences and technology</subject><subject>Female</subject><subject>Forearm - physiology</subject><subject>Hand - physiology</subject><subject>hand prostheses</subject><subject>Humans</subject><subject>Information, signal and communications theory</subject><subject>Joints</subject><subject>Male</subject><subject>Motor Activity - physiology</subject><subject>Muscle, Skeletal - physiology</subject><subject>myoelectric control</subject><subject>Noise</subject><subject>Pattern recognition</subject><subject>Pattern Recognition, Automated - methods</subject><subject>prosthetic control</subject><subject>prosthetics</subject><subject>Reproducibility of Results</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Signal, noise</subject><subject>spatial filters</subject><subject>Telecommunications and information theory</subject><subject>Training</subject><issn>0018-9294</issn><issn>1558-2531</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNpFkNFKwzAUhoMobk4fQATpjeBNZ07SpMmljk2FDUHndUnTU6l0zUzai729Havz6nA43_9z-Ai5BjoFoPph_bSaTxkFNmWgVKr1CRmDECpmgsMpGVMKKtZMJyNyEcJ3vyYqkedkxBhPE56wMaEfW9NWpo4WVd2ir5qvqHQ-end5F9potXNYo219ZaOZa1rv6ktyVpo64NUwJ-RzMV_PXuLl2_Pr7HEZWy6gjUtTIDDDE6ELBSg5xbJ_hCPLpdSskFZZRWWeK5kDpnkhkLPUcCioLgzmfELuD71b7346DG22qYLFujYNui5kQKlWlKUSehQOqPUuBI9ltvXVxvhdD2V7UdleVLYXlQ2i-sztUN_lGyyOiT8zPXA3ACZYU5feNLYK_5xQkoHkPXdz4CpEPJ4lCJkKwX8BTg14bQ</recordid><startdate>20120501</startdate><enddate>20120501</enddate><creator>Hahne, Janne Mathias</creator><creator>Graimann, Bernhard</creator><creator>Muller, Klaus-Robert</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20120501</creationdate><title>Spatial Filtering for Robust Myoelectric Control</title><author>Hahne, Janne Mathias ; Graimann, Bernhard ; Muller, Klaus-Robert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c351t-fade12a3459d81e630ef2943e2b6692d6c8c806bb86b1e7bd5e327a31d09daeb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial Limbs</topic><topic>Common spatial pattern (csp)</topic><topic>Covariance matrix</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Electrodes</topic><topic>Electromyography</topic><topic>Electromyography - methods</topic><topic>Exact sciences and technology</topic><topic>Female</topic><topic>Forearm - physiology</topic><topic>Hand - physiology</topic><topic>hand prostheses</topic><topic>Humans</topic><topic>Information, signal and communications theory</topic><topic>Joints</topic><topic>Male</topic><topic>Motor Activity - physiology</topic><topic>Muscle, Skeletal - physiology</topic><topic>myoelectric control</topic><topic>Noise</topic><topic>Pattern recognition</topic><topic>Pattern Recognition, Automated - methods</topic><topic>prosthetic control</topic><topic>prosthetics</topic><topic>Reproducibility of Results</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Signal, noise</topic><topic>spatial filters</topic><topic>Telecommunications and information theory</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hahne, Janne Mathias</creatorcontrib><creatorcontrib>Graimann, Bernhard</creatorcontrib><creatorcontrib>Muller, Klaus-Robert</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Xplore</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on biomedical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hahne, Janne Mathias</au><au>Graimann, Bernhard</au><au>Muller, Klaus-Robert</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial Filtering for Robust Myoelectric Control</atitle><jtitle>IEEE transactions on biomedical engineering</jtitle><stitle>TBME</stitle><addtitle>IEEE Trans Biomed Eng</addtitle><date>2012-05-01</date><risdate>2012</risdate><volume>59</volume><issue>5</issue><spage>1436</spage><epage>1443</epage><pages>1436-1443</pages><issn>0018-9294</issn><eissn>1558-2531</eissn><coden>IEBEAX</coden><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>Pattern recognition techniques have been applied to extract information from electromyographic (EMG) signals that can be used to control electrical powered hand prostheses. In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, different multiclass extensions of the common spatial patterns algorithm are applied to high-density surface EMG signals acquired from the forearms of ten healthy subjects. Visualization of the obtained filter coefficients provides insight into the physiology of the muscles related to the performed contractions. The CSP methods are compared with a commonly used pattern recognition approach in a six-class classification task. Cross-validation results show a significant improvement in performance and a higher robustness against noise than commonly used pattern recognition methods.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>22374342</pmid><doi>10.1109/TBME.2012.2188799</doi><tpages>8</tpages></addata></record> |
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| subjects | Algorithms Applied sciences Artificial Limbs Common spatial pattern (csp) Covariance matrix Detection, estimation, filtering, equalization, prediction Eigenvalues and eigenfunctions Electrodes Electromyography Electromyography - methods Exact sciences and technology Female Forearm - physiology Hand - physiology hand prostheses Humans Information, signal and communications theory Joints Male Motor Activity - physiology Muscle, Skeletal - physiology myoelectric control Noise Pattern recognition Pattern Recognition, Automated - methods prosthetic control prosthetics Reproducibility of Results Signal and communications theory Signal processing Signal Processing, Computer-Assisted Signal, noise spatial filters Telecommunications and information theory Training |
| title | Spatial Filtering for Robust Myoelectric Control |
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