Principal Components Analysis Preprocessing for Improved Classification Accuracies in Pattern-Recognition-Based Myoelectric Control

Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from m...

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Published in:	IEEE transactions on biomedical engineering Vol. 56; no. 5; pp. 1407 - 1414
Main Authors:	Hargrove, Levi J., Li, Guanglin, Englehart, Kevin B., Hudgins, Bernard S.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2009 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Amputee Amputees Conductors Control systems Data mining Decorrelation Dispersion electromyography (EMG) Electromyography - methods Forearm - physiology Humans Muscle Contraction - physiology Muscle, Skeletal - physiology Muscles myoelectric myoelectric signal (MES) Pattern recognition Pattern Recognition, Automated - methods Principal Component Analysis Principal components analysis prostheses Prosthetics Rotation measurement Signal Processing, Computer-Assisted tranrsradial
ISSN:	0018-9294, 1558-2531, 1558-2531
Online Access:	Get full text
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Abstract	Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This ldquotunesrdquo the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly (p<0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects.
AbstractList	Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This ldquotunesrdquo the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly (p<0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects. Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This "tunes" the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly (p<0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects. Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This Idquotunesrdquo the data to alow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly (p<0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects. Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This "tunes" the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly ( p < 0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects.Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This "tunes" the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly ( p < 0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects.
Author	Hargrove, Levi J. Englehart, Kevin B. Hudgins, Bernard S. Li, Guanglin
Author_xml	– sequence: 1 givenname: Levi J. surname: Hargrove fullname: Hargrove, Levi J. email: l-hargrove@northwestern.edu organization: Inst. of Biomed. Eng., Univ. of New Brunswick, Fredericton, NB – sequence: 2 givenname: Guanglin surname: Li fullname: Li, Guanglin organization: Neural Eng. Center for Artificial Limbs, Rehabilitation Inst. of Chicago, Chicago, IL – sequence: 3 givenname: Kevin B. surname: Englehart fullname: Englehart, Kevin B. organization: Inst. of Biomed. Eng., Univ. of New Brunswick, Fredericton, NB – sequence: 4 givenname: Bernard S. surname: Hudgins fullname: Hudgins, Bernard S. organization: Inst. of Biomed. Eng., Univ. of New Brunswick, Fredericton, NB
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SubjectTerms	Algorithms Amputee Amputees Conductors Control systems Data mining Decorrelation Dispersion electromyography (EMG) Electromyography - methods Forearm - physiology Humans Muscle Contraction - physiology Muscle, Skeletal - physiology Muscles myoelectric myoelectric signal (MES) Pattern recognition Pattern Recognition, Automated - methods Principal Component Analysis Principal components analysis prostheses Prosthetics Rotation measurement Signal Processing, Computer-Assisted tranrsradial
Title	Principal Components Analysis Preprocessing for Improved Classification Accuracies in Pattern-Recognition-Based Myoelectric Control
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