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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Bibliographic Details
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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Summary: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.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2008.2008171