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Cited by 211

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Hargrove, Levi J., Li, Guanglin, Englehart, Kevin B., Hudgins, Bernard S.·2009·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2008.2008171
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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 "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.

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Cites

Research works printed but not linked: 29

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Metadata

Authors
Hargrove, L. J., Li, G., Englehart, K. B., & Hudgins, B. S.
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2009-05
Volume
56
Issue
5
Pages
1407-1414
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2008.2008171
OpenAlex
W2120395111
Cited by
211
References
3
FWCI
2.77
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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