Spatial Feature Extraction for Classification of Nonstationary Myoelectric Signals

D. Hofmann, J. M. Herrmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We compare classifiers for the classification of myoelectric signals and show that the performance can be improved by using spatial features that are extracted by independent component analysis. The obtained filters can be interpreted as reflecting the spatial structure of the data source. We find that the performance improves for several preprocessing algorithms, but it affects the relative performance for various classifiers in different ways. A critical performance difference is especially seen when non-stationary signal regimes during the onset of static contractions are included. Although a practically utilizable performance appears to be reached for the present data set by a certain combination of classification and preprocessing algorithms, it remains to be further optimized in order to keep this level for more realistic data sets.
Original languageEnglish
Title of host publicationMachine Learning and Applications (ICMLA), 2012 11th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages416-421
Number of pages6
Volume2
ISBN (Print)978-1-4673-4651-1
DOIs
Publication statusPublished - 1 Dec 2012

Keywords

  • electromyography
  • feature extraction
  • filtering theory
  • medical signal processing
  • signal classification
  • filter
  • independent component analysis
  • nonstationary myoelectric signal classification
  • spatial feature extraction
  • static contraction
  • Accuracy
  • Electrodes
  • Feature extraction
  • Kernel
  • Muscles
  • Support vector machines
  • Vegetation
  • linear discriminant analysis
  • myoelectric prosthesis control
  • random forests
  • support vector machines
  • tree classifiers

Fingerprint

Dive into the research topics of 'Spatial Feature Extraction for Classification of Nonstationary Myoelectric Signals'. Together they form a unique fingerprint.

Cite this