Spatial filtering for robust myoelectric control

Janne Mathias Hahne, Bernhard Graimann, Klaus Robert Muller

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)


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.

Original languageEnglish
Article number6156755
Pages (from-to)1436-1443
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number5
Publication statusPublished - 2012 May

Bibliographical note

Funding Information:
Manuscript received November 15, 2011; revised January 18, 2012; accepted January 28, 2012. Date of publication February 23, 2012; date of current version April 20, 2012. This work was supported by the Marie Currie Industry-Academia Partnerships and Pathways (IAPP) Grant “AMYO,” under Project 251555 and by the World Class University Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, under Grant R31-10008. Asterisk indicates corresponding author.


  • Common spatial pattern (csp)
  • hand prostheses
  • myoelectric control
  • prosthetic control
  • prosthetics
  • spatial filters

ASJC Scopus subject areas

  • Biomedical Engineering


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