Spatial filtering for robust myoelectric control

Janne Mathias Hahne, Bernhard Graimann, Klaus Robert Muller

Research output: Contribution to journalArticlepeer-review

75 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


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

ASJC Scopus subject areas

  • Biomedical Engineering


Dive into the research topics of 'Spatial filtering for robust myoelectric control'. Together they form a unique fingerprint.

Cite this