Spatial Filtering for Robust Myoelectric Control

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, dif...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2012-05, Vol.59 (5), p.1436-1443
Main Authors: Hahne, Janne Mathias, Graimann, Bernhard, Muller, Klaus-Robert
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Limbs
Common spatial pattern (csp)
Covariance matrix
Detection, estimation, filtering, equalization, prediction
Eigenvalues and eigenfunctions
Electrodes
Electromyography
Electromyography - methods
Exact sciences and technology
Female
Forearm - physiology
Hand - physiology
hand prostheses
Humans
Information, signal and communications theory
Joints
Male
Motor Activity - physiology
Muscle, Skeletal - physiology
myoelectric control
Noise
Pattern recognition
Pattern Recognition, Automated - methods
prosthetic control
prosthetics
Reproducibility of Results
Signal and communications theory
Signal processing
Signal Processing, Computer-Assisted
Signal, noise
spatial filters
Telecommunications and information theory
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2012.2188799