Open Access

Onset Detection in Surface Electromyographic Signals: A Systematic Comparison of Methods

  • Gerhard Staude1Email author,
  • Claus Flachenecker1,
  • Martin Daumer1 and
  • Werner Wolf1
EURASIP Journal on Advances in Signal Processing20012001:867853

DOI: 10.1155/S1110865701000191

Received: 26 July 2000

Published: 1 June 2001

Abstract

Various methods to determine the onset of the electromyographic activity which occurs in response to a stimulus have been discussed in the literature over the last decade. Due to the stochastic characteristic of the surface electromyogram (SEMG), onset detection is a challenging task, especially in weak SEMG responses. The performance of the onset detection methods were tested, mostly by comparing their automated onset estimations to the manually determined onsets found by well-trained SEMG examiners. But a systematic comparison between methods, which reveals the benefits and the drawbacks of each method compared to the other ones and shows the specific dependence of the detection accuracy on signal parameters, is still lacking. In this paper, several classical threshold-based approaches as well as some statistically optimized algorithms were tested on large samples of simulated SEMG data with well-known signal parameters. Rating between methods is performed by comparing their performance to that of a statistically optimal maximum likelihood estimator which serves as reference method. In addition, performance was evaluated on real SEMG data obtained in a reaction time experiment. Results indicate that detection behavior strongly depends on SEMG parameters, such as onset rise time, signal-to-noise ratio or background activity level. It is shown that some of the threshold-based signal-power-estimation procedures are very sensitive to signal parameters, whereas statistically optimized algorithms are generally more robust.

Keywords

EMG electromyography onset detection method performance comparison

Authors’ Affiliations

(1)
Institut für Mathematik und Datenverarbeitung, Universität der Bundeswehr München

Copyright

© Staude et al. 2001