Research Impact: Accurate identification of surface electromyography (EMG) muscle onset is vital when examining short temporal parameters such as electromechanical delay. The visual method is considered the ‘gold-standard’ in onset detection. Automatic detection methods are commonly employed to increase objectivity and reduce analysis time, but it is unclear if they are sensitive enough to accurately detect EMG onset when relating them to short-duration motor events. This study aimed to determine: 1) if automatic detection methods could be used interchangeably with visual methods in detecting EMG onsets 2) if the Teager-Kaiser energy operator (TKEO) as a conditioning step would improve the accuracy of popular EMG onset detection methods. The accuracy of three automatic onset detection methods: approximated generalized likelihood ratio (AGLR), TKEO, and threshold-based method were examined against the visual method. EMG signals from fast, explosive, and slow, ramped isometric plantarflexor contractions were evaluated using each technique.
For visual detection, the inclusion of TKEO conditioning improved inter-rater and intra-rater reliability across contraction types compared with visual detection without TKEO conditioning. In conclusion, the examined automatic detection methods are not sensitive enough to be applied when relating EMG onset to a motor event of short duration. To attain the accuracy needed, visual detection is recommended. The inclusion of TKEO as a conditioning step before visual detection of EMG onsets is recommended to improve visual detection reliability.
Crotty, E.D., Furlong, L.A.M, Hayes, K. and Harrison, A.J. (2021). Onset detection in surface electromyographic signals across isometric explosive and ramped contractions: a comparison of computer-based methods, Physiological Measurement, https://doi.org/10.1088/1361-6579/abef56