Objective motor response onset detection in surface myoelectric signals.

Precise detection of discrete motor events like the onsets of voluntary muscle contractions is a prerequisite for various psychophysiological approaches in sensorimotor system analysis. In biomedical research and clinical diagnosis, motor events frequently are determined from surface electromyographic (SEMG) signals by some computerized detection algorithm. However, little is known about the reliability and accuracy of these methods, which frequently rely on intuitive and heuristic criteria. Therefore, the systematic approach to computerized detection of discrete motor events from SEMG signals presented by this paper fills a basic gap in EMG signal processing. Based upon a dynamic process model for the SEMG signal, a formal detection scheme is established which incorporates the essential processing modules common to the majority of algorithms. In addition, using concepts of statistically optimal change detection in random processes, a new model-based algorithm is presented which serves as a reference for optimum performance. The validity of this concept is demonstrated for the specific example of accurate detection of muscle activation onsets in rapid voluntary contractions; the estimation error (i.e., the deviation between estimated and "true" onset time) was evaluated by statistical simulations for three representative methods. Results show a substantial decrease of performance of traditional methods in the case of highly variable dynamic muscle activation profiles and/or superimposed activation patterns (e.g., due to a secondary motor task simultaneously executed by the same muscle). The model-based approach provided significantly more accurate results, even when the exact model parameters were unknown but estimated from the SEMG signal actually measured. It is concluded that the detection algorithm has to be critically taken into consideration during interpretation of motor events resolved from SEMG signals. The process model together with the corresponding statistically optimal detector represents an efficient tool for selecting appropriate detection algorithms for a particular experimental condition, and it allows a quantitative assessment of their performance.