Faculty Advisor

Edward A. Clancy


High quality automated electromyogram (EMG) decomposition algorithms are necessary to insure the reliability of clinical and scientific information derived from them. In this work, we used experimental and simulated data to analyze the decomposition performance of three publicly available algorithms¡ªEMGLAB [McGill et al., 2005] (single-channel data only), Fuzzy Expert [Erim and Lin, 2008] and Montreal [Florestal et al., 2009]. Comparison data consisted of quadrifilar needle EMG from the tibialis anterior of 12 subjects (young and elderly) at three contraction levels (10, 20 and 50% MVC), single-channel clinical EMG from the biceps brachii of 10 subjects, and matched simulation data for both electrode types. Performance was assessed via agreement between pairs of algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For the quadrifilar data, median agreements between the Montreal and Fuzzy Expert algorithms at 10, 20 and 50% MVC were 95.7, 86.4 and 64.8%, respectively. For the single-channel data, median agreements between pairs of algorithms were 94.9% (Montreal vs. Fuzzy Expert) and 100% (EMGLAB vs. either Montreal or Fuzzy Expert). Accuracy on the simulated data exceeded this performance. Agreement/accuracy was strongly related to trial Complexity, as was motor unit signal to noise ratio, Dissimilarity and Decomposability Index. When agreement was high between algorithm pairs applied to the simulated data, so was the individual accuracy of each algorithm.


Worcester Polytechnic Institute

Degree Name



Electrical & Computer Engineering

Project Type


Date Accepted





decomposition, EMG