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Abstruct-The study of the electric signals generated by contracting muscle fibers (EMG) is relevant to neurophysiological research and to the diagnosis of motoneuron diseases. Classical diagnosis methods make use of invasive, painful needle electrodes. We explored a non-invasive alternative to these methods by developing a specific algorithm that uses Independent Component Analysis OCA) and template-matching techniques to decompose surface EMG (s-EMG). An experiment was carried out with two healthy subjects performing isometric contractions at different force levels. We measured s-EMGs with an electrode array and applied to them our decomposition algorithm. The obtained motor-unit firing pattern is in agreement with results obtained with needle electrodes. Kepordssurface-EMG, ICA, template matching

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Abstract. Independent Component Analysis (ICA) can be used as a signal preprocessing tool to decompose electrode-array surface-electromyogram (s-EMG) signals into their constitutive motor-unit action potentials [García et al., IEEE EMB Mag., vol. 23 (5) (2004)]. In the present study, we have established the effectiveness and the limitations of ICA for s-EMG decomposition using a set of synthetic signals. In addition, we have selected the best-suited algorithm to perform s-EMG decomposition by comparing the effectiveness of two of the most popular standard ICA algorithms.