Pattern Classification Techniques for EMG Decomposition

The electromyographic (EMG) signal decomposition process is addressed by developing different pattern classification approaches. Single classifier and multiclassifier approaches are described for this purpose. Single classifiers include: certainty-based classifiers, classifiers based on the nearest neighbour decision rule: the fuzzy k-NN classifiers, and classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers. Multiple classifier approaches aggregate the decision of the heterogeneous classifiers aiming to achieve better classification performance. Multiple classifier systems include: one-stage classifier fusion, diversity-based one-stage classifier fusion,hybrid classifier fusion, and diversity-based hybrid classifier fusion schemes.