Time-frequency representation for classification of the transient myoelectric signal

An accurate and computationally efficient means of classifying myoelectric signal (MES) patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient MES pattern classification, many forms of signal representation have been suggested. It is shown that feature sets based upon the short-time transform, the wavelet transform, and the wavelet packet transform provide an effective representation for classification, provided that they are subject to dimensionality reduction by principal components analysis.