A Morlet wavelet classification technique for ICA filtered sEMG experimental data
A. Greco, Domenico Costantino, Francesco Carlo Morabito, Mario Versaci · 2004
The paper proposes the use of independent component analysis (ICA), an unsupervised learning technique, in order to process raw surface electromyographic (sEMG) data by reducing the typical "cross-talk" effect on the electric interference pattern measured by the surface sensors. The ICA is implemented by means of a multi-layer NN scheme. The basic tool is the wavelet decomposition that allows us to detect and analyse time-varying signals. An auto-associative NN that exploits wavelet coefficients an input vector is also used as simple detector of non-stationary based on a measure of reconstruction error. In addition, Morlet wavelets have been exploited for classification problems.