A Morlet wavelet classification technique for ICA filtered sEMG experimental data

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.