An application of the principle of maximum information preservation to linear systems
Advances in neural information processing systems 1
Nonlinear system input structure identification: two stage fuzzy curves and surfaces
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, the electrical signals coming from muscles in activity through experimental electromyogram interference patterns measured on human subjects are investigated. The experiments make use of surface ElectroMyoGraphic (sEMG). The use of Independent Component Analysis (ICA) is suggested as a method for processing raw sEMG data by reducing the ”cross-talk” effect. ICA also allows us to remove artefacts and to separate the different sources of muscle activity. The main ICs are used to reconstruct the original signal by using a neuro-fuzzy network. An auto-associative Neural Network that exploits wavelet coefficients as an input vector is also used as simple detector of non-stationarity based on a measure of reconstruction error.