High-precision EMG signal decomposition using communication techniques

This paper presents a new approach to the decomposition of electromyographi c (EMG) signals, EMG signals consist of a superposition of delayed finite-d uration waveforms that carry the information about the firing of different muscle fiber groups. The new approach is based on a communication technical interpretation of the EMG signal, The source is modeled as a signaling sys tem with interssymbol-interference, which encodes a well-defined sparse inf ormation sequence. This point of view allows a maximum-likelihood (ML) as w ell as a maximum aposteriori (MAP) estimation of the underlying firing patt ern to be made. The high accuracy attainable with the proposed method is il lustrated both with measured and artificially generated EMG signals.