SVD and higher-order statistics applied in the detection of human nerve signal activity

Due to the very low signal levels (μV) and the significantly higher levels of interference from adjacent muscles (and other noise sources), the overall signal-to-noise ratios (SNRs) of human nerve signals recorded from whole-cuff electrodes is very poor. Typically, non-real-time methods (ensemble averaging) are used to contend with these poor SNRs. However, if these signals are to be useful in providing real-time information (in a closed-loop control system), other methods must be employed to this end, subspace analysis methods using the eigenvalues of the autocorrelation (a 2^nd order statistic) and cumulant (a 3^rd order statistic) matrices of time-series samples were evaluated