Parametric modeling of somatosensory evoked potentials

The authors examine methods of characterizing somatosensory evoked potentials (SEPs) in both the time and frequency domains. They have found that the truncated impulse response (TIR) method produced an accurate time-domain model of the SEP signals at model orders greatly reduced from the original state space matrix. The TIR method was valuable for smoothing signals that were slightly corrupted by noise. For signals that were greatly corrupted by noise, the TIR method was not able to perform as well. Therefore, the TIR method was not a feature extraction method but was valuable for data simulation. In the frequency domain, the autoregressive moving average (ARMA) model was used to parameterize the SEP signal. An overdetermined set of Yule-Walker equations was created to determine the autoregressive (AR) parameters of the original data with the model order established by the singular value decomposition. From these AR parameters, a residual time series was generated which was used to find the moving average parameters. The resulting ARMA model was used to produce a simulated data sequence.