Improved single-trial signal extraction of low SNR events

Initial investigations of Birch´s outlier processing method (OPM) have demonstrated an ability to extract a special class of finite-duration signals from colored noise processes. This special class of signals are low SNR signals whose shapes and latencies can vary dramatically from trial to trial. Such signals, termed highly variable events (HVE) in the present paper, are commonly found in physiological signal analysis applications. The present paper identifies that the OPM produces suboptimal HVE estimates due to its use of time-invariant influence functions and demonstrates that the addition of time-varying influence functions (TVIFs) produce improved estimates. Simulation experiments with signals in white and colored noise processes were used to demonstrate the modified OPM algorithm´s superior performance compared to the performance of the original algorithm and to the performance of a time-invariant minimum mean-square-error (MMSE) filter for linear and stationary processes. The simulation results indicate that the OPM algorithm with TVIFs can extract HVEs from a linear and stationary process for SNR levels above -2.5 dB and can work effectively as low as -10.0 dB in certain situations