Tracking of changes in latency and amplitude of the evoked potential by using adaptive LMS filters and exponential averagers

The adaptive LMS algorithm in combination with exponential averagers are compared to the use of exponential averagers only in tracking latency and amplitude changes in the evoked potential. The estimator is intended for use in applications where neurologic functions are monitored by detecting changes in the evoked potential. Two different structures of the estimator are evaluated and it is found that averaging before filtering is to be preferred. It is shown that the desired signal to the LMS-filter can have a rather low SNR with only mirror influence on the estimator performance. The estimator which combines an LMS filter and an exponential averager was shown to detect changes in latency faster than the estimator which uses a nonfiltered average. The LMS filter is shown to exhibit bias in the estimate of the evoked potential due to the fact that response and background spectra has overlapping frequency ranges. The bias seems not to affect the latency estimation while amplitude estimation was clearly affected. Simulations are performed with both white noise and EEG background.