Adaptive filtering of nonstationary signals using a modified P-vector algorithm

A new algorithm and two stage filter structure were developed for adaptive filtering of evoked responses. The new modified P-vector algorithm (mPa) allows the adaptive filter to simultaneously use the data gathered by a single electrode as its filter input and as its desired response. This is called single electrode adaptive filtering. Modeling the evoked response as the sum of three uncorrelated signal components, a two stage single channel time-sequenced adaptive filter structure was developed which improves convergence characteristics by decoupling the time-varying mean component from the jitter and noise components in the first stage. The EEG noise statistics must be known a priori and are adaptively estimated in real time. The performance of the two stage mPa time-sequenced adaptive filter approaches the performance for the ideal case of an adaptive filter having a noiseless desired response if the EEG is stationary. A second approach to single electrode adaptive filtering was investigated using a multichannel time-sequenced adaptive filter. The theory of multichannel time-sequenced adaptive filtering was developed and tested. Experimentally and theoretically, its performance approached ensemble averaging when only a single electrode is used.