Adaptive wavelet filtering for analysis of event-related potentials from the electro-encephalogram.

A challenging task in psychophysiology is the extraction of event-related potentials (ERPs) from the background electro-encephalogram. The task is made more difficult by the properties of ERPs, which typically consist of multiple features of variable latency, localised in time and frequency. A novel technique is described for analysis of ERPs, adaptive wavelet filtering (AWF), which is proposed as an alternative to trial averaging. Band-limited detail representations of each trial are obtained using wavelet analysis. The Woody adaptive filter is then used to align trials with respect to the evoked response. In a simulation study, the AWF extracts 39% of higher-frequency signal variance from background noise, compared with less than 1% for standard averaging and the Woody filter. The AWF is applied to a data-set of 448 ERPs, comprising right-finger button presses from eight subjects. Average split-half reliability of the AWF on scales up to 12 Hz was 0.51.