Method for Adaptive Complex Wavelet Based Filtering of Eeg Signals

摘要

A method for adaptive filtering of EEG signals in the wavelet domain using a nearly shift-invariant complex wavelet transform. EEG signal data is segmented into a set of K “trials” or “light averages” of M-frames of data each. These trials are overlapped by a number of frames P, where PM. A dual-tree complex wavelet transform is computed for each light average K of EEG signal data. Next, the phase variance of each resulting normalized wavelet coefficient is computed, and the magnitude of each wavelet coefficient is selectively scaled according to the phase variance of the coefficients. The resulting wavelet coefficients are then utilized to reconstruct the ABR signal extracted from the EEG data.