Adaptive complex wavelet-based filtering of EEG for extraction of evoked potential responses

We propose a new method for the extraction of auditory brainstem responses (ABRs) from an EEG signal. It is based on adaptive filtering of signals in the wavelet domain, where the transform used is a nearly shift-invariant complex wavelet transform (CWT). We compare our algorithm to two existing methods. The first simply consists of bandpass filtering the input EEG signal followed by linear averaging. The second method uses signal-adaptive filtering in the Fourier domain based on phase variance computed at each spectral component of the FFT. Realistic models of EEG and ABR are generated for this comparison. Results show that the wavelet-based method consistently outperforms the other two methods for ABR signals with an initial signal-to-noise ratio less than -20 dB.