Optimal Denoising of Brainstem Auditory Evoked Response (Baer) for Automatic Peak Identification and Brainstem Assessment

Brainstem auditory evoked responses (BAER) are transient signals embedded in the EEG recorded from scalp electrodes, when a subject is presented with a series of acoustic clicks. These signals typically have a signal-to-noise ratio (SNR) well below -10 dB. The extraction of BAER signals from the EEG for the purpose of automatically computing features of interest from the BAER waveform(s) is described in this paper. These features are: 1) Presence of an actual BAER response (at least peak I), 2) Presence of peak V, 3) Inter-peak latency I-V. We propose to combine a signal-adaptive denoising technique based on complex wavelets with a signal quality metric referred to as the FSP variance ratio for quantitative evaluation of signal quality in order to optimally denoise BAER signals and perform reliable waveform analysis