Bayesian weighting is a tool designed to assist the clinician when testing in less than optimal conditions. While giving sweeps with less noise a higher “score”, sweeps with more noise are not given the same level of importance in the overall recording. Each sweep is analysed and not simply accepted or rejected as with traditional averaging but is given a unique signficance based on its level of noise. Bayesian weighting can be used in all typical ABR testing situations and will be effective when EEG levels vary during recording.
As Bayesian weighting does not change the response of a waveform, you may use it in all typical ABR recording situations.
Bayesian weighting weights noisy sweeps less and quiet sweeps more. In a situation where all sweeps are the same, they will be weighted equally. This is identical to normal averaging. Hence, there is no difference between recordings with and without Bayesian weighting.
In this case, all noisy sweeps are simply rejected for both Bayesian and non-Bayesian recordings. Only the most quiet sweeps are accepted, and you have a situation almost similar to above. With softer rejection criteria (e.g. 80µV) you would benefit from the contributions of the more noisy sweeps in your averaging thus arriving at your desired residual noise level in less time.
If two waveforms each have 40nV residual noise, then they will look equally clean. It does not matter whether you have used 1.000 or 10.000 sweeps to get there or what type of weighting is used. Weighting will simply get you there faster if the patient has fluctuating EEG during the session. It should be noted that two waveforms each with 40nV residual noise may exhibit minor variations in wave morphology due to e.g. the frequency distribution of the residual noise. This may differ with different test situations, and quiet patients tend to provide residual noise with more high frequency content and less low frequency content, which may look nicer to the eye. So keeping the patient as relaxed as possible is still recommended.
Elberling, C. & Wahlgreen (1985). Estimation of auditory brainstem response, ABR, by means of Bayesian interference. Scand. Audiol (14) 89-96.