A time-frequency feature extraction scheme for the automated detection of binaural interaction in auditory brainstem responses.

The binaural interaction component (BIC), the difference between the summed monaurally evoked potentials of each ear and the binaurally evoked brainstem potentials, has been shown to be related to directional hearing. However, the detection of the beta-peak as the most consistent part of the BIC is often difficult. Furthermore, there is no clearly defined signal feature characterizing the difference between the monaurally and the binaurally evoked brainstem responses. A closer look at the signals shows that amplitude differences as well as latency differences and variations in wave V slopes could be the reason for the formation of a beta-peak. Using a time-scale feature extraction scheme, we were able to define a signal feature (morphological local discriminant bases (MLDB) coefficient 1) that accounts for the difference between the sum of the monaurally and binaurally evoked brainstem potentials. With use of this signal feature, reliable automated detection of differences between monaurally and binaurally evoked potentials is possible. As coefficient 1 replicates the behaviour of subjective measurements as well as of the BIC measurements, it can also be seen as a correlate of binaural interaction. With use of this signal feature, it is possible to judge from a given binaurally evoked potential whether it contains information on binaural interaction or not, without comparing it to the sum of the monaurally evoked brainstem responses Consequently, binaural interaction can be assessed by one, instead of three, measurements by using the method described in this paper.