The relationship between mismatch response and the acoustic change complex in normal hearing infants

Abstract

This paper identified that MMR can be used to identify discrimination in normal hearing infants. This suggests that MMR has potential for use in infants with hearing loss to validate hearing aid fittings.

Recording Obligatory Cortical Auditory Evoked Potentials in Infants: Quantitative Information on Feasibility and Parent Acceptability

“…Nor does it mean that audible stimuli can be discriminated. Evoked potential measures that are being used as potential approaches for assessing early infant speech discrimination include the mismatch negativity response and acoustic change complex (see, e.g., Uhler et al 2018 ).…”

Mismatched response predicts behavioral speech discrimination outcomes in infants with hearing loss and normal hearing

“…During the EEG recordings, sufficient EEG was captured before and after each block so that evoked responses were not distorted by the filter edges. Each channel (excluding the eye channel) was re‐referenced to the average of the two mastoid channels, M1 and M2 (i.e., linked mastoids), and only the vertex electrode, Cz, was retained for analysis (Uhler et al, 2018). A time‐frequency representation of the continuous EEG was then computed via the continuous wavelet transform (CWT; 6‐cycle Morlet wavelet, 64 log‐spaced scales from 1 to 18 Hz).…”

“…For the purposes of segment alignment, we considered the onset of each vowel stimulus to be the point at which voicing begins (i.e., voice onset) and the onset of each CV stimulus to be the point at which the consonant burst begins. Time‐frequency coherence (TFC) maps were computed for the standard and deviant conditions in each MMR SE block using a bootstrapped averaging procedure (Gilley et al, 2017; Uhler et al, 2018). First, data were pooled into two sets of trials defined by whether a trial was standard or deviant and with the constraint that included trials must be both preceded by and followed by a standard trial.…”

“…We were more likely to obtain an MMR than an ACC in sleeping infants, so we focused on MMR. The filter settings and analysis parameters that led to a robust response in every individual sleeping infant were identified (Uhler et al, 2018). We compared several analytical approaches (e.g., traditional signal averaging, time‐frequency analysis, band‐pass filtering) and found that an eigenvector analysis of the MMR’s spectral covariances led to the most reliable and robust responses.…”

“…Our recent work, described above, focused on improving ERP analyses in order to reliably capture and analyze individual MMR SE results from infants with NH and with HL. To this end, we have examined the application of time‐frequency (TF) transformations and latent variable (i.e., variables that are not directly observed but rather inferred from observed variables) analyses for extracting experimentally relevant signals in the MMR (Gilley et al, 2017) and have systematically examined the effects of different time and frequency windows for identifying MMR signals (Uhler et al, 2018). While such methods have been successful for extracting MMR signals, they have also highlighted the individual variability inherent in infant ERPs.…”