Auditory Stimulus-response Modeling with a Match-Mismatch Task

An auditory stimulus can be related to the brain response that it evokes by a stimulus-response model fit to the data. This offers insight into perceptual processes within the brain and is also of potential use for devices such as Brain Computer Interfaces (BCI). The quality of the model can be quantified by measuring the fit with a regression problem, or by applying it to a classification task and measuring its performance. Here we focus on a match-mismatch (MM) task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it. The MM task allows stimulus-response models to be evaluated in the limit of very high model accuracy, making it an attractive alternative to the more commonly used task of auditory attention detection (AAD). The MM task does not require class labels, so it is immune to mislabeling, and it is applicable to data recorded in listening scenarios with only one sound source, thus it is cheap to obtain large quantities of training and testing data. Performance metrics from this task, associated with regression accuracy, provide complementary insights into the relation between stimulus and response, as well as information about discriminatory power directly applicable to BCI applications. Using these metrics, we describe a range of models of increasing complexity that we compare to methods in the literature, showing state-of-the-art performance. We document in detail one particular implementation, calibrated on a publicly-available database, that can serve as a robust reference to evaluate future developments.