Auditory Stimulus-response Modeling with a Match-Mismatch Task

Abstract

The relation between a continuous ongoing stimulus and the brain response that it evokes can be characterized by a stimulus-response model fit to the data. This systems-identification approach offers insight into perceptual processes within the brain, and it is also of potential practical 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 {\em match-mismatch}\ task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it. The match-mismatch task can be used to compare performance of different stimulus-response models. We show that performance in a match-mismatch task and metrics summarizing regression accuracies can provide complementary insights in the relation between stimulus and response. Importantly, the match-mismatch task provides information about discriminatory power, making it directly applicable to BCI applications. Evaluation is performed on a freely available database, and code is available for scripts and functions to allow scrutiny of our results and facilitate comparative evaluation of future developments.