Multi-domain feature selection in auditory MisMatch Negativity via PARAFAC-based template matching approach.

MisMatch Negativity (MMN) is a small event-related potential (ERP) that provide an index of sensory learning and perceptual accuracy for the cognitive research. Group-level analysis plays an important role for detecting differences at group or condition level, especially when the signal-to-noise ratio is low. Tensor factorization has provided a framework for group-level analysis of ERPs by exploiting more information of brain responses in more domains simultaneously. A 4-way ERP tensor of time × frequency × channel × subjects/condition is generated and decomposed via PARAFAC. A crucial step after PARAFAC decomposition is to select the component that corresponds to the event of interest and moreover differentiates the two groups\conditions. This is usually done manually, which is tedious when the number of components is high. Here we propose a technique to select the multi-domain feature of an ERP among all extracted features by a template matching approach, that uses the MMN temporal and spectral signatures. Following a statistical test, the selected feature significantly discriminated subjects for the two experimental conditions.