Latent Quotient Space for Extreme Point Neighborhood Applied over Discrete Signal Time Series of MEG Recordings

Several studies have reported methods for signal similarity measurement. However, none of the reported methods consider temporal peak-shape features. In this paper, we formalize signal similarity using mathematical concepts and define a new distance function between signals that considers temporal peak-shape characteristics, providing higher precision than current similarity measurements. This distance function addresses latent geometric characteristics in quotient spaces that are not addressed by existing methods. We include an example of using this method on discrete MEG recordings, known for their high spatial and temporal resolution, which were recorded in neighborhoods of extreme points in a cross-area projection of brain activity.