A multi-modal machine learning system uses multiple unique data sources and types to improve its performance. This article proposes a system that combines results from several types of models, all of which are trained on different data signals. As an example, an experiment is described in which multiple types of data are collected from rats suffering from seizures, including piezoelectric motion, video, and electrocorticography (ECoG).