  Motivated by the emerging area of graph signal processing (GSP), we introduce
a novel method to draw inference from spatiotemporal signals. Data acquisition
in different locations over time is common in sensor networks, for diverse
applications ranging from object tracking in wireless networks to medical uses
such as electroencephalography (EEG) signal processing. In this paper we
leverage novel techniques of GSP to develop a hierarchical feature extraction
approach by mapping the data onto a series of spatiotemporal graphs. Such a
model maps signals onto vertices of a graph and the time-space dependencies
among signals are modeled by the edge weights. Signal components acquired from
different locations and time often have complicated functional dependencies.
Accordingly, their corresponding graph weights are learned from data and used
in two ways. First, they are used as a part of the embedding related to the
topology of graph, such as density. Second, they provide the connectivities of
the base graph for extracting higher level GSP-based features. The latter
include the energies of the signal's graph Fourier transform in different
frequency bands. We test our approach on the intracranial EEG (iEEG) data set
of the Kaggle epileptic seizure detection contest. In comparison to the winning
code, the results show a slight net improvement and up to 6 percent improvement
in per subject analysis, while the number of features are decreased by 75
percent on average.
