  Graph Signal Processing has become a very useful framework for signal
operations and representations defined on irregular domains. Exploiting
transformations that are defined on graph models can be highly beneficial when
the graph encodes relationships between signals. In this work, we present the
benefits of using Spectral Graph Wavelet Transform (SGWT) as a feature
extractor for machine learning on brain graphs. First, we consider a synthetic
regression problem in which the smooth graph signals are generated as input
with additive noise, and the target is derived from the input without noise.
This enables us to optimize the spectrum coverage using different wavelet
shapes. Finally, we present the benefits obtained by SGWT on a functional
Magnetic Resonance Imaging (fMRI) open dataset on human subjects, with several
graphs and wavelet shapes, by demonstrating significant performance
improvements compared to the state of the art.
