  Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal
data often degraded by complex, non-Gaussian noise. For reliable analysis of
brain imaging data, it is important to extract discriminative, low-dimensional
intrinsic representation of the recorded data. This work proposes a new method
to learn the low-dimensional representations from the noise-degraded
measurements. In particular, our work proposes a new deep neural network design
that integrates graph information such as brain connectivity with
fully-connected layers. Our work leverages efficient graph filter design using
Chebyshev polynomial and recent work on convolutional nets on graph-structured
data. Our approach exploits graph structure as the prior side information,
localized graph filter for feature extraction and neural networks for high
capacity learning. Experiments on real MEG datasets show that our approach can
extract more discriminative representations, leading to improved accuracy in a
supervised classification task.
