Graph networks can model data observed across different levels of biological systems that
span from population graphs (with patients as network nodes) to molecular graphs that …

The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …

Graphons are infinite-dimensional objects that represent the limit of convergent sequences
of graphs as their number of nodes goes to infinity. This paper derives a theory of graphon …

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional
neuroimaging datasets, while taking into account both the spatial and functional …

This paper proposes a novel graph signal-based deep learning method for
electroencephalography (EEG) and its application to EEG-based video identification. We …

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in
designing various brain-computer interface (BCI) applications. Most of the current …

Modeling spatiotemporal interactions in multivariate time series is key to their effective
processing, but challenging because of their irregular and often unknown structure …

In many network problems, graphs may change by the addition of nodes, or the same
problem may need to be solved in multiple similar graphs. This generates inefficiency, as …

Imagined Speech (IS) is the imagination of speech without using the tongue or muscles. In
recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) …

Epileptic Seizure is a chronic nervous system disorder which is analyzed using
Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing …