  A vast majority of spiking neural networks (SNNs) are trained based on
inductive biases that are not necessarily a good fit for several critical tasks
that require low-latency and power efficiency. Inferring brain behavior based
on the associated electroenchephalography (EEG) signals is an example of how
networks training and inference efficiency can be heavily impacted by learning
spatio-temporal dependencies. Up to now, SNNs rely solely on general inductive
biases to model the dynamic relations between different data streams. Here, we
propose a graph spiking neural network architecture for multi-channel EEG
classification (EEGSN) that learns the dynamic relational information present
in the distributed EEG sensors. Our method reduced the inference computational
complexity by $\times 20$ compared to the state-of-the-art SNNs, while achieved
comparable accuracy on motor execution classification tasks. Overall, our work
provides a framework for interpretable and efficient training of graph spiking
networks that are suitable for low-latency and low-power real-time
applications.
