EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking Neural Networks
This work addresses the need for efficient, low-latency, and low-power real-time EEG decoding applications, representing an incremental improvement over existing SNN methods.
The paper tackled the problem of inefficient low-latency decoding of EEG signals with spiking neural networks by proposing a graph spiking neural network architecture (EEGSN) that learns dynamic relational information from distributed EEG sensors, resulting in a 20x reduction in inference computational complexity while maintaining comparable accuracy on motor execution classification tasks.
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.