EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices.
This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis in applications such as epileptic seizure detection. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data.
Our model demonstrates high accuracy in both seizure detection and classification tasks. Notably, REST achieves a remarkable 9-fold acceleration in inference speed compared to state-of-the-art models, while simultaneously demanding substantially less memory than the smallest model employed for this task. These attributes position REST as a promising candidate for real-time implementation in clinical devices, such as Responsive Neurostimulation or seizure alert systems.
The model is located at REST.py and by using the Train_Rest.py you can easily train REST.
Simple update of REST can be applied by:
def update(self, x_t , edge_index , edge_weight , s_t , fire_rate ):
if s_t is None:
s_t = self.l1(x_t)
else:
s_t = self.l1(x_t) + self.l2(s_t)
ds = self.gc1(s_t , edge_index , edge_weight.float() )
ds = ds.relu()
ds = self.gc2(ds , edge_index , edge_weight.float() )
update_mask = torch.rand(ds.size()).float().cuda() <= fire_rate
ds *= update_mask
s_t = s_t + ds
return s_t
This project is inspired by the super cool concept of Neural Cellular Automata (NCA) — models that self-organize complex behavior through simple, local rules, much like how biological cells operate.
🧬 NCA models update each “cell” in parallel using local context, enabling emergent computation over time — perfect inspiration for spiking and message-passing systems on graphs!
🔗 Read the original paper: Growing Neural Cellular Automata by Mordvintsev et al. (2020)