We investigate how token granularity and model size affect EEG-language model performance in both in-distribution and cross-site scenarios, and find that token granularity is a critical, task-dependent scaling dimension for clinical EEG models, sometimes more important than model size.
We build lightweight and interpretable transformer-like neural nets by unrolling a spectral denoising algorithm for signals on a balanced signed graph—graph with no cycles of odd number of negative edges. Experiments show that our method achieves classification performance comparable to representative deep learning schemes, while employing dramatically fewer parameters.
We present a graph neural network that is able to track cortical spreading depressions in scalp EEG signals. We show that our model is scalable to different densities of EEG and generalizable to different head models.