  Epilepsy is one of the most serious neurological diseases, affecting 1-2% of
the world's population. The diagnosis of epilepsy depends heavily on the
recognition of epileptic waves, i.e., disordered electrical brainwave activity
in the patient's brain. Existing works have begun to employ machine learning
models to detect epileptic waves via cortical electroencephalogram (EEG).
However, the recently developed stereoelectrocorticography (SEEG) method
provides information in stereo that is more precise than conventional EEG, and
has been broadly applied in clinical practice. Therefore, we propose the first
data-driven study to detect epileptic waves in a real-world SEEG dataset. While
offering new opportunities, SEEG also poses several challenges. In clinical
practice, epileptic wave activities are considered to propagate between
different regions in the brain. These propagation paths, also known as the
epileptogenic network, are deemed to be a key factor in the context of epilepsy
surgery. However, the question of how to extract an exact epileptogenic network
for each patient remains an open problem in the field of neuroscience. To
address these challenges, we propose a novel model (BrainNet) that jointly
learns the dynamic diffusion graphs and models the brain wave diffusion
patterns. In addition, our model effectively aids in resisting label imbalance
and severe noise by employing several self-supervised learning tasks and a
hierarchical framework. By experimenting with the extensive real SEEG dataset
obtained from multiple patients, we find that BrainNet outperforms several
latest state-of-the-art baselines derived from time-series analysis.
