  Epilepsy is one of the most common neurological disorders, and many patients
require surgical intervention when medication fails to control seizures. For
effective surgical outcomes, precise localisation of the epileptogenic focus -
often approximated through the Seizure Onset Zone (SOZ) - is critical yet
remains a challenge. Active probing through electrical stimulation is already
standard clinical practice for identifying epileptogenic areas. This paper
advances the application of deep learning for SOZ localisation using Single
Pulse Electrical Stimulation (SPES) responses. We achieve this by introducing
Transformer models that incorporate cross-channel attention. We evaluate these
models on held-out patient test sets to assess their generalisability to unseen
patients and electrode placements.
  Our study makes three key contributions: Firstly, we implement an existing
deep learning model to compare two SPES analysis paradigms - namely, divergent
and convergent. These paradigms evaluate outward and inward effective
connections, respectively. Our findings reveal a notable improvement in moving
from a divergent (AUROC: 0.574) to a convergent approach (AUROC: 0.666),
marking the first application of the latter in this context. Secondly, we
demonstrate the efficacy of the Transformer models in handling heterogeneous
electrode placements, increasing the AUROC to 0.730. Lastly, by incorporating
inter-trial variability, we further refine the Transformer models, with an
AUROC of 0.745, yielding more consistent predictions across patients. These
advancements provide a deeper insight into SOZ localisation and represent a
significant step in modelling patient-specific intracranial EEG electrode
placements in SPES. Future work will explore integrating these models into
clinical decision-making processes to bridge the gap between deep learning
research and practical healthcare applications.
