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.

翻译：癫痫是最常见的神经系统疾病之一，当药物无法控制癫痫发作时，许多患者需要手术干预。为实现有效的手术效果，精确定位致痫灶（通常通过癫痫发作起始区近似表示）至关重要，但仍是临床挑战。通过电刺激进行主动探测已成为识别致痫区的标准临床实践。本文利用单脉冲电刺激响应推进深度学习在癫痫发作起始区定位中的应用。我们通过引入具备跨通道注意力机制的Transformer模型实现这一目标。我们在留出患者测试集上评估这些模型，以检验其对未见患者及电极放置位置的泛化能力。本研究贡献三方面：首先，我们实现了一种现有深度学习模型，比较两种单脉冲电刺激分析范式——即发散与收敛范式。这两种范式分别评估外向与内向有效连接。研究结果表明，从发散方法（AUROC：0.574）转向收敛方法（AUROC：0.666）后性能显著提升，这也是后者在该领域的首次应用。其次，我们证明了Transformer模型在处理异质性电极放置位置时的有效性，将AUROC提升至0.730。最后，通过纳入试验间变异性，我们进一步优化Transformer模型，使AUROC达到0.745，为患者提供更一致的预测结果。这些进展深化了对癫痫发作起始区定位的理解，标志着在单脉冲电刺激中建模患者特异性颅内脑电图电极放置位置的重要突破。未来工作将探索将这些模型整合至临床决策流程，以弥合深度学习研究与实际医疗应用之间的鸿沟。