Omni-iEEG：一个大规模、全面的iEEG数据集和癫痫研究基准
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
AI总结：
Omni-iEEG提供了一个大规模的iEEG数据集和基准，用于癫痫研究，包含302名患者178小时的高分辨率记录及36000个专家验证的病理事件注释，旨在提升癫痫研究的可重复性和临床相关性。
摘要：
癫痫影响全球超过5000万人，其中三分之一的患者患有药物难以控制的癫痫发作，手术是获得癫痫自由的最佳机会。准确确定癫痫发作区（EZ）依赖于颅内EEG（iEEG）。然而，临床工作流程仍然受到劳动密集型手动审查的限制。同时，现有数据驱动的方法通常是在单中心数据集上开发的，这些数据集在格式和元数据上不一致，缺乏标准化的基准，并且很少发布病理事件注释，这阻碍了可重复性、跨中心验证和临床相关性。在大量努力协调异构iEEG格式、元数据和记录后，我们提出了$\textbf{Omni-iEEG}$，一个大规模的术前iEEG资源，包含$\textbf{302名患者}$和$\textbf{178小时}$的高分辨率记录。该数据集包括协调的临床元数据，如癫痫发作起始区、切除和手术结果，均经过认证的癫痫专家验证。此外，Omni-iEEG提供了超过36000个专家验证的病理事件注释，使稳健的生物标志物研究成为可能。Omni-iEEG在机器学习和癫痫研究之间架起桥梁。它定义了具有统一评估指标的临床有意义的任务，这些指标基于临床先验知识，使模型在临床相关环境中进行系统评估。除了基准测试外，我们还展示了端到端模型在长iEEG段上的潜力，并突出了在非神经生理学领域预训练的表示的可转移性。这些贡献使Omni-iEEG成为可重复、可推广和临床可转换的癫痫研究的基础。项目页面和数据集及代码链接可在omni-ieeg.github.io/omni-ieeg上找到。
英文摘要：
Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG). Clinical workflows, however, remain constrained by labor-intensive manual review. At the same time, existing data-driven approaches are typically developed on single-center datasets that are inconsistent in format and metadata, lack standardized benchmarks, and rarely release pathological event annotations, creating barriers to reproducibility, cross-center validation, and clinical relevance. With extensive efforts to reconcile heterogeneous iEEG formats, metadata, and recordings across publicly available sources, we present $\textbf{Omni-iEEG}$, a large-scale, pre-surgical iEEG resource comprising $\textbf{302 patients}$ and $\textbf{178 hours}$ of high-resolution recordings. The dataset includes harmonized clinical metadata such as seizure onset zones, resections, and surgical outcomes, all validated by board-certified epileptologists. In addition, Omni-iEEG provides over 36K expert-validated annotations of pathological events, enabling robust biomarker studies. Omni-iEEG serves as a bridge between machine learning and epilepsy research. It defines clinically meaningful tasks with unified evaluation metrics grounded in clinical priors, enabling systematic evaluation of models in clinically relevant settings. Beyond benchmarking, we demonstrate the potential of end-to-end modeling on long iEEG segments and highlight the transferability of representations pretrained on non-neurophysiological domains. Together, these contributions establish Omni-iEEG as a foundation for reproducible, generalizable, and clinically translatable epilepsy research. The project page with dataset and code links is available at omni-ieeg.github.io/omni-ieeg.
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