The success of foundation models in natural language processing and computer vision has motivated similar approaches in time series analysis. While foundational time series models have proven beneficial on a variety of tasks, their effectiveness in medical applications with limited data remains underexplored. In this work, we investigate this question in the context of electroencephalography (EEG) by evaluating general-purpose time series models on age prediction, seizure detection, and classification of clinically relevant EEG events. We compare their diagnostic performance against specialised EEG models and assess the quality of the extracted features. The results show that general-purpose models are competitive and capture features useful to localising demographic and disease-related biomarkers. These findings indicate that foundational time series models can reduce the reliance on large task-specific datasets and models, making them valuable in clinical practice.

翻译：自然语言处理和计算机视觉领域基础模型的成功，激发了时间序列分析中类似方法的探索。尽管基础性时间序列模型已在多种任务中证明其有效性，但它们在数据有限的医学应用中的效能仍未得到充分研究。本研究通过评估通用时间序列模型在年龄预测、癫痫检测及临床相关脑电图事件分类任务上的表现，在脑电图分析背景下探讨这一问题。我们将其诊断性能与专用脑电图模型进行比较，并评估所提取特征的质量。结果表明，通用模型具有竞争力，并能捕捉到对定位人口统计学和疾病相关生物标志物有用的特征。这些发现表明，基础性时间序列模型可以减少对大规模任务特定数据集和模型的依赖，使其在临床实践中具有重要价值。