The success of foundation models in natural language processing and computer vision has motivated similar approaches for general time series analysis. While these models are effective for a variety of tasks, their applicability in medical domains with limited data remains largely unexplored. To address this, we investigate the effectiveness of foundation models in medical time series analysis involving electroencephalography (EEG). Through extensive experiments on tasks such as age prediction, seizure detection, and the classification of clinically relevant EEG events, we compare their diagnostic accuracy with that of specialised EEG models. Our analysis shows that foundation models extract meaningful EEG features, outperform specialised models even without domain adaptation, and localise task-specific biomarkers. Moreover, we demonstrate that diagnostic accuracy is substantially influenced by architectural choices such as context length. Overall, our study reveals that foundation models with general time series understanding eliminate the dependency on large domain-specific datasets, making them valuable tools for clinical practice.

翻译：基础模型在自然语言处理和计算机视觉领域的成功，激发了将其应用于通用时间序列分析的类似方法。尽管这些模型在多种任务中表现有效，但它们在数据有限的医学领域的适用性仍很大程度上未被探索。为此，我们研究了基础模型在涉及脑电图（EEG）的医学时间序列分析中的有效性。通过在对年龄预测、癫痫发作检测以及临床相关EEG事件分类等任务上进行广泛实验，我们将其诊断准确性与专门的EEG模型进行了比较。我们的分析表明，基础模型能够提取有意义的EEG特征，即使在没有领域适应的情况下也优于专门的模型，并能定位任务特定的生物标志物。此外，我们证明诊断准确性在很大程度上受到诸如上下文长度等架构选择的影响。总体而言，我们的研究表明，具备通用时间序列理解能力的基础模型消除了对大型领域特定数据集的依赖，使其成为临床实践中的宝贵工具。