Premise. Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e., EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rubrics for long-term research progress remain unclear. Objective. In this work, we conduct a review of ten early EEG-FMs to capture common trends and identify key directions for future development of EEG-FMs. Methods. We comparatively analyze each EEG-FM using three fundamental pillars of foundation modeling, namely the representation of input data, self-supervised modeling, and the evaluation strategy. Based on this analysis, we present a critical synthesis of EEG-FM methodology, empirical findings, and outstanding research gaps. Results. We find that most EEG-FMs adopt a sequence-based modeling scheme that relies on transformer-based backbones and the reconstruction of masked temporal EEG sequences for self-supervision. However, model evaluations remain heterogeneous and largely limited, making it challenging to assess their practical off-the-shelf utility. In addition to adopting standardized and realistic evaluations, future work should demonstrate more substantial scaling effects and make principled and trustworthy choices throughout the EEG representation learning pipeline. Significance. Our review indicates that the development of benchmarks, software tools, technical methodologies, and applications in collaboration with domain experts may advance the translational utility and real-world adoption of EEG-FMs.

翻译：前提。通过脑电图（EEG）记录的大脑电活动模式为科学和临床研究提供了巨大价值。有监督的EEG编码器无法学习稳健的EEG模式及其对昂贵信号标注的过度依赖，已促使研究转向通用的自监督EEG编码器，即EEG基础模型（EEG-FMs），以实现稳健且可扩展的EEG特征提取。然而，早期EEG-FMs的实际应用准备度以及长期研究进展的评估标准仍不明确。目标。本文对十个早期EEG-FM进行了评述，以捕捉共同趋势并确定EEG-FM未来发展的关键方向。方法。我们使用基础建模的三个基本支柱，即输入数据的表示、自监督建模和评估策略，对每个EEG-FM进行了比较分析。基于此分析，我们对EEG-FM的方法论、实证发现以及突出的研究空白进行了批判性综合。结果。我们发现，大多数EEG-FM采用基于序列的建模方案，该方案依赖于基于Transformer的主干网络以及掩码时间EEG序列的重建来实现自监督。然而，模型评估仍然存在异质性且大多有限，这使得评估其实际开箱即用效用变得困难。除了采用标准化和现实的评估外，未来的工作应展示更显著的规模效应，并在整个EEG表示学习流程中做出有原则且可信赖的选择。意义。我们的评述表明，与领域专家合作开发基准、软件工具、技术方法和应用，可能会推动EEG-FMs的转化效用和实际应用。