EEG recordings are inherently contaminated by artifacts such as ocular, muscular, and environmental noise, which obscure neural activity and complicate preprocessing. Artifact classification offers advantages in stability and transparency, providing a viable alternative to ICA-based methods that enable flexible use alongside human inspections and across various applications. However, artifact classification is limited by its training data as it requires extensive manual labeling, which cannot fully cover the diversity of real-world EEG. Semi-synthetic data (SSD) methods have been proposed to address this limitation, but prior approaches typically injected single artifact types using ICA components or required separately recorded artifact signals, reducing both the realism of the generated data and the applicability of the method. To overcome these issues, we introduce SSDLabeler, a framework that generates realistic, annotated SSDs by decomposing real EEG with ICA, epoch-level artifact verification using RMS and PSD criteria, and reinjecting multiple artifact types into clean data. When applied to train a multi-label artifact classifier, it improved accuracy on raw EEG across diverse conditions compared to prior SSD and raw EEG training, establishing a scalable foundation for artifact handling that captures the co-occurrence and complexity of real EEG.

翻译：脑电图记录本质上受到眼动、肌肉和环境噪声等伪迹的污染，这些伪迹会掩盖神经活动并增加预处理的复杂性。伪迹分类在稳定性和透明度方面具有优势，为基于独立成分分析的方法提供了一种可行的替代方案，使其能够灵活地结合人工检查并适用于多种应用场景。然而，伪迹分类受限于其训练数据，因为它需要大量的人工标注，而人工标注无法完全覆盖真实脑电图数据的多样性。半合成数据方法已被提出以解决这一局限，但先前的方法通常使用独立成分分析成分注入单一类型的伪迹，或需要单独记录的伪迹信号，这降低了生成数据的真实性和方法的适用性。为克服这些问题，我们提出了SSDLabeler框架，该框架通过以下步骤生成真实且带标注的半合成数据：利用独立成分分析分解真实脑电图信号，基于均方根和功率谱密度准则进行分段级伪迹验证，并将多种伪迹类型重新注入清洁数据中。当应用于训练多标签伪迹分类器时，与先前的半合成数据及原始脑电图训练相比，该方法在不同条件下对原始脑电图数据的分类准确率均有提升，为捕捉真实脑电图中伪迹共现性和复杂性的处理流程建立了可扩展的基础。