Electroencephalogram (EEG)-based seizure subtype classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutualSHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Divergence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification.

翻译：基于脑电图（EEG）的癫痫亚型分类可提升临床诊断效率。无源半监督域自适应（SF-SSDA）方法可将预训练模型迁移至无源数据且目标域标记数据有限的新数据集，适用于隐私保护的癫痫亚型分类任务。本文针对基于EEG的癫痫亚型分类中SF-SSDA面临的两大挑战展开研究：1）如何在分类器设计中有效融合原始EEG数据与专家知识？2）如何实现SF-SSDA中源域与目标域的分布对齐？我们提出一种基于知识与数据融合的SF-SSDA方法——KDF-MutualSHOT，用于基于EEG的癫痫亚型分类。在源模型训练阶段，KDF采用Jensen-Shannon散度促进基于特征驱动的决策树模型与基于数据驱动的Transformer模型之间的相互学习。为将KDF适配至新目标数据集，我们开发了SF-SSDA算法MutualSHOT，其核心为基于一致性的伪标签选择策略。在公开数据集TUSZ与CHSZ上的实验表明，KDF-MutualSHOT在跨被试癫痫亚型分类任务中优于其他监督学习方法及无源域自适应方法。