In practical sleep stage classification, a key challenge is the variability of EEG data across different subjects and environments. Differences in physiology, age, health status, and recording conditions can lead to domain shifts between data. These domain shifts often result in decreased model accuracy and reliability, particularly when the model is applied to new data with characteristics different from those it was originally trained on, which is a typical manifestation of negative transfer. To address this, we propose SelectiveFinetuning in this paper. Our method utilizes a pretrained Multi Resolution Convolutional Neural Network (MRCNN) to extract EEG features, capturing the distinctive characteristics of different sleep stages. To mitigate the effect of domain shifts, we introduce a domain aligning mechanism that employs Earth Mover Distance (EMD) to evaluate and select source domain data closely matching the target domain. By finetuning the model with selective source data, our SelectiveFinetuning enhances the model's performance on target domain that exhibits domain shifts compared to the data used for training. Experimental results show that our method outperforms existing baselines, offering greater robustness and adaptability in practical scenarios where data distributions are often unpredictable.

本研究针对不同受试者和环境下EEG数据的变异性问题，提出了一种选择性微调方法，以克服在新数据上应用模型时出现的负转移现象。通过引入领域对齐机制，采用地球移动者距离（EMD）选择与目标领域紧密匹配的源领域数据，本方法在提高模型在受领域转移影响的数据上的表现方面显示了显著的优势，具有更强的鲁棒性和适应性。

选择性微调：通过选择性领域对齐提升睡眠阶段转移学习