While analytics of sleep electroencephalography (EEG) holds certain
advantages over other methods in clinical applications, high variability across
subjects poses a significant challenge when it comes to deploying machine
learning models for classification tasks in the real world. In such instances,
machine learning models that exhibit exceptional performance on a specific
dataset may not necessarily demonstrate similar proficiency when applied to a
distinct dataset for the same task. The scarcity of high-quality biomedical
data further compounds this challenge, making it difficult to evaluate the
model's generality comprehensively. In this paper, we introduce Transfer
Euclidean Alignment - a transfer learning technique to tackle the problem of
the dearth of human biomedical data for training deep learning models. We
tested the robustness of this transfer learning technique on various rule-based
classical machine learning models as well as the EEGNet-based deep learning
model by evaluating on different datasets, including human and mouse data in a
binary classification task of detecting individuals with versus without
traumatic brain injury (TBI). By demonstrating notable improvements with an
average increase of 14.42% for intraspecies datasets and 5.53% for interspecies
datasets, our findings underscore the importance of the use of transfer
learning to improve the performance of machine learning and deep learning
models when using diverse datasets for training.

本文介绍了一种转移学习技术，Transfer Euclidean Alignment，用于解决在临床应用中部署机器学习模型时由于主体间的高度变异性而面临的挑战。通过在各种基于规则的经典机器学习模型以及基于 EEGNet 的深度学习模型上进行评估，包括人类和小鼠数据在内的不同数据集的二分类任务中，证明了该转移学习技术的稳健性，在种内数据集上平均提高了 14.42％，在种间数据集上提高了 5.53％，强调了在使用多样化数据集进行训练时使用转移学习来改善机器学习和深度学习模型的性能的重要性。