The variability in EEG signals between different individuals poses a significant challenge when implementing brain-computer interfaces (BCI). Commonly proposed solutions to this problem include deep learning models, due to their increased capacity and generalization, as well as explicit domain adaptation techniques. Here, we introduce the Latent Alignment method that won the Benchmarks for EEG Transfer Learning (BEETL) competition and present its formulation as a deep set applied on the set of trials from a given subject. Its performance is compared to recent statistical domain adaptation techniques under various conditions. The experimental paradigms include motor imagery (MI), oddball event-related potentials (ERP) and sleep stage classification, where different well-established deep learning models are applied on each task. Our experimental results show that performing statistical distribution alignment at later stages in a deep learning model is beneficial to the classification accuracy, yielding the highest performance for our proposed method. We further investigate practical considerations that arise in the context of using deep learning and statistical alignment for EEG decoding. In this regard, we study class-discriminative artifacts that can spuriously improve results for deep learning models, as well as the impact of class-imbalance on alignment. We delineate a trade-off relationship between increased classification accuracy when alignment is performed at later modeling stages, and susceptibility to class-imbalance in the set of trials that the statistics are computed on.

翻译：不同个体间脑电图信号的可变性对脑-机接口（BCI）的实际应用构成重大挑战。针对该问题，现有解决方案通常采用深度学习模型（因其强大的容量与泛化能力）及显式域适应技术。本文提出赢得脑电图迁移学习基准（BEETL）竞赛的潜层对齐方法，并将其形式化为对受试者试验集进行深度集操作。我们在多种条件下将所提方法与最新统计域适应技术进行性能对比。实验范式涵盖运动想象（MI）、oddball事件相关电位（ERP）与睡眠阶段分类任务，针对每项任务分别应用成熟的深度学习模型。实验结果表明，在深度学习模型后期阶段实施统计分布对齐有利于提升分类准确率，且我们提出的方法取得了最佳性能。进一步探讨了深度学习与统计对齐联合应用于脑电图解码时的实践考量，包括可能虚假提升深度学习模型效果的类别判别性伪迹，以及类别不平衡对对齐的影响。研究表明，阶段对齐提升分类准确率与统计计算所用的试验集类别不平衡敏感性之间存在权衡关系。