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.

基于深度学习模型和统计领域适应技术，本研究介绍了获得 EEG 转移学习竞赛冠军的 Latent Alignment 方法，并将其表述为应用于给定受试者试验集的深度集合；实验结果表明，在深度学习模型的较晚阶段进行统计分布对齐有助于分类准确性，同时研究了深度学习模型中可能产生虚假改善结果的类别区分偏差以及类别不平衡对对齐的影响。