Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using new regularization techniques during model training. We propose several graphical models to describe an EEG classification task. From each model, we identify statistical relationships that should hold true in an idealized training scenario (with infinite data and a globally-optimal model) but that may not hold in practice. We design regularization penalties to enforce these relationships in two stages. First, we identify suitable proxy quantities (divergences such as Mutual Information and Wasserstein-1) that can be used to measure statistical independence and dependence relationships. Second, we provide algorithms to efficiently estimate these quantities during training using secondary neural network models. We conduct extensive computational experiments using a large benchmark EEG dataset, comparing our proposed techniques with a baseline method that uses an adversarial classifier. We find our proposed methods significantly increase balanced accuracy on test subjects and decrease overfitting. The proposed methods exhibit a larger benefit over a greater range of hyperparameters than the baseline method, with only a small computational cost at training time. These benefits are largest when used for a fixed training period, though there is still a significant benefit for a subset of hyperparameters when our techniques are used in conjunction with early stopping regularization.

翻译：脑电图数据的分类模型在评估未见过的测试被试时，性能会大幅下降。我们通过在模型训练过程中采用新的正则化技术来减少这种性能下降。我们提出了几种图模型来描述脑电图分类任务。从每个模型中，我们识别出在理想化训练场景（拥有无限数据和全局最优模型）中应成立的统计关系，但实际中这些关系可能并不成立。我们设计了两阶段的正则化惩罚项来强制执行这些关系。首先，我们确定合适的代理量（如互信息和Wasserstein-1散度），用于衡量统计独立性和依赖性关系。其次，我们提供算法，在训练期间利用辅助神经网络模型高效估计这些量。我们使用大型基准脑电图数据集进行了广泛的计算实验，将所提出的技术与使用对抗性分类器的基线方法进行了对比。我们发现，所提出的方法显著提高了测试被试的平衡准确率，并减少了过拟合。与基线方法相比，所提出的方法在更广泛的超参数范围内展现出更大的优势，且训练时仅增加少量计算成本。当在固定训练周期内使用时，这些优势最为显著；但当我们的技术与早停正则化结合使用时，部分超参数仍能获得显著的性能提升。