  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.
