  Deep learning based electroencephalography (EEG) signal processing methods
are known to suffer from poor test-time generalization due to the changes in
data distribution. This becomes a more challenging problem when
privacy-preserving representation learning is of interest such as in clinical
settings. To that end, we propose a multi-source learning architecture where we
extract domain-invariant representations from dataset-specific private
encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain
alignment approach to impose domain-invariance for encoded representations,
which outperforms state-of-the-art approaches in EEG-based emotion
classification. Furthermore, representations learned in our pipeline preserve
domain privacy as dataset-specific private encoding alleviates the need for
conventional, centralized EEG-based deep neural network training approaches
with shared parameters.
