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.
@article{arxiv.2201.11613,
title = {Domain-Invariant Representation Learning from EEG with Private Encoders},
author = {David Bethge and Philipp Hallgarten and Tobias Grosse-Puppendahl and Mohamed Kari and Ralf Mikut and Albrecht Schmidt and Ozan Özdenizci},
journal= {arXiv preprint arXiv:2201.11613},
year = {2022}
}