Domain-Invariant Representation Learning from EEG with Private Encoders

• URL: http://arxiv.org/abs/2201.11613v1
• Date: Thu, 27 Jan 2022 16:14:26 GMT
• Title: Domain-Invariant Representation Learning from EEG with Private Encoders
• Authors: David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari, Ralf Mikut, Albrecht Schmidt, Ozan \"Ozdenizci
• Abstract summary: We propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model outperforms state-of-the-art approaches in EEG-based emotion classification.
• Score: 19.704138140955745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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