Domain-Invariant Representation Learning from EEG with Private Encoders

David Bethge · Philipp Hallgarten · Tobias Große-Puppendahl · Mohamed Kari · Ralf Mikut · Albrecht Schmidt +1 more

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Domain Adaptation and Few-Shot Learning

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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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2015Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks 2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions 2013Differential entropy feature for EEG-based emotion classification 2017DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices 2014A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges 2021MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition

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