mlsanityData to 13 Oct 2026

Domain-Invariant Representation Learning from EEG with Private Encoders

arXiv 2201.11613 ↗First posted 27 Jan 2022cs.LG cs.CV cs.HCcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 27 Jan 2022.

Builds on

9 of 22 references.

References · 9

  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,030
  2. 2015Generating Sentences from a Continuous Spacecited by 609
  3. 2016Revisiting Batch Normalization For Practical Domain Adaptationcited by 286
  4. 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
  5. 2020Federated Transfer Learning for EEG Signal Classificationcited by 36
  6. 2021MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognitioncited by 25
  7. 2019Protecting Privacy of Users in Brain-Computer Interface Applicationscited by 13
  8. 2017Personalized Brain-Computer Interface Models for Motor Rehabilitationcited by 1
  9. 2018Time-Series Prediction of Proximal Aggression Onset in Minimally-Verbal Youth with Autism Spectrum Disorder Using Physiological Biosignalscited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 6; 2023: 2; 2024: 1; 2025: 1; 2026: 2.

12 papers.

  1. 2022EEG2Vec: Learning Affective EEG Representations via Variational Autoencoderscited by 15
  2. 2023EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphscited by 7
  3. 2022Exploiting Multiple EEG Data Domains with Adversarial Learningcited by 5
  4. 2022Domain Adaptation and Generalization on Functional Medical Images: A Systematic Surveycited by 4
  5. 2023TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learningcited by 1
  6. 2024Playing With Neuroscience: Past, Present and Future of Neuroimaging and Gamescited by 1
  7. 2025Unsupervised Pairwise Learning Optimization Framework for Cross-Corpus EEG-Based Emotion Recognition Based on Prototype Representationcited by 1
  8. 2026Cross-Subject Generalization in Decoding Perceived Speech from Non-Invasive Brain Recordingscited by 1
  9. 2022Hashing Learning with Hyper-Class Representationcited by 0
  10. 2022Federated deep transfer learning for EEG decoding using multiple BCI taskscited by 0
  11. 2022PiRL: Participant-Invariant Representation Learning for Healthcarecited by 0
  12. 2026Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiologycited by 0