# Exploiting Multiple EEG Data Domains with Adversarial Learning

arXiv: https://arxiv.org/abs/2204.07777 · HTML: https://mlsanity.com/paper/2204.07777/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Adversarial Multi-task Learning for Text Classification](https://mlsanity.com/paper/1704.05742/) (2017)
- [Domain-Invariant Representation Learning from EEG with Private Encoders](https://mlsanity.com/paper/2201.11613/) (2022)
- [Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction](https://mlsanity.com/paper/2008.11426/) (2020)
- [Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data](https://mlsanity.com/paper/2008.10726/) (2020)
- [Adversarial Deep Learning in EEG Biometrics](https://mlsanity.com/paper/1903.11673/) (2019)
- [Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders](https://mlsanity.com/paper/2009.13453/) (2020)
- [Information Theoretic Feature Transformation Learning for Brain Interfaces](https://mlsanity.com/paper/1903.12235/) (2019)

## Built on by (most cited first, 5 of 5)

- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)
- [Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities](https://mlsanity.com/paper/2211.02624/) (2022)
- [TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning](https://mlsanity.com/paper/2306.06522/) (2023)
- [Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition](https://mlsanity.com/paper/2607.04139/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are highly subject-dependent, and are bound to the equipment and experimental setup used, (i.e. domain). This leads to machine learning models often suffer from poor generalization ability, where they perform significantly worse on real-world data than on the exploited training data. Recent research heavily focuses on cross-subject and cross-session transfer learning frameworks to reduce domain calibration efforts for EEG signals. We argue that multi-source learning via learning domain-invariant representations from multiple data-sources is a viable alternative, as the available data from different EEG data-source domains (e.g., subjects, sessions, experimental setups) grow massively. We propose an adversarial inference approach to learn data-source invariant representations in this context, enabling multi-source learning for EEG-based brain-computer interfaces. We unify EEG recordings from different source domains (i.e., emotion recognition datasets SEED, SEED-IV, DEAP, DREAMER), and demonstrate the feasibility of our invariant representation learning approach in suppressing data-source-relevant information leakage by 35% while still achieving stable EEG-based emotion classification performance.