# Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation

arXiv: https://arxiv.org/abs/2504.03707 · HTML: https://mlsanity.com/paper/2504.03707/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Source-Free Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2103.16372/) (2021)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [Source-Free Domain Adaptation via Distribution Estimation](https://mlsanity.com/paper/2204.11257/) (2022)
- [VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation](https://mlsanity.com/paper/2103.14357/) (2021)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation](https://mlsanity.com/paper/2205.14566/) (2022)
- [Model Adaptation: Unsupervised Domain Adaptation without Source Data](https://mlsanity.com/paper/2502.19316/) (2025)
- [Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation](https://mlsanity.com/paper/2109.04015/) (2021)
- [CoNMix for Source-free Single and Multi-target Domain Adaptation](https://mlsanity.com/paper/2211.03876/) (2022)
- [Source-Free Domain Adaptation with Temporal Imputation for Time Series Data](https://mlsanity.com/paper/2307.07542/) (2023)
- [Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2208.14888/) (2022)
- [SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection](https://mlsanity.com/paper/2205.12840/) (2022)
- [Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification](https://mlsanity.com/paper/2201.01468/) (2022)
- [Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation](https://mlsanity.com/paper/2411.12852/) (2024)

## Built on by (most cited first, 2 of 2)

- [LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition](https://mlsanity.com/paper/2504.09156/) (2025)
- [Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding](https://mlsanity.com/paper/2605.00857/) (2026)

## Abstract (arXiv)

Emotion recognition is crucial for advancing mental health, healthcare, and technologies such as brain-computer interfaces. EEG-based models, however, struggle in cross-domain settings due to the high cost of labeled data and signal variability across individuals and recording conditions. Unsupervised domain adaptation typically requires access to source data, which is often infeasible because of privacy and computational constraints. Source-free unsupervised domain adaptation (SF-UDA) removes this requirement, but it has not yet been applied to emotion recognition. We propose an SF-UDA approach for cross-domain EEG emotion classification, built on a multi-stage framework that adapts to the target domain without source data. Dual-Loss Adaptive Regularization (DLAR) minimizes prediction discrepancies on confident samples and aligns predictions with expected pseudo-labels. Localized Consistency Learning (LCL) enforces local consistency by promoting similar predictions among reliable neighbors. Together, these components address domain shift and reduce the impact of noisy pseudo-labels, a key challenge in SF-UDA. Experiments on DEAP, SEED, and DREAMER show that our method significantly outperforms state-of-the-art approaches, reaching 65.84% and 58.87% accuracy on SEED and DREAMER when trained on DEAP, and 58.99% and 67.08% on DEAP and DREAMER when trained on SEED. It detects both positive and negative emotions well, making it suitable for practical emotion recognition applications. Code available at: https://github.com/RyersonMultimediaLab/EmotionRecognitionSF-UDA