# FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2503.18998 · HTML: https://mlsanity.com/paper/2503.18998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 64 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning](https://mlsanity.com/paper/2003.04390/) (2020)
- [A review of domain adaptation without target labels](https://mlsanity.com/paper/1901.05335/) (2019)
- [Model-Based Domain Generalization](https://mlsanity.com/paper/2102.11436/) (2021)
- [Sparse Mixture-of-Experts are Domain Generalizable Learners](https://mlsanity.com/paper/2206.04046/) (2022)
- [Cross-domain Few-shot Learning with Task-specific Adapters](https://mlsanity.com/paper/2107.00358/) (2021)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [Domain Generalization through Meta-Learning: A Survey](https://mlsanity.com/paper/2404.02785/) (2024)
- [MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition](https://mlsanity.com/paper/2107.07740/) (2021)
- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic Segmentation](https://mlsanity.com/paper/2404.02638/) (2024)
- [PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition](https://mlsanity.com/paper/2302.02520/) (2023)
- [GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition](https://mlsanity.com/paper/2205.01030/) (2022)
- [A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface](https://mlsanity.com/paper/2209.03785/) (2022)
- [Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition](https://mlsanity.com/paper/2305.07180/) (2023)
- [Progressive Graph Convolution Network for EEG Emotion Recognition](https://mlsanity.com/paper/2112.09069/) (2021)

## Abstract (arXiv)

Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Existing works have primarily addressed these challenges through domain adaptation or generalization strategies. However, they typically require extensive target subject data or demonstrate limited generalization performance to unseen subjects. Recent few-shot learning paradigms attempt to address these limitations but often encounter catastrophic overfitting during subject-specific adaptation with limited samples. This article introduces the few-shot adapter with a cross-view fusion method called FACE for cross-subject EEG emotion recognition, which leverages dynamic multi-view fusion and effective subject-specific adaptation. Specifically, FACE incorporates a cross-view fusion module that dynamically integrates global brain connectivity with localized patterns via subject-specific fusion weights to provide complementary emotional information. Moreover, the few-shot adapter module is proposed to enable rapid adaptation for unseen subjects while reducing overfitting by enhancing adapter structures with meta-learning. Experimental results on three public EEG emotion recognition benchmarks demonstrate FACE's superior generalization performance over state-of-the-art methods. FACE provides a practical solution for cross-subject scenarios with limited labeled data.