# EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2409.15733 · HTML: https://mlsanity.com/paper/2409.15733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Probabilistic Model-Agnostic Meta-Learning](https://mlsanity.com/paper/1806.02817/) (2018)
- [Cycle Self-Training for Domain Adaptation](https://mlsanity.com/paper/2103.03571/) (2021)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices](https://mlsanity.com/paper/2208.02953/) (2022)
- [Real-time EEG-based Emotion Recognition Model using Principal Component Analysis and Tree-based Models for Neurohumanities](https://mlsanity.com/paper/2401.15743/) (2024)

## Abstract (arXiv)

Electroencephalography (EEG)-based emotion recognition has gained significant traction due to its accuracy and objectivity. However, the non-stationary nature of EEG signals leads to distribution drift over time, causing severe performance degradation when the model is reused. While numerous domain adaptation (DA) approaches have been proposed in recent years to address this issue, their reliance on large amounts of target data for calibration restricts them to offline scenarios, rendering them unsuitable for real-time applications. To address this challenge, this paper proposes Evolvable Fast Adaptation (EvoFA), an online adaptive framework tailored for EEG data. EvoFA organically integrates the rapid adaptation of Few-Shot Learning (FSL) and the distribution matching of Domain Adaptation (DA) through a two-stage generalization process. During the training phase, a robust base meta-learning model is constructed for strong generalization. In the testing phase, a designed evolvable meta-adaptation module iteratively aligns the marginal distribution of target (testing) data with the evolving source (training) data within a model-agnostic meta-learning framework, enabling the model to learn the evolving trends of testing data relative to training data and improving online testing performance. Experimental results demonstrate that EvoFA achieves significant improvements compared to the basic FSL method and previous online methods. The introduction of EvoFA paves the way for broader adoption of EEG-based emotion recognition in real-world applications. Our code will be released upon publication.