EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition

TL;DR

EvoFA is an online adaptive framework that enhances EEG emotion recognition by combining meta-learning and distribution matching, enabling real-time performance despite non-stationary EEG signals.

Contribution

This paper introduces EvoFA, a novel online adaptive method that integrates meta-learning and distribution matching for EEG emotion recognition, addressing the limitations of existing domain adaptation approaches.

Findings

EvoFA significantly outperforms basic Few-Shot Learning methods.

EvoFA surpasses previous online adaptation techniques.

Experimental results confirm improved online EEG emotion recognition performance.

Abstract

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.