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

--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 F ACE for cross-subject EEG emotion recognition, which leverages dynamic multi-view fusion and effective subject-specific adaptation. Specifically, F ACE 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 F ACE's superior generalization performance over state-of-the-art methods. F ACE provides a practical solution for cross-subject scenarios with limited labeled data. NDERST ANDING Human emotions is fundamental and crucial to advancing fields such as human-computer interaction [1] and mental health [2]. Electroencephalography (EEG) has recently emerged as a remarkable tool for capturing subject's neural responses to emotional states [3]. EEG-based emotion recognition remains challenging due to the substantial inter-subject variance in brain activity patterns [4], [5]. Additionally, intra-subject variance arises from the non-stationary nature of EEG signals, which exhibit variations in frequency and amplitude over time within the same subject. Comparison of training data and processes between Few-Shot Learning (FSL) and traditional deep learning (DL) in cross-subject EEG emotion recognition.