# EEG–fNIRS Cross-Subject Emotion Recognition Based on Attention Graph Isomorphism Network and Contrastive Learning

## TL;DR

This paper introduces a new method for emotion recognition using EEG and fNIRS data that improves accuracy and generalization across subjects.

## Contribution

The novel DC-AGIN model combines attention graph isomorphism networks with contrastive learning to enhance cross-subject emotion recognition.

## Key findings

- DC-AGIN achieves 96.98% accuracy in subject-dependent four-class emotion classification.
- The model reaches 62.56% accuracy under subject-independent leave-one-subject-out validation.
- DC-AGIN outperforms existing models in cross-subject emotion recognition tasks.

## Abstract

Background/Objectives: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively capture the spatiotemporal dynamics of brain activity during affective cognition, and their combination is promising for improving emotion recognition. However, multi-modal cross-subject emotion recognition remains challenging due to heterogeneous signal characteristics that hinder effective fusion and substantial inter-subject variability that degrades generalization to unseen subjects. Methods: To address these issues, this paper proposes DC-AGIN, a dual-contrastive learning attention graph isomorphism network for EEG–fNIRS emotion recognition. DC-AGIN employs an attention-weighted AGIN encoder to adaptively emphasize informative brain-region topology while suppressing redundant connectivity noise. For cross-modal fusion, a cross-modal contrastive learning module projects EEG and fNIRS representations into a shared latent semantic space, promoting semantic alignment and complementarity across modalities. Results: To further enhance cross-subject generalization, a supervised contrastive learning mechanism is introduced to explicitly mitigate subject-specific identity information and encourage subject-invariant affective representations. Experiments on a self-collected dataset are conducted under both subject-dependent five-fold cross-validation and subject-independent leave-one-subject-out (LOSO) protocols. The proposed method achieves 96.98% accuracy in four-class classification in the subject-dependent setting and 62.56% under LOSO. Compared with existing models, DC-AGIN achieves SOTA performance. Conclusions: These results demonstrate that the work on attention aggregation, cross-modal and cross-subject contrastive learning enables more robust EEG-fNIRS emotion recognition, thus supporting the effectiveness of DC-AGIN in generalizable emotion representation learning.