# Enhancing Cross-Dataset EEG Emotion Recognition: A Novel Approach with Emotional EEG Style Transfer Network

arXiv: https://arxiv.org/abs/2403.16540 · HTML: https://mlsanity.com/paper/2403.16540/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2024.

## Built on by (most cited first, 4 of 4)

- [Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition](https://mlsanity.com/paper/2404.09559/) (2024)
- [Multi-dataset Joint Pre-training of Emotional EEG Enables Generalizable Affective Computing](https://mlsanity.com/paper/2510.22197/) (2025)
- [EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition](https://mlsanity.com/paper/2408.09186/) (2024)
- [HEDN: A Hard-Easy Dual Network with Source Reliability Assessment for Cross-Subject EEG Emotion Recognition](https://mlsanity.com/paper/2511.06782/) (2025)

## Abstract (arXiv)

Recognizing the pivotal role of EEG emotion recognition in the development of affective Brain-Computer Interfaces (aBCIs), considerable research efforts have been dedicated to this field. While prior methods have demonstrated success in intra-subject EEG emotion recognition, a critical challenge persists in addressing the style mismatch between EEG signals from the source domain (training data) and the target domain (test data). To tackle the significant inter-domain differences in cross-dataset EEG emotion recognition, this paper introduces an innovative solution known as the Emotional EEG Style Transfer Network (E2STN). The primary objective of this network is to effectively capture content information from the source domain and the style characteristics from the target domain, enabling the reconstruction of stylized EEG emotion representations. These representations prove highly beneficial in enhancing cross-dataset discriminative prediction. Concretely, E2STN consists of three key modulestextemdash transfer module, transfer evaluation module, and discriminative prediction moduletextemdash which address the domain style transfer, transfer quality evaluation, and discriminative prediction, respectively. Extensive experiments demonstrate that E2STN achieves state-of-the-art performance in cross-dataset EEG emotion recognition tasks.