A dual-branch network with brain region-constrained attention for EEG emotion recognition.

Overview

1. Key Laboratory of Linguistic and Cultural Computing, Ministry of Education, ‘National Languages Information Technology, Northwest Minzu University, Lanzhou, China
2. Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing, “National Languages Information Technology, Northwest Minzu University, Lanzhou, China

Abstract

Introduction: Electroencephalography (EEG)-based emotion recognition provides an objective avenue for affective computing. However, the complexity of EEG signals across temporal, frequency, and spatial domains makes any single dimension inadequate.

Methods: To overcome these limitations, we propose the Brain Region-Constrained Attention Dual-Branch Network (BRAD-Net). This network adopts a parallel Spatio-Temporal and Spectral-Spatial dual-branch architecture to achieve synergistic multi-domain EEG feature learning. Within the spatio-temporal branch, we introduce a novel Brain Region-Constrained Attention mechanism, which strictly confines self-attention computation to channels belonging to the same brain region. This design not only suppresses irrelevant cross-region interference but also incorporates neuroanatomical priors of brain parcellation, thereby enabling effective and interpretable representation learning.

Results: In subject-dependent experiments using 10-fold cross-validation on DEAP and DREAMER datasets, BRAD-Net achieves high accuracies of 97.44%, 97.70%, and 97.97% for valence, arousal, and dominance on DEAP, and 99.66%, 99.78%, and 99.80% on DREAMER, respectively. Leave-one-subject-out validation on DREAMER dataset achieves accuracies of 72.80% and 75.66% for arousal and dominance, respectively. Additionally, the BRAD-Net demonstrates strong cross-paradigm adaptability, achieving 98.21% accuracy on a depression classification dataset.

Conclusions: These findings confirm that integrating neuroanatomical priors into a dual-branch multi-dimensional learning framework effectively extracts robust and interpretable neural representations. BRAD-Net not only advances high-performance EEG emotion recognition but also provides a novel, biologically-constrained design paradigm for developing more interpretable brain-computer interface models. By demonstrating that restricting attention to within-brain-region interactions suffices for accurate emotion recognition, our work offers a new theoretical perspective on the application of brain parcellation knowledge in classification models.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• figshare:4244171, at figshare; found in “Data availability statement”
• zenodo:546113, at Zenodo; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. The DEAP dataset used in this study was introduced in https://doi.org/10.1109/T-AFFC.2011.15; the DREAMER dataset is available at https://zenodo.org/records/546113; and the depression detection dataset is available at https://figshare.com/articles/dataset/EEG_Data_New/4244171.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 59 references.

Cite

This paper

Hua, C., Cao, H., Li, Z., & Huang, Z. (2026). A dual-branch network with brain region-constrained attention for EEG emotion recognition. Frontiers in neuroscience, 20, 1810609. https://doi.org/10.3389/fnins.2026.1810609

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