analiza aiqbench
DAH-Net demonstrates a significant reduction in the train-test gap to 0.81% while maintaining high accuracy on a small dataset, though its practical utility remains contingent on validation across diverse subjects and external datasets. The study highlights the distinct roles of covariance versus eigenvalue features in EEG analysis, offering a compact architecture that could facilitate lightweight affective computing applications.
DAH-Net: A Dual-Attention Hybrid Network for Interpretable and Robust EEG-Based Emotion Recognition
arXiv:2602.06411v2 Announce Type: replace Abstract: EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity, subject variability, and limited interpretability. We propose DAH-Net, a dual-attention hybrid network integrating 1D-CNN, BiLSTM, and dual multi-head attention (16+8 heads) for three-class EEG emotion classification. Evaluated on 2,479 samples with 988 EEG features, DAH-Net achieves 99.19% held-out test accuracy with a 0.81% train-test gap, outperforming RF (96.17%), SVM (96.77%), MLP (97.18%), and Transformer (98.19%) baselines. Friedman testing (\c{hi}2 = 28.54, p < 0.001) and post-hoc Wilcoxon comparisons confirm statistical significance. Feature-level analysis using Random Forest importance, SHAP attribution, and feature category isolation shows that covariance features achieve near-baseline standalone accuracy (94.96%), while eigenvalue features show limited standalone performance (84.07%) but provide compact complementary information. The compact architecture (3.33M parameters, approximately 13.3MB using 32-bit weights) suggests potential for future lightweight EEG-based affective computing, pending subject-independent and external validation.
Abstract:EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity, subject variability, and limited interpretability. We propose DAH-Net, a dual-attention hybrid network integrating 1D-CNN, BiLSTM, and dual multi-head attention (16+8 heads) for three-class EEG emotion classification. Evaluated on 2,479 samples with 988 EEG features, DAH-Net achieves 99.19% held-out test accuracy with a 0.81% train-test gap, outperforming RF (96.17%), SVM (96.77%), MLP (97.18%), and Transformer (98.19%) baselines. Friedman testing (\c{hi}2 = 28.54, p < 0.001) and post-hoc Wilcoxon comparisons confirm statistical significance. Feature-level analysis using Random Forest importance, SHAP attribution, and feature category isolation shows that covariance features achieve near-baseline standalone accuracy (94.96%), while eigenvalue features show limited standalone performance (84.07%) but provide compact complementary information. The compact architecture (3.33M parameters, approximately 13.3MB using 32-bit weights) suggests potential for future lightweight EEG-based affective computing, pending subject-independent and external validation.
Machine Learning (cs.LG)
arXiv:2602.06411 [cs.LG]
(or arXiv:2602.06411v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.06411 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 237 cuvinte
