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%),
The continuous advancements in deep learning architectures and neuroscience are converging to enable more robust and interpretable brain-computer interface applications.
Improved EEG-based emotion recognition can significantly enhance affective computing, mental health diagnostics, and human-computer interaction, opening new avenues for personalized care and adaptive systems.
The DAH-Net's high accuracy and interpretability reduce a major bottleneck in reliable EEG signal processing for emotional states, suggesting a more practical path towards real-world applications.
- · Neuroscience researchers
- · Mental health tech startups
- · BCI developers
- · AI model developers
- · Traditional EEG analysis methods
- · Companies with less sophisticated BCI algorithms
More accurate and reliable emotion detection via EEG becomes feasible for research and early applications.
The integration of such systems into consumer devices or clinical tools could accelerate, leading to proactive mental health monitoring.
Ethical considerations around emotional privacy and potential misuse of highly accurate emotion recognition technology will become more prominent.
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Read at arXiv cs.LG