Task-State EEG-Based Mental Stress Recognition Using Multi-Band Dynamic Attention Network

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The proposed model enhances mental stress detection accuracy during cognitive tasks, significantly outperforming current methods.
Experiments reveal accuracy improvements with the multiband dynamic attention network, achieving superior feature extraction capabilities.
Wavelet packet transform and frequency-domain attention contribute to robust representation of stress-related EEG features.
The approach highlights the importance of inter-band interactions in EEG-based mental state assessments.

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This analysis demonstrates improved mental stress detection using EEG signals, highlighting deep learning's role and cross-band interactions.

Wang et al. (2025) studied this question.

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This analysis demonstrates improved mental stress detection using EEG signals, highlighting deep learning's role and cross-band interactions.