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January 29, 2026CCF Transactions on Pervasive Computing and Interaction

An efficient methodology for EEG-based music emotion recognition with optimal windowing using DPFQN and EDRW

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Key Points

Emotion recognition accuracy improves with optimal windowing techniques, addressing previous limitations.
Key evidence includes a significant increase in recognition rates through the application of DPFQN and EDRW.
Analysis of EEG signals reveals that optimized windowing enhances data processing and emotion classification.
Improved methodologies may enable more effective music therapy interventions for emotional well-being.

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Implication

Observational analysis demonstrates improved emotion recognition accuracy in EEG data, suggesting practical applications in music therapy.

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Cite This Study

Han Xue (2026) studied this question.

synapsesocial.com/papers/69a75d6fc6e9836116a27775 https://doi.org/10.1007/s42486-025-00223-x

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