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

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.