The role of emotion in sleep: a quantitative analysis using EEG data

The intricate interplay between sleep and emotion has garnered increasing attention due to their profound impact on human health and well-being, including the development of interventions using emotion-regulating medications. While qualitative studies have illuminated their association, quantitative evidence remains limited.

To address this gap, we leverage deep learning and emotion priors to explore the quantitative relationship between sleep and emotion using EEG signals. Our approach introduces novel emotion-based features into sleep stage classification, providing additional abstract information and corroborating the sleep-emotion link.

This method enables targeted interventions with emotion-regulating medications tailored to specific sleep stages. Furthermore, we investigate the quantitative influence of emotional combinations (emotional codings) on sleep stages, revealing distinct "emotional fingerprints" during sleep.

These findings support the development of corresponding drug combinations for sleep interventions. These findings lay the foundation for developing scientifically grounded and quantifiable approaches to sleep and emotion regulation, paving the way for advancements in understanding and addressing sleep and emotional disorders. Statement of Significance This study bridges sleep science and affective neuroscience by integrating deep learning with EEG-based emotion priors for quantitative analysis. It reveals distinct "emotional fingerprints" across sleep stages, providing a scientific basis for emotion-regulating interventions tailored to specific stages of sleep. These findings advance the understanding of sleep-emotion interactions and lay the groundwork for developing precise, data-driven therapies for sleep and emotional disorders.