Whilst interest in music as an aide to sleep has circulated amongst neuroscience and clinical studies there has been little focus from music research further investigating the musical content and perception in a sleeping context. This project proposes to utilise a musicological perspective with techniques and tools from neuroscience and psychology to address how music listening affects sleep experiences and outcomes. Specifically, the project aims to analyse how differing spatial relationships to the sound sources within music influence sleep outcomes. The aim will be to form categorisations and analyses of these relationships and environments to form a picture of how these spatial experiences influence people’s sleep.

The absence of comprehensive and standardized data structures for sleep recordings presents a significant obstacle for sleep researchers. In this project, we aim to establish a robust framework for organizing and analyzing sleep data by integrating neurophysiological sleep recordings, particularly wearable EEG data, with diverse non-physiological data, such as music datasets and annotations. The primary objective is to address the current lack of comprehensive and standardized data structures for sleep recordings, thereby laying the groundwork for interdisciplinary collaborations and facilitating data sharing in sleep research. Methodologically, the project covers data annotation, preprocessing, scheme development, access and management, as well as the incorporation of data analysis tools. 

Research at the University of Stuttgart is based on the premise of subconscious preferences towards auditory/musical stimuli during sleep, which are thought to affect the behaviour, and therefore quality, of an individual’s sleep. The existence of these preferences has been previously indicated, both by the subjectivity of conscious musical preference and by the variety present within available sleep music. We would therefore like to establish a formal description of the relationship between auditory/musical features and the resultant sleep quality, using personalised models based on contemporary machine learning techniques. Our approach aims for a widespread, general applicability, creating new ways of representing sleep which go beyond the traditional models developed for clinical contexts (e.g. treating insomnia). We also want to test popular conceptions (and misconceptions) regarding sleep music, based on scientific evidence and empirical data rather than the folklore which currently influences many audio-related sleep aids. The results produced are intended to be interpretable, promising great relevance to sleep science as a whole.

Music has been used as a sleep aid since ancient times and in different cultures around the world. In modern times, with insomnia representing a major health problem, and with continuous access to music, many people report using music to fall asleep. Despite this, little attention has been paid both on the specific individual characteristics of people listening to music for sleep and on the characteristics of music used to sleep. In this project, we will investigate and model the influence of demographic variables, music preferences and habits, and psychological factors on the choice of music for sleep. We will then study the impact of different types of music on sleep, and in particular on sleep initiation and consolidation, using both neuroimaging and behavioural methods.

Sleep is a fundamental physiological state that plays a critical role in memory consolidation, allowing the brain to efficiently process and integrate sensory information from prior wakeful experiences. While extensive research has explored the effects of sensory stimuli during wake and sleep, a noticeable gap exists in understanding the brain’s reaction to complex auditory stimuli, especially distinguishable features in musical streams. The primary objective is to investigate whether sleep influences the ability to discriminate between musical and non-musical sounds. Specifically, we aim to examine the temporal dynamics of auditory processing during sleep, to characterise when and how musical processing diminishes across sleep stages. Additionally, we will investigate the effect of neurophysiological signatures (slow-waves, k-complexes etc) on musical expectations and explore networks and states that reflect conscious access during sleep. Our studies will utilise current approaches in machine learning to decode neural responses of musical expectations in sleep, using neuroimaging techniques such as EEG and MEG.

Understanding the dynamical mechanisms underlying brain state transitions is crucial for basic and clinical neuroscience (e.g., sleep-wake cycle and the recovery of comatose patients). So far, little insights has been gained by utilising continuous sound as a perturbation tool to understand the brain state transition with data-driven system identification method. In terms of neuromodulation, current closed-loops auditory stimulation paradigms only focus on a few neurophysiological targets, such as slow-wave and spindle activity. By adopting the view of system neuroscience, a model-based auditory stimulation informed by the change of  system parameters will provide insight into how we could use sound to drive the system into different states. 

Memory is a fundamental aspect of human cognition, acting as the cornerstone for learning, decision-making, and personal identity. It is during sleep, particularly through the rhythms of slow oscillations, that our brains meticulously consolidate and enhance the memories acquired throughout the day. This project aims to investigate this process in a naturalistic setting, introducing a closed-loop auditory stimulation (CLAS) system for home use. By modulating continuous sound based on real-time EEG and contrasting this with traditional click-based methods, the study seeks to identify the auditory parameters that most effectively influence brain activity during sleep. The goal is to advance our understanding of the intricate dynamics of sound and sleep slow waves, in order to ultimately leverage sound to boost memory consolidation during sleep.

Sleep affects every part of our waking life and good quality sleep is essential for human health. However, sleep disorders including insomnia, circadian rhythm disturbances, and sleep apnea, are on the rise. Music and sounds help with sleep disorders. Evidence supports the use of music for good sleep but research on other types of sounds and therapeutic tools is limited. The research proposes the development and implementation of Sonic Interaction Designs tools to bridge this gap.

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During waking, music induces emotions. However, it is still unknown to what extent music listening during sleep activates emotional processing and induces emotional reactions. In the project, we will compare emotional reactions and brain oscillations induced by music during wakefulness and different sleep stages (Non rapid-eye movement (NREM) sleep stages N1 – N3 and REM sleep). In addition, we will focus on comparing reactions elicited by either preferred or non-preferred musical stimuli. We will first study the effects on sleep in healthy and then translate our findings to participants with sleeping difficulties.