Spatial qualities are often not considered at the forefront of the perceived experience of many musical encounters, especially in cognitive description or communication of musical material. Within a music listening context however, there is still the perception and creation of a sense of an environment or space which the listener enters. This element of listening often contributes to our experience of using music for falling asleep.  An opportunity therefore presents itself to begin exploring and describing the complex relations between musical content and their resulting spatial experiences in a sleep context. This will be investigated in connection to the experience of security. From this, the aim is to identify different factors affecting spatial experience with regard to the music listened to whilst falling asleep.

This project aims to develop a sleep data infrastructure designed to organize and analyze both neurophysiological sleep recordings—particularly from wearable devices—and non-physiological data. A central component of this infrastructure is the creation of a reliable, real-time, multi-modal sleep scoring model tailored to wearable data. Additionally, the project will establish a generic tool for managing data collection and analysis in real-world, non-laboratory settings. The goal is to enable large-scale data collection while reducing the researcher’s workload per participant through automation and efficient workflows, improving sleep research outside the lab.

This project is based on the premise of individual preferences towards musical stimuli during sleep, which have been previously indicated both by the individualised nature of general musical preference and by the variety present within available sleep music. These preferences are thought to affect the relationship between different musical features and resultant sleep quality. We approach such relationships using data-driven techniques, taking influence from recent advancements in machine learning to develop personalised models for recommendation and generation of sleep music. This aims for widespread applicability, creating new ways of representing sleep music preference which go beyond models developed for specific clinical contexts (e.g., treating insomnia) and are compatible with lightweight sleep data gathered in a home environment (e.g., low-channel EEG, smartwatch physiological measurements). Popular conceptions regarding sleep music will be tested systematically, based on 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 other ongoing research in the fields of music and sleep science.

Music has been used as a sleep aid since ancient times, with lullabies sharing characteristics around the world and across cultures. In the modern age, insomnia represents a major health problem, and with 24-hour access to music, many people report using music (among other strategies) to fall asleep. Despite this, little attention has been paid both to the characteristics of music used to aid sleep and to the specific individual characteristics of people listening to music for sleep. In this project, we investigate and model the influence of demographic variables, music preferences and habits, motivations, and psychological factors on the choice of music for sleep. Through this, we aim to create personalised recommendations of sleep music based on individual factors. 

Moreover, research into the effects of music on the brain based on objective measures has yielded mixed results. In our project, we also investigate the impact of music on the brain during sleep initiation and consolidation, using EEG and fMRI as well as behavioural measures.

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. For this, we use monophonic melodies, speech and song phrases to understand both predictive and perceptual learning that may be partially preserved during sleep. Specifically, we aim to examine the temporal dynamics of information processing, to characterise when and how these processes diminish 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 to sounds during sleep. Our studies will utilise current approaches in machine learning to predict neural responses to musical expectations in sleep, using neuroimaging techniques such as EEG.

This project uses stochastic dynamical system modeling to investigate how the brain transitions among different states, particularly the shift from wakefulness to various stages of sleep. By analyzing bistable dynamics in the wake-to-sleep transition, we aim to uncover the underlying mechanisms that govern these changes. Building on our initial focus on closed-loop auditory stimulation, we now integrate multiple interventions—such as light exposure and sound stimulation—into a broader methodological framework. This approach allows us to identify the key parameters that drive state transitions and refine neuromodulation strategies accordingly. Ultimately, our goal is to develop data-driven insights that can inform more targeted interventions for sleep regulation and related disorders. This research promises to benefit clinical applications, deepen our understanding of consciousness, and pave the way for innovative treatments that harness precise control over brain-state.

Slow-wave sleep plays a critical role in memory consolidation, emotional regulation, and brain restoration. However, despite its well-documented importance, current techniques for enhancing this sleep stage, such as closed-loop auditory stimulation (CLAS), have produced inconsistent results. To date, CLAS research has almost exclusively focused on brief, isolated sound pulses, neglecting the potential of more complex auditory inputs.

This project bridges the gap between sleep neuroscience and sound technology by advancing CLAS through a continuous sound paradigm modulated in real-time by EEG. By integrating sound design principles into sleep research, it aims to deepen our understanding of the interplay between sound and sleep slow oscillations. Ultimately, this works seeks to pave the way for practical and adaptable auditory stimulation techniques in both research and real-world sleep therapy applications.

Sleep affects every part of our 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 focus on investigating sounds that are not music for sleep and proposes creative development and implementation of Sonic Interaction Designs tools to bridge this gap.

While music has long been recognized as a non-pharmacological aid for sleep, research has primarily focused on its general effects, overlooking the nuanced impact of different types of sleep music across various sleep phases. This study aims to bridge this gap by investigating how generative music, dynamically shaped in real time, influences sleep physiology and subjective sleep quality in a naturalistic home setting. Using wearable EEG, the project will examine how different sleep music affects various phases of sleep (eg., winding down, sleep onset, and sleep-to-wake transition), areas often neglected in past studies. Additionally, it will explore how subjective preferences, expectations, and engagement levels determine how individual differences shape the effectiveness of sleep music. Overall, the goal is to gain a deeper understanding of how different types of sleep music—particularly generative soundscapes designed for sleep—affect sleep quality, providing insights into the role of functional music as a personalized, adaptive tool for sleep enhancement.

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.