Whilst music is often used as a self-medication to promote sleep, little is known about how music promotes sleep and how its effectiveness can be increased. In this project, we investigate how music can be employed to facilitate physiological and attentional changes required to make the transition from an awake state to sleep. This is done by first investigating adjustments of musical characteristics to match physiological and attentional characteristics. Subsequently, this matching process is used to set up a bio-feedback loop that provides cues and direction for the sleep induction process. These processes will first be trialled with healthy volunteers and secondly with a group of insomniacs.
Project description Music comes in a great variety of shapes and forms, and indeed it may analogously serve a variety of purposes. Depending on such features as tempo, pitch range, spectral centroid, intensity, pulse salience and melodic and harmonic complexity, music may support activation and motivation for a high intensity sports event, or music may support relaxation and sleep induction. Processes central to the activating and relaxational nature of music engagement include bodily and neurophysiological entrainment, as well as positive enjoyment of the music, and a willingness to use music for a particular purpose. Capitalising on the ability of listeners to entrain with music, and for music to subsequently influence physiology, attention, and relaxation of listeners, we propose the development of a system that sets up a musical biofeedback loop that is used to facilitate sleep induction: attentional and physiological measures are taken of participants to assess their state of arousal/sleepiness. Musical characteristics are adjusted to match the arousal state of the participant. Finally, a feedback loop is established as listeners receive feedback on their state of arousal, providing cues for stages towards sleep and positive reinforcement for relaxation, when participants learn to adjust physiology to accomplish a sonic result. This project is realised in collaboration with researchers of SleepCogni and will use a handheld device developed by the company that measures skin temperature, reaction time, heart rate and skin conductance. It builds on a pilot study that showed promising results for the use of music for sleep with gradually changing characteristics when compared to using no music or music with constant characteristics. Music specially composed for the purpose of the pilot was used that had properties characteristic of music used for sleep induction. This set of characteristics was defined through an analysis of sleep music play lists. The pilot was conducted with healthy volunteers and without any reference to normal duration of sleep induction of participants. The central thesis of SleepCogni is that lighting and music/sound can inform the participant about his or her wakeful state, and provide reinforcing feedback on the transition from awake to asleep. The proposed project will offer a central contribution to this thesis by accomplishing the following innovations: Experiment 1: Modelling and testing of relationship between musical parameters and sleepy-awake judgments and relaxation judgments of participants Experiment 2: Modelling and testing of physiology and attentional states of participants with respect to awake and asleep states (in collaboration with SleepCogni partners) Experiment 3: Modelling and testing relationships between musical parameters and induced physiological-attentional states. Experiment 4: Modelling and testing a musical biofeedback loop to facilitate sleep onset and quality (in collaboration with SleepCogni partners) This project sets up a human-computer interface based on physiological and attentional measures. It will use music synthesis tools for music production and machine learning to model relationships between music and physiological and attentional states. Fellow researchers from the SleepCogni project will lead Experiment 2, and will assist the researcher in programming and evaluating the success of a feedback loop in Experiment 4
Fully funded for UK/EU Students.
National Minimum Doctoral Stipend for 2019/20 is £15,009 Research Councils UK Indicative Fee Level for 2019/20 is £4,327
Please liaise with the Department of Music regarding eligibility.