'Lines of flight': digital musical instruments, inclusive music bands, meaningful engagement in music-making and learning

In this study I examine the development of three inclusive music bands in Cork city. Derived from Jellison’s research on inclusive music education, inclusive music bands involve students with disabilities coming together with typically developing peers to make and learn music that is meaningful (Jellison, 2012). As part of this study, I established three inclusive music bands to address the lack of inclusive music making and learning experiences in Cork city. Each of these bands evolved and adapted in order to be socio-culturally relevant within formal and informal settings: Circles (community education band), Till 4 (secondary school band) and Mish Mash (third level and community band). I integrated Digital Musical Instruments into the three bands, in order to ensure access to music making and learning for band members with profound physical disabilities. Digital Musical Instruments are electronic music devices that facilitate active music making with minimal movement. This is the first study in Ireland to examine the experiences of inclusive music making and learning using Digital Musical Instruments. I propose that the integration of Digital Musical Instruments into inclusive music bands has the potential to further the equality and social justice agenda in music education in Ireland. In this study, I employed qualitative research methodology, incorporating participatory action research methodology and case study design. In this thesis I reveal the experiences of being involved in an inclusive music band in Cork city. I particularly focus on examining whether the use of this technology enhances meaningful music making and learning experiences for members with disabilities within inclusive environments. To both inform and understand the person centered and adaptable nature of these inclusive bands, I draw theoretical insights from Sen’s Capabilities Approach and Deleuze and Guatarri’s Rhizome Theory. Supported by descriptive narrative from research participants and an indepth examination of literature, I discover the optimum conditions and associated challenges of inclusive music practice in Cork city.

Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.