I. Introduction
The major healthcare challenges facing developed countries result from diseases of middle and old age. As such, modern medical science relies upon modelling human disease processes in genetically modified animals in order to understand disease pathology and develop novel therapeutics. Mice are by far the most commonly used species accounting for 74% of experimental procedures conducted on genetically modified organisms. Within the field of neuro-degenerative research, monitoring neural activity via electroencephalography (EEG) potentials in rodents provides a valuable insight into loss of brain function in disease-states and mechanisms of recovery with drug treatment. Wireless, miniature, head-mounted EEG devices are considered a substantial innovation in animal welfare over previous technologies. However, there is also both an ethical and legal imperative to mitigate the welfare impact of EEG recording on test subjects. Considering the small body mass of mice (20–30g), improved welfare can be best accomplished through reduction of device weight. Meeting this demand not only preserves the ability to undertake such research, but as will be highlighted in this paper, it can enable new methodologies and support new discoveries in neuroscience through innovation at the level of sensors and ultra-miniature systems. In this paper, the authors describe the design, implementation and capabilities of an ultra-miniature sensor, the NAT, and its recent validation through use in innovative neuroscience applications, including new neuroscience methodologies enabled by the novel capabilities of the device. In particular, it is observed that combining infrared (IR) event-stamping (a notable feature of the NAT-1 device), with video motion tracking, enables rapid, automated and sensitive EEG phenotyping of Alzheimers Disease (AD) mouse models [1]. This approach has also proven valuable in achieving the precise synchrony between behavioral and electrophysiological data records required to make use of novel time resolved quantitative EEG analyses.