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• Conference paper
  Janeiko V, Rezvani R, Pourshahrokhi N, Enshaeifar S, Krogbaek M, Christophersen SH, Elsaleh T, Barnaghi Pet al., 2020,

  Enabling Context-Aware Search using Extracted Insights from IoT Data Streams

  , 4th IEEE Global Internet of Things Summit (GIoTS), Publisher: IEEE
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• Conference paper
  Cavuto M, Hallam R, Rapeaux A, Maslik M, Troiani F, Constandinou Tet al., 2019,

  Live demonstration: a public engagement platform for invasive neural interfaces

  , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 1-1

  Neural interfaces, and more specifically ones ofthe invasive/implantable variety, today are a topic of muchcontroversy, often making the general public uncomfortable andintimidated. We have thus devised a bespoke interactive demoto help people understand brain implants and their need inthe age of wearable devices, with the secondary objective ofintroducing the wireless cortical neural probe that we, at NGNI(Next Generation Neural Interfaces) lab, are developing.

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• Journal article
  Graham NSN, Sharp DJ, 2019,

  Understanding neurodegeneration after traumatic brain injury: from mechanisms to clinical trials in dementia

  , Journal of Neurology, Neurosurgery & Psychiatry, Vol: 90, ISSN: 0022-3050

  Traumatic brain injury (TBI) leads to increased rates of dementia, including Alzheimer’s disease. The mechanisms by which trauma can trigger neurodegeneration are increasingly understood. For example, diffuse axonal injury is implicated in disrupting microtubule function, providing the potential context for pathologies of tau and amyloid to develop. The neuropathology of post-traumatic dementias is increasingly well characterised, with recent work focusing on chronic traumatic encephalopathy (CTE). However, clinical diagnosis of post-traumatic dementia is problematic. It is often difficult to disentangle the direct effects of TBI from those produced by progressive neurodegeneration or other post-traumatic sequelae such as psychiatric impairment. CTE can only be confidently identified at postmortem and patients are often confused and anxious about the most likely cause of their post-traumatic problems. A new approach to the assessment of the long-term effects of TBI is needed. Accurate methods are available for the investigation of other neurodegenerative conditions. These should be systematically employed in TBI. MRI and positron emission tomography neuroimaging provide biomarkers of neurodegeneration which may be of particular use in the postinjury setting. Brain atrophy is a key measure of disease progression and can be used to accurately quantify neuronal loss. Fluid biomarkers such as neurofilament light can complement neuroimaging, representing sensitive potential methods to track neurodegenerative processes that develop after TBI. These biomarkers could characterise endophenotypes associated with distinct types of post-traumatic neurodegeneration. In addition, they might profitably be used in clinical trials of neuroprotective and disease-modifying treatments, improving trial design by providing precise and sensitive measures of neuronal loss.

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• Journal article
  Freemont P, 2019,

  Synthetic biology industry - Data-driven design is creating new opportunities in biotechnology.

  , Emerging Topics in Life Sciences, Vol: 3, Pages: 651-657, ISSN: 2397-8554

  Synthetic biology is a rapidly emerging interdisciplinary research field that is primarily built upon foundational advances in molecular biology combined with engineering design. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies. This has spurned a rapid growth in start-up companies and the new synthetic biology industry is growing rapidly, with start-up companies receiving ~$6.1B investment since 2015 and a global synthetic biology market value estimated to be $14B by 2026. Many of the new start-upscan be grouped within a multi-layer ‘technology stack’. The ‘stack’ comprises a number of technology layers which together can be applied to a diversity of new biotechnology applications like consumer biotechnology products and living therapies. The ‘stack’ also enables new commercial opportunities and value chains similar to the software design and manufacturing revolution of the 20th century. However, synthetic biology industry is at a crucial point, as it now requires recognisable commercial successes in order for the industry to expand and scale, in terms of investment and companies. However, such expansion may directly challenge the ethos of synthetic biology, in terms of open technology sharing and democratisation, which could by accident lead to multi-national corporations and technology monopolies similar to the existing biotechnology/biopharma industry.

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• Conference paper
  Han Y, Lauteslager T, Lande TS, Constandinou TGet al., 2019,

  UWB radar for non-contact heart rate variability monitoring and mental state classification.

  , Annual Meeting of the IEEE Engineering in Medicine and Biology Society, Pages: 6578-6582, ISSN: 1557-170X

  Heart rate variability (HRV), as measured by ultra-wideband (UWB) radar, enables contactless monitoring of physiological functioning in the human body. In the current study, we verified the reliability of HRV extraction from radar data, under limited transmitter power. In addition, we conducted a feasibility study of mental state classification from HRV data, measured using radar. Specifically, arctangent demodulation with calibration and low rank approximation have been used for radar signal pre-processing. An adaptive continuous wavelet filter and moving average filter were utilized for HRV extraction. For the mental state classification task, performance of support vector machine, k-nearest neighbors and random forest classifiers have been compared. The developed system has been validated on human participants, with 10 participants for HRV extraction, and three participants for the proof-of-concept mental state classification study. The results of HRV extraction demonstrate the reliability of time-domain parameter extraction from radar data. However, frequency-domain HRV parameters proved to be unreliable under low SNR. The best average overall mental state classification accuracy achieved was 82.34%, which has important implications for the feasibility of mental health monitoring using UWB radar.

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