About the Project
Introduction and Motivation Non-invasive neuroimaging is a cornerstone of modern neuroscience and clinical diagnostics. Techniques such as MRI, EEG, and MEG have advanced our understanding of the brain, but they come with significant limitations—high costs, immobility, limited spatial or temporal resolution, and the need for specialized environments. In contrast, radar-based sensing technologies offer a promising alternative, with the potential for portable, low-cost, and real-time monitoring of…
physiological signals. This project explores the feasibility and potential of near-range microwave radar as a novel, non-invasive modality for monitoring brain activity and cerebral conditions.
- Designing and optimizing a near-field radar system suitable for high-resolution neuroimaging applications.
- Characterizing the electromagnetic interaction between microwave signals and cranial tissues to understand signal propagation, reflection, and attenuation.
- Developing signal processing and machine learning algorithms to extract meaningful neural information from radar reflections.
- Validating the radar system’s performance through phantom studies, simulations, and preliminary in-vivo experiments.
- A proof-of-concept radar system capable of detecting cerebral activity or physiological changes non-invasively.
- A validated electromagnetic model of radar-head interactions.
- A signal processing pipeline for interpreting radar data in neuroimaging contexts.
- A foundation for future clinical or wearable radar-based neuro-monitoring devices.
Funding Notes
there is no funding for this project
References
Islam, M.S., Islam, M.T. and Almutairi, A.F., 2022. A portable non-invasive microwave based head imaging system using compact metamaterial loaded 3D unidirectional antenna for stroke detection. Scientific Reports, 12(1), p.8895.
Alqadami, A.S., Bialkowski, K.S., Mobashsher, A.T. and Abbosh, A.M., 2018. Wearable electromagnetic head imaging system using flexible wideband antenna array based on polymer technology for brain stroke diagnosis. IEEE transactions on biomedical circuits and systems, 13(1), pp.124-134
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