{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/390868"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/390868","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"High-Density Electrode Arrays for Cutaneous Electrophysiology and Body Surface Potential Mapping Design, fabrication and biomedical applications","abstract":"This thesis investigates the design, fabrication and application of high-density electrode arrays for cutaneous electrophysiology and body surface potential mapping, aiming to improve the precision, wearability and interpretability of next-generation non-invasive monitoring systems. Bridging bioelectronics, materials science and machine learning, the work delivers a platform for advanced spatio-temporal electrophysiological sensing. The first section focuses on the optimisation of electrode array designs. Through modelling and experimental validation, key design parameters are optimised to maximise signal quality and spatio-temporal resolution. Novel conductive polymer composites and 3D profiling methods further improve impedance characteristics and skin-electrode interaction, offering a reproducible strategy for high-performance body surface potential mapping arrays. The second section introduces an innovative textile-based fabrication platform. By adapting blade-coating and lamination techniques for stretchable fabrics, the thesis demonstrates the creation of conformable, scalable electrode arrays with stable electrical performance. Multi-layer architectures with embedded routing components preserve flexibility and signal fidelity, enabling high-density wearable systems suitable for extended biomedical use. In the final section, the developed systems are applied to a range of biomedical tasks, including gesture recognition, posture-sensitive cardiac monitoring, neuroprosthetic decoding and sensorimotor integration, using interpretable machine learning to extract clinically meaningful insights from spatio-temporal signals. A multimodal framework is also presented to predict muscle activity from cortical data, illustrating the system’s potential in neurotechnology as well as a full-arm body surface potential mapping recording to demonstrate scalability. Together, these contributions establish a complete and scalable platform for high-density non-invasive electrophysiology, combining optimal design, robust fabrication and explainable analysis. Future directions include long-term validation, closed-loop therapeutic integration and the exploration of advanced bio-interfacing materials.","abstract_html":"This thesis investigates the design, fabrication and application of high-density electrode arrays for cutaneous electrophysiology and body surface potential mapping, aiming to improve the precision, wearability and interpretability of next-generation non-invasive monitoring systems. Bridging bioelectronics, materials science and machine learning, the work delivers a platform for advanced spatio-temporal electrophysiological sensing. The first section focuses on the optimisation of electrode array designs. Through modelling and experimental validation, key design parameters are optimised to maximise signal quality and spatio-temporal resolution. Novel conductive polymer composites and 3D profiling methods further improve impedance characteristics and skin-electrode interaction, offering a reproducible strategy for high-performance body surface potential mapping arrays. The second section introduces an innovative textile-based fabrication platform. By adapting blade-coating and lamination techniques for stretchable fabrics, the thesis demonstrates the creation of conformable, scalable electrode arrays with stable electrical performance. Multi-layer architectures with embedded routing components preserve flexibility and signal fidelity, enabling high-density wearable systems suitable for extended biomedical use. In the final section, the developed systems are applied to a range of biomedical tasks, including gesture recognition, posture-sensitive cardiac monitoring, neuroprosthetic decoding and sensorimotor integration, using interpretable machine learning to extract clinically meaningful insights from spatio-temporal signals. A multimodal framework is also presented to predict muscle activity from cortical data, illustrating the system’s potential in neurotechnology as well as a full-arm body surface potential mapping recording to demonstrate scalability. Together, these contributions establish a complete and scalable platform for high-density non-invasive electrophysiology, combining optimal design, robust fabrication and explainable analysis. 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