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Abstract
Recent work has demonstrated the feasibility of minimally-invasive implantation of electrodes into a cortical blood vessel. However, the effect of the dura and blood vessel on recording signal quality is not understood and may be a critical factor impacting implementation of a closed-loop endovascular neuromodulation system. The present work compares the performance and recording signal quality of a minimally-invasive endovascular neural interface with conventional subdural and epidural interfaces. We compared bandwidth, signal-to-noise ratio, and spatial resolution of recorded cortical signals using subdural, epidural and endovascular arrays four weeks after implantation in sheep. We show that the quality of the signals (bandwidth and signal-to-noise ratio) of the endovascular neural interface is not significantly different from conventional neural sensors. However, the spatial resolution depends on the array location and the frequency of recording. We also show that there is a direct correlation between the signal-noise-ratio and classification accuracy, and that decoding accuracy is comparable between electrode arrays. These results support the consideration for use of an endovascular neural interface in a clinical trial of a novel closed-loop neuromodulation technology.
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1 Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Vascular Bionics Laboratory, Department of Medicine, Royal Melbourne Hospital, (RMH), The University of Melbourne, Parkville, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia; SmartStent Pty Ltd, Parkville, Australia
2 Vascular Bionics Laboratory, Department of Medicine, Royal Melbourne Hospital, (RMH), The University of Melbourne, Parkville, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia; SmartStent Pty Ltd, Parkville, Australia
3 Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Physiology and Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Australia
4 Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Vascular Bionics Laboratory, Department of Medicine, Royal Melbourne Hospital, (RMH), The University of Melbourne, Parkville, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia
5 Department of Veterinary Science, The University of Melbourne, Werribee, Australia
6 Vascular Bionics Laboratory, Department of Medicine, Royal Melbourne Hospital, (RMH), The University of Melbourne, Parkville, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia
7 Florey Institute of Neuroscience and Mental Health, Parkville, Australia
8 Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Centre for Neural Engineering, The University of Melbourne, Carlton, Australia