Abstract
Neural interfaces have great potential to treat disease and disability by modulating the electrical signals within the nervous system. However, whilst neural stimulation is a well-established technique, current neural interfaces are limited by poor recording ability. Low signal amplitudes necessitate the use of highly invasive techniques that divide or penetrate the nerve, and as such are unsuitable for chronic implantation. In this paper, we present the first application of the velocity selective recording technique to the detection of respiration activity in the vagus nerve, which is involved with treatments for epilepsy, depression, and rheumatoid arthritis. Further, we show this using a chronically implantable interface that does not divide the nerve. We also validate our recording setup using electrical stimulation and we present an analysis of the recorded signal amplitudes. The recording interface was formed from a cuff containing ten electrodes implanted around the intact right vagus nerve of a Danish Landrace pig. Nine differential amplifiers were connected to adjacent electrodes, and the resulting signals were processed to discriminate neural activity based on conduction velocity. Despite the average single channel signal-to-noise ratio of − 5.8 dB, it was possible to observe distinct action potentials travelling in both directions along the nerve. Further, contrary to expectation given the low signal-to-noise ratio, we have shown that it was possible to identify afferent neural activity that encoded respiration. The significance of this is the demonstration of a chronically implantable method for neural recording, a result that will transform the capabilities of future neuroprostheses.
Original language | English |
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Pages (from-to) | 127-136 |
Number of pages | 10 |
Journal | Biomedical Engineering Letters |
Volume | 8 |
Issue number | 1 |
Early online date | 22 Nov 2017 |
DOIs | |
Publication status | Published - 1 Feb 2018 |
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Alan Hunter
- Department of Mechanical Engineering - Deputy Head of Department: Workload and Wellbeing
- Water Innovation and Research Centre (WIRC)
- UKRI CDT in Accountable, Responsible and Transparent AI
Person: Research & Teaching
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Benjamin Metcalfe, FRSA
- Department of Electronic & Electrical Engineering - Head of Department
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Bioengineering & Biomedical Technologies (CBio)
- Bath Institute for the Augmented Human
- IAAPS: Propulsion and Mobility
Person: Research & Teaching, Core staff, Affiliate staff
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John Taylor
- Department of Electronic & Electrical Engineering - Professor
- Electronics Materials, Circuits & Systems Research Unit (EMaCS)
Person: Research & Teaching