First Demonstration of Velocity Selective Recording from the Pig Vagus using a Nerve Cuff shows Respiration Afferents

Benjamin Metcalfe, Thomas Nielsen, Nick Donaldson, Alan J. Hunter, John Taylor

Research output: Contribution to journalArticlepeer-review

28 Citations (SciVal)
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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 languageEnglish
Pages (from-to)127-136
Number of pages10
JournalBiomedical Engineering Letters
Issue number1
Early online date22 Nov 2017
Publication statusPublished - 1 Feb 2018


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