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Implantable Systems for Biomedical Applications
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Implantable Systems for Biomedical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 7893

Special Issue Editors

Prof. Dr. John Taylor

E-Mail Website
Guest Editor
Department of Electronic & Electrical Engineering, Centre for Biosensors, Bioelectronics and Biodevices (C3Bio), University of Bath, Bath, UK
Interests: analogue and mixed analogue and digital system design, low-power implantable systems for biomedical applications and interfacing between tissue and electronics
Dr. Benjamin Metcalfe

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
Interests: advanced neural interfaces; biologically inspired autonomous systems; biomedical signal processing for both in-vivo and ex-vivo applications; neuronal modeling and computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It has long been suggested that neural interfaces implanted within the peripheral nervous system have great potential to treat disease and disability and thus provide important components in new generations of bioelectronic medicines. This belief is driven at least partly by the fact that neurostimulation has seen a long history of success in a variety of chronic clinical applications, especially given the availability of modern integrated circuit technology. However, in many current and emerging applications the ability to make robust and chronic neural recordings, and thus form a closed-loop interface, is considered to be an equally important.

This Special Issue aims to highlight advances in the development, testing, and modelling of closed-loop peripheral nerve interfaces aimed at the treatment of medical conditions of current interest. Topics include, but are not limited, to:

  • Current developments in recording methods (spatial, temporal and combinations of both);
  • Combining recording and stimulation in a single interface;
  • Current progress in implantable low noise amplifier design;
  • Current approaches to implantable electrode design;
  • Future challenges and opportunities for closed-loop interfaces

Prof. Dr. John Taylor
Dr. Benjamin Metcalfe
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

16 pages, 2544 KiB  
Article
Low-Power Wireless Data Transfer System for Stimulation in an Intracortical Visual Prosthesis
by Adedayo Omisakin, Rob M. C. Mestrom and Mark J. Bentum
Sensors 2021, 21(3), 735; https://doi.org/10.3390/s21030735 - 22 Jan 2021
Cited by 5 | Viewed by 2240
Abstract
There is a growing interest to improve the quality of life of blind people. An implanted intracortical prosthesis could be the last resort in many cases of visual impairment. Technology at this moment is at a stage that implementation is at sight. Making [...] Read more.
There is a growing interest to improve the quality of life of blind people. An implanted intracortical prosthesis could be the last resort in many cases of visual impairment. Technology at this moment is at a stage that implementation is at sight. Making the data communication to and from the implanted electrodes wireless is beneficial to avoid infection and to ease mobility. Here, we focus on the stimulation side, or downlink, for which we propose a low-power non-coherent digital demodulator on the implanted receiver. The experimentally demonstrated downlink is on a scaled-down version at a 1 MHz carrier frequency showing a data rate of 125 kbps. This provides proof of principle for the system with a 12 MHz carrier frequency and a data rate of 4 Mbps, which consumes under 1 mW at the receiver side in integrated circuit (IC) simulation. Due to its digital architecture, the system is easily adjustable to an ISM frequency band with its power consumption scaling linearly with the carrier frequency. The tested system uses off-the-shelf coils, which gave sufficient bandwidth, while staying within safe SAR limits. The digital receiver achieved a reduction in power consumption by skipping clock cycles of redundant bits. The system shows a promising pathway to a low-power wireless-enabled visual prosthesis. Full article
(This article belongs to the Special Issue Implantable Systems for Biomedical Applications)
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19 pages, 6612 KiB  
Article
Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study
by Stephen Sammut, Ryan G. L. Koh and José Zariffa
Sensors 2021, 21(2), 506; https://doi.org/10.3390/s21020506 - 12 Jan 2021
Cited by 5 | Viewed by 2261
Abstract
Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal [...] Read more.
Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration. Full article
(This article belongs to the Special Issue Implantable Systems for Biomedical Applications)
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15 pages, 2522 KiB  
Article
Minimal Tissue Reaction after Chronic Subdural Electrode Implantation for Fully Implantable Brain–Machine Interfaces
by Tianfang Yan, Seiji Kameda, Katsuyoshi Suzuki, Taro Kaiju, Masato Inoue, Takafumi Suzuki and Masayuki Hirata
Sensors 2021, 21(1), 178; https://doi.org/10.3390/s21010178 - 29 Dec 2020
Cited by 9 | Viewed by 2999
Abstract
There is a growing interest in the use of electrocorticographic (ECoG) signals in brain–machine interfaces (BMIs). However, there is still a lack of studies involving the long-term evaluation of the tissue response related to electrode implantation. Here, we investigated biocompatibility, including chronic tissue [...] Read more.
There is a growing interest in the use of electrocorticographic (ECoG) signals in brain–machine interfaces (BMIs). However, there is still a lack of studies involving the long-term evaluation of the tissue response related to electrode implantation. Here, we investigated biocompatibility, including chronic tissue response to subdural electrodes and a fully implantable wireless BMI device. We implanted a half-sized fully implantable device with subdural electrodes in six beagles for 6 months. Histological analysis of the surrounding tissues, including the dural membrane and cortices, was performed to evaluate the effects of chronic implantation. Our results showed no adverse events, including infectious signs, throughout the 6-month implantation period. Thick connective tissue proliferation was found in the surrounding tissues in the epidural space and subcutaneous space. Quantitative measures of subdural reactive tissues showed minimal encapsulation between the electrodes and the underlying cortex. Immunohistochemical evaluation showed no significant difference in the cell densities of neurons, astrocytes, and microglia between the implanted sites and contralateral sites. In conclusion, we established a beagle model to evaluate cortical implantable devices. We confirmed that a fully implantable wireless device and subdural electrodes could be stably maintained with sufficient biocompatibility in vivo. Full article
(This article belongs to the Special Issue Implantable Systems for Biomedical Applications)
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