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Grani F, Soto-Sánchez C, Fimia A, Fernández E. Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges. Front Cell Neurosci 2022; 16:1034270. [PMID: 36582211 PMCID: PMC9792612 DOI: 10.3389/fncel.2022.1034270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
Current cortical visual prosthesis approaches are primarily unidirectional and do not consider the feed-back circuits that exist in just about every part of the nervous system. Herein, we provide a brief overview of some recent developments for better controlling brain stimulation and present preliminary human data indicating that closed-loop strategies could considerably enhance the effectiveness, safety, and long-term stability of visual cortex stimulation. We propose that the development of improved closed-loop strategies may help to enhance our capacity to communicate with the brain.
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Affiliation(s)
- Fabrizio Grani
- Institute of Bioengineering, Universidad Miguel Hernández de Elche, Elche, Spain
| | - Cristina Soto-Sánchez
- Institute of Bioengineering, Universidad Miguel Hernández de Elche, Elche, Spain,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Antonio Fimia
- Departamento de Ciencia de Materiales, Óptica y Tecnología Electrónica, Universidad Miguel Hernández de Elche, Elche, Spain
| | - Eduardo Fernández
- Institute of Bioengineering, Universidad Miguel Hernández de Elche, Elche, Spain,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain,*Correspondence: Eduardo Fernández,
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Luu DK, Nguyen AT, Jiang M, Drealan MW, Xu J, Wu T, Tam WK, Zhao W, Lim BZH, Overstreet CK, Zhao Q, Cheng J, Keefer EW, Yang Z. Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface. IEEE Trans Biomed Eng 2022; 69:3051-3063. [PMID: 35302937 DOI: 10.1109/tbme.2022.3160618] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. METHODS Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputees movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor decoding experiments with three human amputees. RESULTS First, we show the AI agent enables amputees to intuitively control a prosthetic hand with individual finger and wrist movements up to 97-98% accuracy. Second, we demonstrate the AI agent's real-time performance by measuring the reaction time and information throughput in a hand gesture matching task. Third, we investigate the AI agent's long-term uses and show the decoder's robust predictive performance over a 16-month implant duration. Conclusion & significance: Our study demonstrates the potential of AI-enabled nerve technology, underling the next generation of dexterous and intuitive prosthetic hands.
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Nguyen AT, Drealan MW, Khue Luu D, Jiang M, Xu J, Cheng J, Zhao Q, Keefer EW, Yang Z. A portable, self-contained neuroprosthetic hand with deep learning-based finger control. J Neural Eng 2021; 18. [PMID: 34571503 DOI: 10.1088/1741-2552/ac2a8d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/27/2021] [Indexed: 01/07/2023]
Abstract
Objective.Deep learning-based neural decoders have emerged as the prominent approach to enable dexterous and intuitive control of neuroprosthetic hands. Yet few studies have materialized the use of deep learning in clinical settings due to its high computational requirements.Approach.Recent advancements of edge computing devices bring the potential to alleviate this problem. Here we present the implementation of a neuroprosthetic hand with embedded deep learning-based control. The neural decoder is designed based on the recurrent neural network architecture and deployed on the NVIDIA Jetson Nano-a compacted yet powerful edge computing platform for deep learning inference. This enables the implementation of the neuroprosthetic hand as a portable and self-contained unit with real-time control of individual finger movements.Main results.A pilot study with a transradial amputee is conducted to evaluate the proposed system using peripheral nerve signals acquired from implanted intrafascicular microelectrodes. The preliminary experiment results show the system's capabilities of providing robust, high-accuracy (95%-99%) and low-latency (50-120 ms) control of individual finger movements in various laboratory and real-world environments.Conclusion.This work is a technological demonstration of modern edge computing platforms to enable the effective use of deep learning-based neural decoders for neuroprosthesis control as an autonomous system.Significance.The proposed system helps pioneer the deployment of deep neural networks in clinical applications underlying a new class of wearable biomedical devices with embedded artificial intelligence.Clinical trial registration: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.
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Affiliation(s)
- Anh Tuan Nguyen
- Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America.,Fasikl Incorporated, Minneapolis, MN, United States of America
| | - Markus W Drealan
- Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Diu Khue Luu
- Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Ming Jiang
- Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Jian Xu
- Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Jonathan Cheng
- Plastic Surgery, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Qi Zhao
- Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Edward W Keefer
- Nerves Incorporated, Dallas, TX, United States of America.,Fasikl Incorporated, Minneapolis, MN, United States of America
| | - Zhi Yang
- Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America.,Fasikl Incorporated, Minneapolis, MN, United States of America
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Telyshev D, Nesterenko I, Bochkov A, Malinina A, Markov A, Bordovsky S, Polunin G, Ananichuk A, Reshetov I. Functional Evaluation of Larynx Nerve Stimulator With EMG Acquisition Capability and Wireless Connectivity. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:629-641. [PMID: 34232890 DOI: 10.1109/tbcas.2021.3094890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recently, an electrical stimulation of the paralyzed muscle, as a potential therapy for restoring function of a denervated muscle system, has been debated as an innovative treatment in the management of patients with laryngeal paralysis. Numerous studies in acute and chronic animal models have demonstrated that electrical stimulation of the paralyzed posterior cricoarytenoideus muscle (PCA) offers an approach to induce vocal fold abduction and restore ventilation through the glottis. The study aims to test applicability of the controlled opening of the rima glottides via direct electrical stimulation of the posterior cricoarytenoideus muscle. We developed for this purpose a novel instrument system for the controlled larynx nerve stimulation. An acute experiment on the 4 years old pig showed effectiveness of the engineered stimulator. The controlled opening of rima glottidis of both posterior cricoarytenoid muscles and afterwards of both PCA muscle contraction were observed as a result of the electrical stimulation with the applied current in the range of 0.1-3 mA and pulse width of 1 ms and 10 ms. Performed research indicates a large potential of the novel nerve stimulator for the human larynx stimulation.
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Shon A, Brakel K, Hook M, Park H. Fully Implantable Plantar Cutaneous Augmentation System for Rats Using Closed-loop Electrical Nerve Stimulation. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:326-338. [PMID: 33861705 DOI: 10.1109/tbcas.2021.3072894] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Plantar cutaneous feedback plays an important role in stable and efficient gait, by modulating the activity of ankle dorsi- and plantar-flexor muscles. However, central and peripheral nervous system trauma often decrease plantar cutaneous feedback and/or interneuronal excitability in processing the plantar cutaneous feedback. In this study, we tested a fully implantable neural recording and stimulation system augmenting plantar cutaneous feedback. Electromyograms were recorded from the medial gastrocnemius muscle for stance phase detection, while biphasic stimulation pulses were applied to the distal-tibial nerve during the stance phase to augment plantar cutaneous feedback. A Bluetooth low energy and a Qi-standard inductive link were adopted for wireless communication and wireless charging, respectively. To test the operation of the system, one intact rat walked on a treadmill with the electrical system implanted into its back. Leg kinematics were recorded to identify the stance phase. Stimulation was applied, with a 250-ms onset delay from stance onset and 200-ms duration, resulting in the onset at 47.58 ± 2.82% of stance phase and the offset at 83.49 ± 4.26% of stance phase (Mean ± SEM). The conduction velocity of the compound action potential (31.2 m/s and 41.6 m/s at 1·T and 2·T, respectively) suggests that the evoked action potential was characteristic of an afferent volley for cutaneous feedback. We also demonstrated successful wireless charging and system reset functions. The experimental results suggest that the presented implantable system can be a valuable neural interface tool to investigate the effect of plantar cutaneous augmentation on gait in a rat model.
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Nguyen AT, Xu J, Jiang M, Luu DK, Wu T, Tam WK, Zhao W, Drealan MW, Overstreet CK, Zhao Q, Cheng J, Keefer E, Yang Z. A bioelectric neural interface towards intuitive prosthetic control for amputees. J Neural Eng 2020; 17. [PMID: 33091891 DOI: 10.1088/1741-2552/abc3d3] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 10/22/2020] [Indexed: 01/17/2023]
Abstract
OBJECTIVE While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful. APPROACH Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject's motor intention. MAIN RESULTS A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient's true intention. SIGNIFICANCE Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways. (Clinical trial identifier: NCT02994160).
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Affiliation(s)
- Anh Tuan Nguyen
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | - Jian Xu
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | - Ming Jiang
- Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | - Diu Khue Luu
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | - Tong Wu
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | - Wing-Kin Tam
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | - Wenfeng Zhao
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | - Markus W Drealan
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | | | - Qi Zhao
- Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
| | | | | | - Zhi Yang
- Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, UNITED STATES
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Rapeaux A, Constandinou TG. An HFAC block-capable and module-extendable 4-channel stimulator for acute neurophysiology. J Neural Eng 2020; 17:046013. [PMID: 32428874 DOI: 10.1088/1741-2552/ab947a] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE This paper describes the design, testing and use of a novel multichannel block-capable stimulator for acute neurophysiology experiments to study highly selective neural interfacing techniques. This paper demonstrates the stimulator's ability to excite and inhibit nerve activity in the rat sciatic nerve model concurrently using monophasic and biphasic nerve stimulation as well as high-frequency alternating current (HFAC). APPROACH The proposed stimulator uses a Howland Current Pump circuit as the main analogue stimulator element. 4 current output channels with a common return path were implemented on printed circuit board using Commercial Off-The-Shelf components. Programmable operation is carried out by an ARM Cortex-M4 Microcontroller on the Freescale freedom development platform (K64F). MAIN RESULTS This stimulator design achieves ± 10 mA of output current with ± 15 V of compliance and less than 6 µA of resolution using a quad-channel 12-bit external DAC, for four independently driven channels. This allows the stimulator to carry out both excitatory and inhibitory (HFAC block) stimulation. DC Output impedance is above 1 M Ω. Overall cost for materials i.e. PCB boards and electronic components is less than USD 450 or GBP 350 and device size is approximately 9 cm × 6 cm × 5 cm. SIGNIFICANCE Experimental neurophysiology often requires significant investment in bulky equipment for specific stimulation requirements, especially when using HFAC block. Different stimulators have limited means of communicating with each other, making protocols more complicated. This device provides an effective solution for multi-channel stimulation and block of nerves, enabling studies on selective neural interfacing in acute scenarios with an affordable, portable and space-saving design for the laboratory. The stimulator can be further upgraded with additional modules to extend functionality while maintaining straightforward programming and integration of functions with one controller. Additionally, all source files including all code and PCB design files are freely available to the community to use and further develop.
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Affiliation(s)
- Adrien Rapeaux
- Centre for Bio-Inspired Technology, Imperial College London , London, SW7 2AZ, United Kingdom. Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, United Kingdom. Care Research & Technology Centre, UK Dementia Research Institute at Imperial College London, London, United Kingdom. Author to whom any correspondence should be addressed
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Xu J, Nguyen AT, Wu T, Zhao W, Luu DK, Yang Z. A Wide Dynamic Range Neural Data Acquisition System With High-Precision Delta-Sigma ADC and On-Chip EC-PC Spike Processor. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:425-440. [PMID: 32031949 PMCID: PMC7310583 DOI: 10.1109/tbcas.2020.2972013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A high-performance, wide dynamic range, fully-integrated neural interface is one key component for many advanced bidirectional neuromodulation technologies. In this paper, to complement the previously proposed frequency-shaping amplifier (FSA) and high-precision electrical microstimulator, we will present a proof-of-concept design of a neural data acquisition (DAQ) system that includes a 15-bit, low-power Delta-Sigma analog-to-digital converter (ADC) and a real-time spike processor based on one exponential component-polynomial component (EC-PC) algorithm. High-precision data conversion with low power consumption and small chip area is achieved by employing several techniques, such as opamp-sharing, multi-bit successive approximation (SAR) quantizer, two-step summation, and ultra-low distortion data weighted averaging (DWA). The on-chip EC-PC engine enables low latency, automatic detection, and extraction of spiking activities, thus supporting closed-loop control, real-time data compression and /or neural information decoding. The prototype chip was fabricated in a 0.13 μm CMOS process and verified in both bench-top and In-Vivo experiments. Bench-top measurement results indicate the designed ADC achieves a peak signal-to-noise and distortion ratio (SNDR) of 91.8 dB and a dynamic range of 93.0 dB over a 10 kHz bandwidth, where the total power consumption of the modulator is only 20 μW at 1.0 V supply, corresponding to a figure-of-merit (FOM) of 31.4fJ /conversion-step. In In-Vivo experiments, the proposed DAQ system has been demonstrated to obtain high-quality neural activities from a rat's motor cortex and also greatly reduce recovery time from system saturation due to electrical microstimulation.
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Das R, Moradi F, Heidari H. Biointegrated and Wirelessly Powered Implantable Brain Devices: A Review. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:343-358. [PMID: 31944987 DOI: 10.1109/tbcas.2020.2966920] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
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Pancrazio JJ, Cogan SF. Editorial for the Special Issue on Neural Electrodes: Design and Applications. MICROMACHINES 2019; 10:E466. [PMID: 31336980 PMCID: PMC6680485 DOI: 10.3390/mi10070466] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 07/09/2019] [Indexed: 12/14/2022]
Abstract
Neural electrodes enable the recording and stimulation of bioelectrical activity from the nervous system [...].
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Affiliation(s)
- Joseph J Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, BSB 13.633, Richardson, TX 75080, USA.
| | - Stuart F Cogan
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, BSB 13.633, Richardson, TX 75080, USA.
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Gulino M, Kim D, Pané S, Santos SD, Pêgo AP. Tissue Response to Neural Implants: The Use of Model Systems Toward New Design Solutions of Implantable Microelectrodes. Front Neurosci 2019; 13:689. [PMID: 31333407 PMCID: PMC6624471 DOI: 10.3389/fnins.2019.00689] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 06/18/2019] [Indexed: 01/28/2023] Open
Abstract
The development of implantable neuroelectrodes is advancing rapidly as these tools are becoming increasingly ubiquitous in clinical practice, especially for the treatment of traumatic and neurodegenerative disorders. Electrodes have been exploited in a wide number of neural interface devices, such as deep brain stimulation, which is one of the most successful therapies with proven efficacy in the treatment of diseases like Parkinson or epilepsy. However, one of the main caveats related to the clinical application of electrodes is the nervous tissue response at the injury site, characterized by a cascade of inflammatory events, which culminate in chronic inflammation, and, in turn, result in the failure of the implant over extended periods of time. To overcome current limitations of the most widespread macroelectrode based systems, new design strategies and the development of innovative materials with superior biocompatibility characteristics are currently being investigated. This review describes the current state of the art of in vitro, ex vivo, and in vivo models available for the study of neural tissue response to implantable microelectrodes. We particularly highlight new models with increased complexity that closely mimic in vivo scenarios and that can serve as promising alternatives to animal studies for investigation of microelectrodes in neural tissues. Additionally, we also express our view on the impact of the progress in the field of neural tissue engineering on neural implant research.
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Affiliation(s)
- Maurizio Gulino
- i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- INEB – Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal
- FEUP – Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
| | - Donghoon Kim
- Multi-Scale Robotics Lab (MSRL), Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland
| | - Salvador Pané
- Multi-Scale Robotics Lab (MSRL), Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland
| | - Sofia Duque Santos
- i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- INEB – Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal
| | - Ana Paula Pêgo
- i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- INEB – Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal
- FEUP – Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
- ICBAS – Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal
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Lee B, Jia Y, Mirbozorgi SA, Connolly M, Tong X, Zeng Z, Mahmoudi B, Ghovanloo M. An Inductively-Powered Wireless Neural Recording and Stimulation System for Freely-Behaving Animals. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:413-424. [PMID: 30624226 PMCID: PMC6510586 DOI: 10.1109/tbcas.2019.2891303] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
An inductively-powered wireless integrated neural recording and stimulation (WINeRS-8) system-on-a-chip (SoC) that is compatible with the EnerCage-HC2 for wireless/battery-less operation has been presented for neuroscience experiments on freely behaving animals. WINeRS-8 includes a 32-ch recording analog front end, a 4-ch current-controlled stimulator, and a 434 MHz on - off keying data link to an external software- defined radio wideband receiver (Rx). The headstage also has a bluetooth low energy link for controlling the SoC. WINeRS-8/EnerCage-HC2 systems form a bidirectional wireless and battery-less neural interface within a standard homecage, which can support longitudinal experiments in an enriched environment. Both systems were verified in vivo on rat animal model, and the recorded signals were compared with hardwired and battery-powered recording results. Realtime stimulation and recording verified the system's potential for bidirectional neural interfacing within the homecage, while continuously delivering 35 mW to the hybrid WINeRS-8 headstage over an unlimited period.
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Affiliation(s)
- Byunghun Lee
- School of Electrical Engineering, Incheon National University, South Korea ()
| | - Yaoyao Jia
- GT- Bionics lab, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA ()
| | - S. Abdollah Mirbozorgi
- GT- Bionics lab, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA ()
| | - Mark Connolly
- Department of Physiology, Emory University, Atlanta, GA 30329, USA
| | - Xingyuan Tong
- School of Electronics Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China
| | | | - Babak Mahmoudi
- Department of Physiology, Emory University, Atlanta, GA 30329, USA
| | - Maysam Ghovanloo
- GT- Bionics lab, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA ()
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Abstract
OBJECTIVE A new technique is presented to enhance the precision of the analog-to-digital (AD) and digital-to-analog (DA) conversion, which are fundamental operations of many biomedical information processing systems. In practice, the precision of these operations is always bounded, first by the random mismatch error occurred during system implementation, and subsequently by the intrinsic quantization error determined by the system architecture itself. METHODS Here, we derive a new mathematical interpretation of the previously proposed redundant sensing architecture that not only suppresses mismatch error but also allows achieving an effective resolution exceeding the system's intrinsic resolution, i.e., super-resolution (SR). SR is enabled by an endogenous property of redundant structures regarded as "code diffusion" where the references' value spreads into the neighbor sample space as a result of mismatch error. RESULTS Using Monte Carlo methods, we show a profound theoretical increase of 8-9 b effective resolution or 256-512× enhancement of precision on a 10-b device at 95% sample space. CONCLUSION The proposed SR mechanism can be applied to substantially improve the precision of various AD and DA conversion processes beyond the system resource constraints. SIGNIFICANCE The concept opens the possibility for a wide range of applications in low-power fully integrated sensors and devices where the cost-accuracy tradeoff is inevitable. As a proof-of-concept demonstration, we point out an example where the proposed technique can be used to enhance the precision of an implantable neurostimulator design.
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