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Kim S, Song KI. Flexible Peripheral Nerve Interfacing Electrode for Joint Position Control in Closed-Loop Neuromuscular Stimulation. MICROMACHINES 2024; 15:594. [PMID: 38793167 PMCID: PMC11122956 DOI: 10.3390/mi15050594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024]
Abstract
Addressing peripheral nerve disorders with electronic medicine poses significant challenges, especially in replicating the dynamic mechanical properties of nerves and understanding their functionality. In the field of electronic medicine, it is crucial to design a system that thoroughly understands the functions of the nervous system and ensures a stable interface with nervous tissue, facilitating autonomous neural adaptation. Herein, we present a novel neural interface platform that modulates the peripheral nervous system using flexible nerve electrodes and advanced neuromodulation techniques. Specifically, we have developed a surface-based inverse recruitment model for effective joint position control via direct electrical nerve stimulation. Utilizing barycentric coordinates, this model constructs a three-dimensional framework that accurately interpolates inverse isometric recruitment values across various joint positions, thereby enhancing control stability during stimulation. Experimental results from rabbit ankle joint control trials demonstrate our model's effectiveness. In combination with a proportional-integral-derivative (PID) controller, it shows superior performance by achieving reduced settling time (less than 1.63 s), faster rising time (less than 0.39 s), and smaller steady-state error (less than 3 degrees) compared to the legacy model. Moreover, the model's compatibility with recent advances in flexible interfacing technologies and its integration into a closed-loop controlled functional neuromuscular stimulation (FNS) system highlight its potential for precise neuroprosthetic applications in joint position control. This approach marks a significant advancement in the management of neurological disorders with advanced neuroprosthetic solutions.
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Affiliation(s)
- Sia Kim
- Department of Biomedical Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
| | - Kang-Il Song
- Division of Smart Healthcare, Pukyung National University, Busan 48513, Republic of Korea
- Digital Healthcare Research Center, Institute of Information Technology and Convergence, Pukyung National University, Busan 48513, Republic of Korea
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2
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Koh RGL, Zariffa J, Jabban L, Yen SC, Donaldson N, Metcalfe BW. Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system. J Neural Eng 2022; 19. [PMID: 35772397 DOI: 10.1088/1741-2552/ac7d74] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/30/2022] [Indexed: 11/11/2022]
Abstract
The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of peripheral nerve interfaces, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.
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Affiliation(s)
- Ryan G L Koh
- IBBME, University of Toronto, Rosebrugh Bldg, 164 College St Room 407, Toronto, Ontario, M5S 3G9, CANADA
| | - Jose Zariffa
- Research, Toronto Rehabilitation Institute - University Health Network, 550 University Ave, #12-102, Toronto, Ontario, M5G 2A2, CANADA
| | - Leen Jabban
- Electronic and Electrical Engineering, University of Bath, Electronic and Electrical Engineering, Claverton Down, Bath, Bath, BA2 7AY, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Shih-Cheng Yen
- Engineering Design and Innovation Centre, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, SINGAPORE
| | - Nick Donaldson
- Medical Physics and Bioengineering, University College London, Gower Street, London, WC1E 6BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Benjamin W Metcalfe
- Electronics & Electrical Engineering, University of Bath, Claverton Down, Bath, Somerset, BA2 7JY, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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3
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Fathi Y, Erfanian A. Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network. Front Neurosci 2022; 16:801818. [PMID: 35401098 PMCID: PMC8990134 DOI: 10.3389/fnins.2022.801818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/02/2022] [Indexed: 11/13/2022] Open
Abstract
To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.
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Affiliation(s)
- Yaser Fathi
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
| | - Abbas Erfanian
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Abbas Erfanian,
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Kim DW, Song KI, Seong D, Lee YS, Baik S, Song JH, Lee HJ, Son D, Pang C. Electrostatic-Mechanical Synergistic In Situ Multiscale Tissue Adhesion for Sustainable Residue-Free Bioelectronics Interfaces. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2105338. [PMID: 34783075 DOI: 10.1002/adma.202105338] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/04/2021] [Indexed: 06/13/2023]
Abstract
Recent studies on soft adhesives have sought to deeply understand how their chemical or mechanical structures interact strongly with living tissues. The aim is to optimally address the unmet needs of patients with acute or chronic diseases. Synergistic adhesion involving both electrostatic (hydrogen bonds) and mechanical interactions (capillarity-assisted suction stress) seems to be effective in overcoming the challenges associated with long-term unstable coupling to tissues. Here, an electrostatically and mechanically synergistic mechanism of residue-free, sustainable, in situ tissue adhesion by implementing hybrid multiscale architectonics. To deduce the mechanism, a thermodynamic model based on a tailored multiscale combinatory adhesive is proposed. The model supports the experimental results that the thermodynamically controlled swelling of the nanoporous hydrogel embedded in the hierarchical elastomeric structure enhances biofluid-insensitive, sustainable, in situ adhesion to diverse soft, slippery, and wet organ surfaces, as well as clean detachment in the peeling direction. Based on the robust tissue adhesion capability, universal reliable measurements of electrophysiological signals generated by various tissues, ranging from rodent sciatic nerve, the muscle, brain, and human skin, are successfully demonstrated.
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Affiliation(s)
- Da Wan Kim
- School of Chemical Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
| | - Kang-Il Song
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, 41061, Republic of Korea
| | - Duhwan Seong
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Yeon Soo Lee
- School of Chemical Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
| | - Sangyul Baik
- School of Chemical Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
| | - Jin Ho Song
- School of Chemical Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
- SKKU Advanced Institute of Nanotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
| | - Heon Joon Lee
- School of Chemical Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
| | - Donghee Son
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Republic of Korea
- Department of Superintelligence Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea
| | - Changhyun Pang
- School of Chemical Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
- Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea
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Choffin Z, Jeong N, Callihan M, Olmstead S, Sazonov E, Thakral S, Getchell C, Lombardi V. Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique. SENSORS (BASEL, SWITZERLAND) 2021; 21:3790. [PMID: 34070843 PMCID: PMC8198704 DOI: 10.3390/s21113790] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 11/16/2022]
Abstract
Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.
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Affiliation(s)
- Zachary Choffin
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA; (Z.C.); (S.O.); (E.S.)
| | - Nathan Jeong
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA; (Z.C.); (S.O.); (E.S.)
| | - Michael Callihan
- College of Nursing, University of Alabama, Tuscaloosa, AL 35487, USA; (M.C.); (S.T.); (C.G.); (V.L.)
| | - Savannah Olmstead
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA; (Z.C.); (S.O.); (E.S.)
| | - Edward Sazonov
- Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA; (Z.C.); (S.O.); (E.S.)
| | - Sarah Thakral
- College of Nursing, University of Alabama, Tuscaloosa, AL 35487, USA; (M.C.); (S.T.); (C.G.); (V.L.)
| | - Camilee Getchell
- College of Nursing, University of Alabama, Tuscaloosa, AL 35487, USA; (M.C.); (S.T.); (C.G.); (V.L.)
| | - Vito Lombardi
- College of Nursing, University of Alabama, Tuscaloosa, AL 35487, USA; (M.C.); (S.T.); (C.G.); (V.L.)
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Seo H, Han SI, Song KI, Seong D, Lee K, Kim SH, Park T, Koo JH, Shin M, Baac HW, Park OK, Oh SJ, Han HS, Jeon H, Kim YC, Kim DH, Hyeon T, Son D. Durable and Fatigue-Resistant Soft Peripheral Neuroprosthetics for In Vivo Bidirectional Signaling. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2007346. [PMID: 33739558 DOI: 10.1002/adma.202007346] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/08/2021] [Indexed: 06/12/2023]
Abstract
Soft neuroprosthetics that monitor signals from sensory neurons and deliver motor information can potentially replace damaged nerves. However, achieving long-term stability of devices interfacing peripheral nerves is challenging, since dynamic mechanical deformations in peripheral nerves cause material degradation in devices. Here, a durable and fatigue-resistant soft neuroprosthetic device is reported for bidirectional signaling on peripheral nerves. The neuroprosthetic device is made of a nanocomposite of gold nanoshell (AuNS)-coated silver (Ag) flakes dispersed in a tough, stretchable, and self-healing polymer (SHP). The dynamic self-healing property of the nanocomposite allows the percolation network of AuNS-coated flakes to rebuild after degradation. Therefore, its degraded electrical and mechanical performance by repetitive, irregular, and intense deformations at the device-nerve interface can be spontaneously self-recovered. When the device is implanted on a rat sciatic nerve, stable bidirectional signaling is obtained for over 5 weeks. Neural signals collected from a live walking rat using these neuroprosthetics are analyzed by a deep neural network to predict the joint position precisely. This result demonstrates that durable soft neuroprosthetics can facilitate collection and analysis of large-sized in vivo data for solving challenges in neurological disorders.
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Affiliation(s)
- Hyunseon Seo
- School of Medicine, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Center for Biomaterials, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Sang Ihn Han
- Center for Nanoparticle Research, Institute of Basic Science (IBS), Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kang-Il Song
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, 41061, Republic of Korea
| | - Duhwan Seong
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Kyungwoo Lee
- Center for Biomaterials, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Sun Hong Kim
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Taesung Park
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Ja Hoon Koo
- Center for Nanoparticle Research, Institute of Basic Science (IBS), Seoul, 08826, Republic of Korea
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Mikyung Shin
- Department of Biomedical Engineering, SKKU Institute for Convergence, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Republic of Korea
| | - Hyoung Won Baac
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Ok Kyu Park
- Center for Nanoparticle Research, Institute of Basic Science (IBS), Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Soong Ju Oh
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Hyung-Seop Han
- Center for Biomaterials, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Hojeong Jeon
- Center for Biomaterials, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Yu-Chan Kim
- Center for Biomaterials, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Dae-Hyeong Kim
- Center for Nanoparticle Research, Institute of Basic Science (IBS), Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Taeghwan Hyeon
- Center for Nanoparticle Research, Institute of Basic Science (IBS), Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Donghee Son
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Republic of Korea
- Department of Superintelligence Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
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7
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Fathi Y, Erfanian A. Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion. J Neural Eng 2021; 18. [PMID: 33395669 DOI: 10.1088/1741-2552/abd82a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 01/04/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord for decoding the hindlimb kinematics during walking on the treadmill. APPROACH Two different experimental paradigms (i.e., active and passive) were used in the current study. During active experiments, five cats were trained to walk bipedally while their hands kept on the front frame of the treadmill for balance or to walk quadrupedally. During passive experiments, the limb was passively moved by the experimenter. Local field potential (LFP) activity was recorded using a microwire array implanted in the dorsal column (DC) and lateral column (LC) of the L3-L4 spinal segments. The amplitude and frequency components of the LFP formed the feature set and the elastic net regularization was used to decode the hindlimb joint angles. MAIN RESULTS The results show that there is no significant difference between the information content of the signals recorded from the DC and LC regions during walking on the treadmill, but the information content of the DC is significantly higher than that of the LC during passively applied movement of the hindlimb in the anesthetized cats. Moreover, the decoding performance obtained using the recorded signals from the DC is comparable with that from the LC during locomotion. But, the decoding performance obtained using the recording channels in the DC is significantly better than that obtained using the signals recorded from the LC. The long-term analysis shows that robust decoding performance can be achieved over 2-3 months without a significant decrease in performance. SIGNIFICANCE This work presents a promising approach to developing a natural and robust motor neuroprosthesis device using descending neural signals to execute the movement and ascending neural signals as the feedback information for control of the movement.
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Affiliation(s)
- Yaser Fathi
- Biomedical Engineering, Iran University of Science and Technology, Narmak, Resalat Square, Hengam Street, Iran University of Science and Technology, Tehran, Tehran, 16844, Iran (the Islamic Republic of)
| | - Abbas Erfanian
- Biomedical Engineering, Iran University of Science & Technology, Hengam Street, Narmak, Tehran 16844, Iran, Tehran, 16844, IRAN, ISLAMIC REPUBLIC OF
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Adaptive self-healing electronic epineurium for chronic bidirectional neural interfaces. Nat Commun 2020; 11:4195. [PMID: 32826916 PMCID: PMC7442836 DOI: 10.1038/s41467-020-18025-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 07/31/2020] [Indexed: 12/21/2022] Open
Abstract
Realizing a clinical-grade electronic medicine for peripheral nerve disorders is challenging owing to the lack of rational material design that mimics the dynamic mechanical nature of peripheral nerves. Electronic medicine should be soft and stretchable, to feasibly allow autonomous mechanical nerve adaptation. Herein, we report a new type of neural interface platform, an adaptive self-healing electronic epineurium (A-SEE), which can form compressive stress-free and strain-insensitive electronics-nerve interfaces and enable facile biofluid-resistant self-locking owing to dynamic stress relaxation and water-proof self-bonding properties of intrinsically stretchable and self-healable insulating/conducting materials, respectively. Specifically, the A-SEE does not need to be sutured or glued when implanted, thereby significantly reducing complexity and the operation time of microneurosurgery. In addition, the autonomous mechanical adaptability of the A-SEE to peripheral nerves can significantly reduce the mechanical mismatch at electronics-nerve interfaces, which minimizes nerve compression-induced immune responses and device failure. Though a small amount of Ag leaked from the A-SEE is observed in vivo (17.03 ppm after 32 weeks of implantation), we successfully achieved a bidirectional neural signal recording and stimulation in a rat sciatic nerve model for 14 weeks. In view of our materials strategy and in vivo feasibility, the mechanically adaptive self-healing neural interface would be considered a new implantable platform for a wide range application of electronic medicine for neurological disorders in the human nervous system. Electronic implantable devices should be soft and stretchable, such that nerves can adapt mechanically and autonomously. Here, the authors present an adaptive self-healing electronic epineurium which can form compressive stress-free and strain-insensitive electronics-nerve interfaces.
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Classification of naturally evoked compound action potentials in peripheral nerve spatiotemporal recordings. Sci Rep 2019; 9:11145. [PMID: 31366940 PMCID: PMC6668407 DOI: 10.1038/s41598-019-47450-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 07/10/2019] [Indexed: 01/21/2023] Open
Abstract
Peripheral neural signals have the potential to provide the necessary motor, sensory or autonomic information for robust control in many neuroprosthetic and neuromodulation applications. However, developing methods to recover information encoded in these signals is a significant challenge. We introduce the idea of using spatiotemporal signatures extracted from multi-contact nerve cuff electrode recordings to classify naturally evoked compound action potentials (CAP). 9 Long-Evan rats were implanted with a 56-channel nerve cuff on the sciatic nerve. Afferent activity was selectively evoked in the different fascicles of the sciatic nerve (tibial, peroneal, sural) using mechano-sensory stimuli. Spatiotemporal signatures of recorded CAPs were used to train three different classifiers. Performance was measured based on the classification accuracy, F1-score, and the ability to reconstruct original firing rates of neural pathways. The mean classification accuracies, for a 3-class problem, for the best performing classifier was 0.686 ± 0.126 and corresponding mean F1-score was 0.605 ± 0.212. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates found for the best classifier was 0.728 ± 0.276. The proposed method demonstrates the possibility of classifying individual naturally evoked CAPs in peripheral neural signals recorded from extraneural electrodes, allowing for more precise control signals in neuroprosthetic applications.
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10
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Fathi Y, Erfanian A. A probabilistic recurrent neural network for decoding hind limb kinematics from multi-segment recordings of the dorsal horn neurons. J Neural Eng 2019; 16:036023. [PMID: 30849772 DOI: 10.1088/1741-2552/ab0e51] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Providing accurate and robust estimates of limb kinematics from recorded neural activities is prominent in closed-loop control of functional electrical stimulation (FES). A major issue in providing accurate decoding the limb kinematics is the decoding model. The primary goal of this study is to develop a decoding approach to model the dynamic interactions of neural systems for accurate decoding. Another critical issue is to find reliable recording sites. Up to now, neural recordings from spinal neural activities were investigated. In this paper, the neural recordings from different vertebrae in decoding limb kinematics are investigated. APPROACH In the current study, a new generative probabilistic model with explicit considering the joint density is developed. Then, an adaptive discriminative learning algorithm is proposed for learning the model. It will be shown that the proposed generative process can be implemented by a recurrent neural network (RNN) with specific structure. We record the neural activities from dorsal horn neurons by using three electrodes placed in the L4, L5, and L6 vertebrae in anesthetized cats. MAIN RESULTS Information theoretic analysis on single-joint movement and multi-segment recordings implies the rostrocaudal distribution of kinematic information. It is demonstrated that during hip movement, best decoding performance is achieved by L4 recordings. For knee and ankle movements, best decodings are achieved by L5, and L6 recordings respectively. It is also shown that the decoding accuracy using multi-segment recordings outperforms decoding accuracy obtained by single-segment recording in multi-joint movement. The results also confirm the superiority of the proposed probabilistic recurrent neural network (PRNN) over the conventional recurrent neural network and Kalman filter ([Formula: see text]). SIGNIFICANCE Multi-segment recordings from dorsal horn neurons as well as the proposed probabilistic recurrent network model provide a promising approach for robust and accurate decoding limb kinematics.
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Affiliation(s)
- Yaser Fathi
- Department of Biomedical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology (IUST), Tehran, Iran
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11
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Compact Neural Interface Using a Single Multichannel Cuff Electrode for a Functional Neuromuscular Stimulation System. Ann Biomed Eng 2018; 47:754-766. [DOI: 10.1007/s10439-018-02181-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 12/01/2018] [Indexed: 10/27/2022]
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12
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Song KI, Park SE, Lee S, Kim H, Lee SH, Youn I. Compact Optical Nerve Cuff Electrode for Simultaneous Neural Activity Monitoring and Optogenetic Stimulation of Peripheral Nerves. Sci Rep 2018; 8:15630. [PMID: 30353118 PMCID: PMC6199280 DOI: 10.1038/s41598-018-33695-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 10/03/2018] [Indexed: 01/05/2023] Open
Abstract
Optogenetic stimulation of the peripheral nervous system is a novel approach to motor control, somatosensory transduction, and pain processing. Various optical stimulation tools have been developed for optogenetic stimulation using optical fibers and light-emitting diodes positioned on the peripheral nerve. However, these tools require additional sensors to monitor the limb or muscle status. We present herein a novel optical nerve cuff electrode that uses a single cuff electrode to conduct to simultaneously monitor neural activity and optogenetic stimulation of the peripheral nerve. The proposed optical nerve cuff electrode is designed with a polydimethylsiloxane substrate, on which electrodes can be positioned to record neural activity. We confirm that the illumination intensity and the electrical properties of the optical nerve cuff electrode are suitable for optical stimulation with simultaneous neural activity monitoring in Thy1::ChR2 transgenic mice. With the proposed electrode, the limb status is monitored with continuous streaming signals during the optical stimulation of anesthetized and moving animals. In conclusion, this optical nerve cuff electrode provides a new optical modulation tool for peripheral nervous system studies.
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Affiliation(s)
- Kang-Il Song
- Biomedical Research Institute, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Sunghee Estelle Park
- Biomedical Research Institute, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul, 02792, Republic of Korea.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Seul Lee
- Department of Dentistry, Graduate School, Kyunghee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Hyungmin Kim
- Biomedical Research Institute, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Soo Hyun Lee
- Brain Science Institute, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Inchan Youn
- Biomedical Research Institute, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul, 02792, Republic of Korea. .,KHU-KIST Department of Converging Science and Technology, Kyung Hee University, Seoul, 02447, Republic of Korea.
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13
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Hope J, Vanholsbeeck F, McDaid A. Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve. Biomed Phys Eng Express 2018; 4. [DOI: 10.1088/2057-1976/aadff3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 09/10/2018] [Indexed: 11/12/2022]
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14
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Han S, Chu JU, Park JW, Youn I. Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings. J Comput Neurosci 2018; 46:77-90. [PMID: 29766393 DOI: 10.1007/s10827-018-0686-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 04/26/2018] [Accepted: 04/30/2018] [Indexed: 12/31/2022]
Abstract
Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems.
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Affiliation(s)
- Sungmin Han
- Biomedical Research Institute, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02791, Republic of Korea
| | - Jun-Uk Chu
- Daegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery and Materials, 330, Techno Sunhwan-ro, Yuga-myeon, Dalseong-gun, Daegu, 42994, Republic of Korea
| | - Jong Woong Park
- Department of Orthopedic Surgery, Korea University College of Medicine, 73, Inchan-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
| | - Inchan Youn
- Biomedical Research Institute, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02791, Republic of Korea.
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02791, Republic of Korea.
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15
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Brunton E, Blau CW, Nazarpour K. Separability of neural responses to standardised mechanical stimulation of limbs. Sci Rep 2017; 7:11138. [PMID: 28894171 PMCID: PMC5593983 DOI: 10.1038/s41598-017-11349-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 08/18/2017] [Indexed: 12/21/2022] Open
Abstract
Considerable scientific and technological efforts are currently being made towards the development of neural prostheses. Understanding how the peripheral nervous system responds to electro-mechanical stimulation of the limb, will help to inform the design of prostheses that can restore function or accelerate recovery from injury to the sensory motor system. However, due to differences in experimental protocols, it is difficult, if not impossible, to make meaningful comparisons between different peripheral nerve interfaces. Therefore, we developed a low-cost electronic system to standardise the mechanical stimulation of a rat’s hindpaw. Three types of mechanical stimulations, namely, proprioception, touch and nociception were delivered to the limb and the electroneurogram signals were recorded simultaneously from the sciatic nerve with a 16-contact cuff electrode. For the first time, results indicate separability of neural responses according to stimulus type as well as intensity. Statistical analysis reveal that cuff contacts placed circumferentially, rather than longitudinally, are more likely to lead to higher classification rates. This flexible setup may be readily adapted for systematic comparison of various electrodes and mechanical stimuli in rodents. Hence, we have made its electro-mechanical design and computer programme available online
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Affiliation(s)
- Emma Brunton
- School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, Newcastle, UK.
| | - Christoph W Blau
- Faculty of Medical Sciences, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, Newcastle, UK
| | - Kianoush Nazarpour
- School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, Newcastle, UK.,Institute of Neuroscience, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, Newcastle, UK
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