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Wu X, Ye Y, Sun M, Mei Y, Ji B, Wang M, Song E. Recent Progress of Soft and Bioactive Materials in Flexible Bioelectronics. CYBORG AND BIONIC SYSTEMS 2025; 6:0192. [PMID: 40302943 PMCID: PMC12038164 DOI: 10.34133/cbsystems.0192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 08/22/2024] [Accepted: 09/22/2024] [Indexed: 05/02/2025] Open
Abstract
Materials that establish functional, stable interfaces to targeted tissues for long-term monitoring/stimulation equipped with diagnostic/therapeutic capabilities represent breakthroughs in biomedical research and clinical medicine. A fundamental challenge is the mechanical and chemical mismatch between tissues and implants that ultimately results in device failure for corrosion by biofluids and associated foreign body response. Of particular interest is in the development of bioactive materials at the level of chemistry and mechanics for high-performance, minimally invasive function, simultaneously with tissue-like compliance and in vivo biocompatibility. This review summarizes the most recent progress for these purposes, with an emphasis on material properties such as foreign body response, on integration schemes with biological tissues, and on their use as bioelectronic platforms. The article begins with an overview of emerging classes of material platforms for bio-integration with proven utility in live animal models, as high performance and stable interfaces with different form factors. Subsequent sections review various classes of flexible, soft tissue-like materials, ranging from self-healing hydrogel/elastomer to bio-adhesive composites and to bioactive materials. Additional discussions highlight examples of active bioelectronic systems that support electrophysiological mapping, stimulation, and drug delivery as treatments of related diseases, at spatiotemporal resolutions that span from the cellular level to organ-scale dimension. Envisioned applications involve advanced implants for brain, cardiac, and other organ systems, with capabilities of bioactive materials that offer stability for human subjects and live animal models. Results will inspire continuing advancements in functions and benign interfaces to biological systems, thus yielding therapy and diagnostics for human healthcare.
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Affiliation(s)
- Xiaojun Wu
- Institute of Optoelectronics & Department of Materials Science, Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, State Key Laboratory of Integrated Chips and Systems (SKLICS),
Fudan University, Shanghai 200438, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, State Key Laboratory of Molecular Engineering of Polymer,
Fudan University, Shanghai 200438, China
| | - Yuanming Ye
- Unmanned System Research Institute, National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi’an 710072, China
- Queen Mary University of London Engineering School, Northwestern Polytechnical University, Xi’an 710072, China
| | - Mubai Sun
- Institute of Optoelectronics & Department of Materials Science, Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, State Key Laboratory of Integrated Chips and Systems (SKLICS),
Fudan University, Shanghai 200438, China
- Institute of Agro-food Technology, Jilin Academy of Agricultural Sciences (Northeast Agricultural Research Center of China), Changchun, China
| | - Yongfeng Mei
- Institute of Optoelectronics & Department of Materials Science, Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, State Key Laboratory of Integrated Chips and Systems (SKLICS),
Fudan University, Shanghai 200438, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, State Key Laboratory of Molecular Engineering of Polymer,
Fudan University, Shanghai 200438, China
- International Institute for Intelligent Nanorobots and Nanosystems,
Neuromodulation and Brain-machine-interface Centre, Fudan University, Shanghai 200438, China
- Yiwu Research Institute of Fudan University, Yiwu, Zhejiang 322000, China
| | - Bowen Ji
- Unmanned System Research Institute, National Key Laboratory of Unmanned Aerial Vehicle Technology, Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Ming Wang
- Institute of Optoelectronics & Department of Materials Science, Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, State Key Laboratory of Integrated Chips and Systems (SKLICS),
Fudan University, Shanghai 200438, China
- Frontier Institute of Chip and System,
Fudan University, Shanghai 200433, China
| | - Enming Song
- Institute of Optoelectronics & Department of Materials Science, Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, State Key Laboratory of Integrated Chips and Systems (SKLICS),
Fudan University, Shanghai 200438, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, State Key Laboratory of Molecular Engineering of Polymer,
Fudan University, Shanghai 200438, China
- International Institute for Intelligent Nanorobots and Nanosystems,
Neuromodulation and Brain-machine-interface Centre, Fudan University, Shanghai 200438, China
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Erofeev A, Antifeev I, Bolshakova A, Bezprozvanny I, Vlasova O. In Vivo Penetrating Microelectrodes for Brain Electrophysiology. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239085. [PMID: 36501805 PMCID: PMC9735502 DOI: 10.3390/s22239085] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 05/13/2023]
Abstract
In recent decades, microelectrodes have been widely used in neuroscience to understand the mechanisms behind brain functions, as well as the relationship between neural activity and behavior, perception and cognition. However, the recording of neuronal activity over a long period of time is limited for various reasons. In this review, we briefly consider the types of penetrating chronic microelectrodes, as well as the conductive and insulating materials for microelectrode manufacturing. Additionally, we consider the effects of penetrating microelectrode implantation on brain tissue. In conclusion, we review recent advances in the field of in vivo microelectrodes.
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Affiliation(s)
- Alexander Erofeev
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Correspondence: (A.E.); (O.V.)
| | - Ivan Antifeev
- Laboratory of Methods and Instruments for Genetic and Immunoassay Analysis, Institute for Analytical Instrumentation of the Russian Academy of Sciences, 198095 Saint Petersburg, Russia
| | - Anastasia Bolshakova
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
| | - Ilya Bezprozvanny
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Department of Physiology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390, USA
| | - Olga Vlasova
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Correspondence: (A.E.); (O.V.)
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Kang YN, Chou N, Jang JW, Choe HK, Kim S. A 3D flexible neural interface based on a microfluidic interconnection cable capable of chemical delivery. MICROSYSTEMS & NANOENGINEERING 2021; 7:66. [PMID: 34567778 PMCID: PMC8433186 DOI: 10.1038/s41378-021-00295-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/23/2021] [Accepted: 07/11/2021] [Indexed: 05/23/2023]
Abstract
The demand for multifunctional neural interfaces has grown due to the need to provide a better understanding of biological mechanisms related to neurological diseases and neural networks. Direct intracerebral drug injection using microfluidic neural interfaces is an effective way to deliver drugs to the brain, and it expands the utility of drugs by bypassing the blood-brain barrier (BBB). In addition, uses of implantable neural interfacing devices have been challenging due to inevitable acute and chronic tissue responses around the electrodes, pointing to a critical issue still to be overcome. Although neural interfaces comprised of a collection of microneedles in an array have been used for various applications, it has been challenging to integrate microfluidic channels with them due to their characteristic three-dimensional structures, which differ from two-dimensionally fabricated shank-type neural probes. Here we present a method to provide such three-dimensional needle-type arrays with chemical delivery functionality. We fabricated a microfluidic interconnection cable (µFIC) and integrated it with a flexible penetrating microelectrode array (FPMA) that has a 3-dimensional structure comprised of silicon microneedle electrodes supported by a flexible array base. We successfully demonstrated chemical delivery through the developed device by recording neural signals acutely from in vivo brains before and after KCl injection. This suggests the potential of the developed microfluidic neural interface to contribute to neuroscience research by providing simultaneous signal recording and chemical delivery capabilities.
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Affiliation(s)
- Yoo Na Kang
- Department of Medical Assistant Robot, Korea Institute of Machinery & Materials (KIMM), Daegu, Republic of Korea
| | - Namsun Chou
- Center for BioMicrosystems, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Jae-Won Jang
- Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea
| | - Han Kyoung Choe
- Department of Brain & Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea
| | - Sohee Kim
- Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea
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Bollimunta A, Santacruz SR, Eaton RW, Xu PS, Morrison JH, Moxon KA, Carmena JM, Nassi JJ. Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque. Cell Rep 2021; 35:109239. [PMID: 34133921 PMCID: PMC8236375 DOI: 10.1016/j.celrep.2021.109239] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 04/07/2021] [Accepted: 05/18/2021] [Indexed: 01/07/2023] Open
Abstract
Microendoscopic calcium imaging with one-photon miniature microscopes enables unprecedented readout of neural circuit dynamics during active behavior in rodents. In this study, we describe successful application of this technology in the rhesus macaque, demonstrating plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from large populations of neurons simultaneously in bilateral dorsal premotor cortices during performance of a naturalistic motor reach task. Imaging is stable over several months, allowing us to longitudinally track individual neurons and monitor their relationship to motor behavior over time. We observe neuronal calcium dynamics selective for reach direction, which we could use to decode the animal's trial-by-trial motor behavior. This work establishes head-mounted microendoscopic calcium imaging in macaques as a powerful approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and it promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease.
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Affiliation(s)
- Anil Bollimunta
- Inscopix, Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA,These authors contributed equally
| | - Samantha R. Santacruz
- Department of Electrical Engineering and Computer Science, Helen Wills Neuroscience Institute, University of California, Berkeley, 286 Li Ka Shing, MC #3370, Berkeley, CA 94720, USA,Department of Biomedical Engineering, Institute for Neuroscience, The University of Texas at Austin, 107 W. Dean Keeton Street, Stop C0800, Austin, TX 78712, USA,These authors contributed equally
| | - Ryan W. Eaton
- Department of Biomedical Engineering, University of California, Davis, 3141 Health Sciences Drive, Davis, CA 95616, USA,California National Primate Research Center, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Pei S. Xu
- Inscopix, Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA
| | - John H. Morrison
- California National Primate Research Center, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA,Department of Neurology, School of Medicine, University of California Davis, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Karen A. Moxon
- Department of Biomedical Engineering, University of California, Davis, 3141 Health Sciences Drive, Davis, CA 95616, USA,California National Primate Research Center, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Jose M. Carmena
- Department of Electrical Engineering and Computer Science, Helen Wills Neuroscience Institute, University of California, Berkeley, 286 Li Ka Shing, MC #3370, Berkeley, CA 94720, USA,Senior author
| | - Jonathan J. Nassi
- Inscopix, Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA,Senior author,Lead contact,Correspondence:
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Grob L, Rinklin P, Zips S, Mayer D, Weidlich S, Terkan K, Weiß LJK, Adly N, Offenhäusser A, Wolfrum B. Inkjet-Printed and Electroplated 3D Electrodes for Recording Extracellular Signals in Cell Culture. SENSORS (BASEL, SWITZERLAND) 2021; 21:3981. [PMID: 34207725 PMCID: PMC8229631 DOI: 10.3390/s21123981] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/04/2021] [Accepted: 06/06/2021] [Indexed: 02/07/2023]
Abstract
Recent investigations into cardiac or nervous tissues call for systems that are able to electrically record in 3D as opposed to 2D. Typically, challenging microfabrication steps are required to produce 3D microelectrode arrays capable of recording at the desired position within the tissue of interest. As an alternative, additive manufacturing is becoming a versatile platform for rapidly prototyping novel sensors with flexible geometric design. In this work, 3D MEAs for cell-culture applications were fabricated using a piezoelectric inkjet printer. The aspect ratio and height of the printed 3D electrodes were user-defined by adjusting the number of deposited droplets of silver nanoparticle ink along with a continuous printing method and an appropriate drop-to-drop delay. The Ag 3D MEAs were later electroplated with Au and Pt in order to reduce leakage of potentially cytotoxic silver ions into the cellular medium. The functionality of the array was confirmed using impedance spectroscopy, cyclic voltammetry, and recordings of extracellular potentials from cardiomyocyte-like HL-1 cells.
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Affiliation(s)
- Leroy Grob
- Neuroelectronics, Department of Electrical and Computer Engineering, MSB, MSRM, Technical University of Munich, Boltzmannstraße 11, 85748 Garching, Germany; (L.G.); (P.R.); (S.Z.); (K.T.); (L.J.K.W.); (N.A.)
| | - Philipp Rinklin
- Neuroelectronics, Department of Electrical and Computer Engineering, MSB, MSRM, Technical University of Munich, Boltzmannstraße 11, 85748 Garching, Germany; (L.G.); (P.R.); (S.Z.); (K.T.); (L.J.K.W.); (N.A.)
| | - Sabine Zips
- Neuroelectronics, Department of Electrical and Computer Engineering, MSB, MSRM, Technical University of Munich, Boltzmannstraße 11, 85748 Garching, Germany; (L.G.); (P.R.); (S.Z.); (K.T.); (L.J.K.W.); (N.A.)
| | - Dirk Mayer
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; (D.M.); (S.W.); (A.O.)
| | - Sabrina Weidlich
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; (D.M.); (S.W.); (A.O.)
| | - Korkut Terkan
- Neuroelectronics, Department of Electrical and Computer Engineering, MSB, MSRM, Technical University of Munich, Boltzmannstraße 11, 85748 Garching, Germany; (L.G.); (P.R.); (S.Z.); (K.T.); (L.J.K.W.); (N.A.)
| | - Lennart J. K. Weiß
- Neuroelectronics, Department of Electrical and Computer Engineering, MSB, MSRM, Technical University of Munich, Boltzmannstraße 11, 85748 Garching, Germany; (L.G.); (P.R.); (S.Z.); (K.T.); (L.J.K.W.); (N.A.)
| | - Nouran Adly
- Neuroelectronics, Department of Electrical and Computer Engineering, MSB, MSRM, Technical University of Munich, Boltzmannstraße 11, 85748 Garching, Germany; (L.G.); (P.R.); (S.Z.); (K.T.); (L.J.K.W.); (N.A.)
| | - Andreas Offenhäusser
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; (D.M.); (S.W.); (A.O.)
| | - Bernhard Wolfrum
- Neuroelectronics, Department of Electrical and Computer Engineering, MSB, MSRM, Technical University of Munich, Boltzmannstraße 11, 85748 Garching, Germany; (L.G.); (P.R.); (S.Z.); (K.T.); (L.J.K.W.); (N.A.)
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Dunlap CF, Colachis SC, Meyers EC, Bockbrader MA, Friedenberg DA. Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review. Front Neurorobot 2020; 14:558987. [PMID: 33162885 PMCID: PMC7581895 DOI: 10.3389/fnbot.2020.558987] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 09/09/2020] [Indexed: 12/18/2022] Open
Abstract
Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic in vivo environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. In-vivo diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.
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Affiliation(s)
- Collin F. Dunlap
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Samuel C. Colachis
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Eric C. Meyers
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Marcia A. Bockbrader
- Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, United States
| | - David A. Friedenberg
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
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Raspopovic S, Cimolato A, Panarese A, Vallone F, Del Valle J, Micera S, Navarro X. Neural signal recording and processing in somatic neuroprosthetic applications. A review. J Neurosci Methods 2020; 337:108653. [PMID: 32114143 DOI: 10.1016/j.jneumeth.2020.108653] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 11/30/2019] [Accepted: 02/26/2020] [Indexed: 12/11/2022]
Abstract
Neurointerfaces have acquired major relevance as both rehabilitative and therapeutic tools for patients with spinal cord injury, limb amputations and other neural disorders. Bidirectional neural interfaces are a key component for the functional control of neuroprosthetic devices. The two main neuroprosthetic applications of interfaces with the peripheral nervous system (PNS) are: the refined control of artificial prostheses with sensory neural feedback, and functional electrical stimulation (FES) systems attempting to generate motor or visceral responses in paralyzed organs. The results obtained in experimental and clinical studies with both, extraneural and intraneural electrodes are very promising in terms of the achieved functionality for the neural stimulation mode. However, the results of neural recordings with peripheral nerve interfaces are more limited. In this paper we review the different existing approaches for PNS signals recording, denoising, processing and classification, enabling their use for bidirectional interfaces. PNS recordings can provide three types of signals: i) population activity signals recorded by using extraneural electrodes placed on the outer surface of the nerve, which carry information about cumulative nerve activity; ii) spike activity signals recorded with intraneural electrodes placed inside the nerve, which carry information about the electrical activity of a set of individual nerve fibers; and iii) hybrid signals, which contain both spiking and cumulative signals. Finally, we also point out some of the main limitations, which are hampering clinical translation of neural decoding, and indicate possible solutions for improvement.
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Affiliation(s)
- Stanisa Raspopovic
- Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zürich, Switzerland
| | - Andrea Cimolato
- Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zürich, Switzerland; NEARLab - Neuroengineering and Medical Robotics Laboratory, DEIB Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133, Milano, Italy; IIT Central Research Labs Genova, Istituto Italiano Tecnologia, 16163, Genova, Italy
| | | | - Fabio Vallone
- The BioRobotics Institute, Scuola Superiore Sant'Anna, I-56127, Pisa, Italy
| | - Jaume Del Valle
- Institute of Neurosciences and Department of Cell Biology, Physiology and Immunology, Universitat Autònoma De Barcelona, CIBERNED, 08193, Bellaterra, Spain
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, I-56127, Pisa, Italy; Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale De Lausanne, Lausanne, CH-1015, Switzerland.
| | - Xavier Navarro
- Institute of Neurosciences and Department of Cell Biology, Physiology and Immunology, Universitat Autònoma De Barcelona, CIBERNED, 08193, Bellaterra, Spain; Institut Guttmann De Neurorehabilitació, Badalona, Spain.
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