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Journal articles on the topic 'Neural interfaces'

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Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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1

Grill, Warren. "Neural Interfaces." American Scientist 98, no. 1 (2010): 48. http://dx.doi.org/10.1511/2010.82.48.

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2

Warden, Melissa R., Jessica A. Cardin, and Karl Deisseroth. "Optical Neural Interfaces." Annual Review of Biomedical Engineering 16, no. 1 (July 11, 2014): 103–29. http://dx.doi.org/10.1146/annurev-bioeng-071813-104733.

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3

Zhang, Milin, Zijian Tang, Xilin Liu, and Jan Van der Spiegel. "Electronic neural interfaces." Nature Electronics 3, no. 4 (April 2020): 191–200. http://dx.doi.org/10.1038/s41928-020-0390-3.

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4

Zhang, Hongzhi, Mei Yu, Lei Xie, Linlin Jin, and Zhe Yu. "Carbon-Nanofibers-Based Micro-/Nanodevices for Neural-Electrical and Neural-Chemical Interfaces." Journal of Nanomaterials 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/280902.

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Carbon nanofibers (CNFs) have shown great potentials for development of micro-/nanodevices for neural interfaces due to their suitable properties, such as chemical stability, good electrical conductivity, ultramicro size with low electrical impedance, 3D structures with high surface-to-volume ratio, and long-term biocompatibility. In this paper, we review the applications of CNFs as neural-electrical interfaces and neural-chemical interfaces for neural recording and stimulation, electroconductive nanofibrous scaffolds for nerve tissue engineering, drug and gene delivery, and neurochemical sensing. The CNFs-based micro-/nanodevices provide new platforms to fine-tune electrical and chemical cues of neurons at subcellular nanoscale, which can be used for both fundamental studies of material-cell interactions and the development of chronically stable, implantable neural interface devices. Further development of this technology may potentially enable a highly multiplex closed-loop system with multifunctions for neuromodulation and neuroprostheses.
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5

Ahmed, Zabir, Jay W. Reddy, Mohammad H. Malekoshoaraie, Vahid Hassanzade, Ibrahim Kimukin, Vishal Jain, and Maysamreza Chamanzar. "Flexible optoelectric neural interfaces." Current Opinion in Biotechnology 72 (December 2021): 121–30. http://dx.doi.org/10.1016/j.copbio.2021.11.001.

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6

Kuncel, Alexis M., and Warren M. Grill. "NIH Neural Interfaces Workshop." Expert Review of Medical Devices 3, no. 6 (November 2006): 695–97. http://dx.doi.org/10.1586/17434440.3.6.695.

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7

Bellamkonda, Ravi V., S. Balakrishna Pai, and Philippe Renaud. "Materials for neural interfaces." MRS Bulletin 37, no. 6 (June 2012): 557–61. http://dx.doi.org/10.1557/mrs.2012.122.

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8

Sheng, Hao, Xiaomeng Wang, Ning Kong, Wang Xi, Hang Yang, Xiaotong Wu, Kangling Wu, et al. "Neural interfaces by hydrogels." Extreme Mechanics Letters 30 (July 2019): 100510. http://dx.doi.org/10.1016/j.eml.2019.100510.

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9

Wang, Yongchen, Hanlin Zhu, Huiran Yang, Aaron D. Argall, Lan Luan, Chong Xie, and Liang Guo. "Nano functional neural interfaces." Nano Research 11, no. 10 (July 10, 2018): 5065–106. http://dx.doi.org/10.1007/s12274-018-2127-4.

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10

Wang, Xiaomeng, Hao Sheng, and Hao Wang. "Neural interfaces by hydrogels." IBRO Reports 6 (September 2019): S394. http://dx.doi.org/10.1016/j.ibror.2019.07.1252.

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11

Cheung, Karen C. "Implantable microscale neural interfaces." Biomedical Microdevices 9, no. 6 (January 25, 2007): 923–38. http://dx.doi.org/10.1007/s10544-006-9045-z.

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12

Kotov, Nicholas A., Jessica O. Winter, Isaac P. Clements, Edward Jan, Brian P. Timko, Stéphane Campidelli, Smita Pathak, et al. "Nanomaterials for Neural Interfaces." Advanced Materials 21, no. 40 (October 26, 2009): 3970–4004. http://dx.doi.org/10.1002/adma.200801984.

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13

Chen, Daofen, Stephanie J. Fertig, Naomi Kleitman, Roger L. Miller, Eugene Oliver, Grace C. Y. Peng, Nancy L. Shinowara, Michael Weinrich, and Joseph J. Pancrazio. "Advances in neural interfaces: report from the 2006 NIH Neural Interfaces Workshop." Journal of Neural Engineering 4, no. 3 (May 21, 2007): S137—S142. http://dx.doi.org/10.1088/1741-2560/4/3/s01.

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14

Abidian, Mohammad Reza, and David C. Martin. "Neural Interface Biomaterials: Multifunctional Nanobiomaterials for Neural Interfaces (Adv. Funct. Mater. 4/2009)." Advanced Functional Materials 19, no. 4 (February 24, 2009): NA. http://dx.doi.org/10.1002/adfm.200990009.

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15

Лунев, Д. В., С. К. Полетыкин, and Д. О. Кудрявцев. "Brain-computer interfaces: technology overview and modern solutions." Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies 2, no. 3 (July 12, 2022): 0117–26. http://dx.doi.org/10.47813/2782-2818-2022-2-3-0117-0126.

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The purpose of this study is to provide an overview of the current state of neural interface technology and to compare their various modern implementations with each other, highlighting their advantages and features. The article considers the essence of the concept of "neural interface", its purpose, disassembled the structure of this technology and the principles underlying it, as well as classification according to various criteria. Examples of areas of activity in which this technology is currently used or can potentially be applied in the future are given. In addition, the most commonly used modern solutions are collected and analyzed in order to identify the most promising option in terms of functionality and convenience of everyday use. It has been established that the Emotiv Epoc neural interface has the widest functionality with comfortable everyday wear. It was also concluded that the areas of application in which solutions based on neural interfaces currently show the best results are medical diagnostics and remote control of electronic devices, as evidenced by the large number of projects involving neural interfaces in this area and a large number of articles, dedicated to them.
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16

Eiber, Calvin, Jean Delbeke, Jorge Cardoso, Martijn de Neeling, Sam John, Chang Won Lee, Jerry Skefos, Argus Sun, Dimiter Prodanov, and Zach McKinney. "Preliminary Minimum Reporting Requirements for In-Vivo Neural Interface Research: I. Implantable Neural Interfaces." IEEE Open Journal of Engineering in Medicine and Biology 2 (2021): 74–83. http://dx.doi.org/10.1109/ojemb.2021.3060919.

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17

Peng, Chung-Ching, Zhiming Xiao, and Rizwan Bashirullah. "Toward Energy Efficient Neural Interfaces." IEEE Transactions on Biomedical Engineering 56, no. 11 (November 2009): 2697–700. http://dx.doi.org/10.1109/tbme.2009.2029704.

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18

Asplund, Maria, Tobias Nyberg, and Olle Inganäs. "Electroactive polymers for neural interfaces." Polymer Chemistry 1, no. 9 (2010): 1374. http://dx.doi.org/10.1039/c0py00077a.

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19

Fang, Yan, Xinming Li, and Ying Fang. "Organic bioelectronics for neural interfaces." Journal of Materials Chemistry C 3, no. 25 (2015): 6424–30. http://dx.doi.org/10.1039/c5tc00569h.

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20

Tyler, Dustin J. "Neural interfaces for somatosensory feedback." Current Opinion in Neurology 28, no. 6 (December 2015): 574–81. http://dx.doi.org/10.1097/wco.0000000000000266.

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21

Pancrazio, Joseph J. "Neural interfaces at the nanoscale." Nanomedicine 3, no. 6 (December 2008): 823–30. http://dx.doi.org/10.2217/17435889.3.6.823.

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22

Ware, Taylor, Dustin Simon, Robert L. Rennaker, and Walter Voit. "Smart Polymers for Neural Interfaces." Polymer Reviews 53, no. 1 (January 2013): 108–29. http://dx.doi.org/10.1080/15583724.2012.751924.

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23

Abidian, Mohammad Reza, and David C. Martin. "Multifunctional Nanobiomaterials for Neural Interfaces." Advanced Functional Materials 19, no. 4 (February 24, 2009): 573–85. http://dx.doi.org/10.1002/adfm.200801473.

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24

Yang, Letao, Brian M. Conley, Jinho Yoon, Christopher Rathnam, Thanapat Pongkulapa, Brandon Conklin, Yannan Hou, and Ki-Bum Lee. "High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach." Research 2022 (September 15, 2022): 1–15. http://dx.doi.org/10.34133/2022/9784273.

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A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies. Nevertheless, high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome. To this end, we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures (physical cues comprising geometrical, topographical, and mechanical aspects) and the effects of these complex physical cues on stem cell fate decisions. Furthermore, by applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, which allowed for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces, potentially facilitating precision, and personalized tissue engineering applications.
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25

Deshmukh, Ashlesha, Logan Brown, Mary F. Barbe, Alan S. Braverman, Ekta Tiwari, Lucas Hobson, Sudha Shunmugam, et al. "Fully implantable neural recording and stimulation interfaces: Peripheral nerve interface applications." Journal of Neuroscience Methods 333 (March 2020): 108562. http://dx.doi.org/10.1016/j.jneumeth.2019.108562.

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26

KAZANTSEV, V. B., V. I. NEKORKIN, S. MORFU, J. M. BILBAULT, and P. MARQUIÉ. "PROPAGATING INTERFACES IN A TWO-LAYER BISTABLE NEURAL NETWORK." International Journal of Bifurcation and Chaos 16, no. 03 (March 2006): 589–600. http://dx.doi.org/10.1142/s0218127406015003.

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The dynamics of propagating interfaces in a bistable neural network is investigated. We consider the network composed of two coupled 1D lattices and assume that they interact in a local spatial point (pin contact). The network unit is modeled by the FitzHugh–Nagumo-like system in a bistable oscillator mode. The interfaces describe the transition of the network units from the rest (unexcited) state to the excited state where each unit exhibits periodic sequences of excitation pulses or action potentials. We show how the localized inter-layer interaction provides an "excitatory" or "inhibitory" action to the oscillatory activity. In particular, we describe the interface propagation failure and the initiation of spreading activity due to the pin contact. We provide analytical results, computer simulations and physical experiments with two-layer electronic arrays of bistable cells.
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27

Muller, Rikky, Mohammad Meraj Ghanbari, and Andy Zhou. "Miniaturized Wireless Neural Interfaces: A tutorial." IEEE Solid-State Circuits Magazine 13, no. 4 (2021): 88–97. http://dx.doi.org/10.1109/mssc.2021.3111387.

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28

Ramezani, Zeinab, Kyung Jin Seo, and Hui Fang. "Hybrid electrical and optical neural interfaces." Journal of Micromechanics and Microengineering 31, no. 4 (March 19, 2021): 044002. http://dx.doi.org/10.1088/1361-6439/abeb30.

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29

Thompson, Cort H., Marissa J. Zoratti, Nicholas B. Langhals, and Erin K. Purcell. "Regenerative Electrode Interfaces for Neural Prostheses." Tissue Engineering Part B: Reviews 22, no. 2 (April 2016): 125–35. http://dx.doi.org/10.1089/ten.teb.2015.0279.

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30

Straiton, Jenny. "Neural–digital interfaces: creating bionic humans." BioTechniques 69, no. 3 (September 2020): 153–55. http://dx.doi.org/10.2144/btn-2020-0120.

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31

TOM, PAGE, and THORSTEINSSON GISLI. "NEURAL INTERFACES IN DIGITAL PRODUCT DESIGN." i-manager's Journal on Digital Signal Processing 6, no. 1 (2018): 1. http://dx.doi.org/10.26634/jdp.6.1.15155.

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32

Jackson, Andrew. "Neural interfaces take another step forward." Nature 539, no. 7628 (November 2016): 177–78. http://dx.doi.org/10.1038/539177a.

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33

Ware, Taylor, Dustin Simon, David E. Arreaga-Salas, Jonathan Reeder, Robert Rennaker, Edward W. Keefer, and Walter Voit. "Fabrication of Responsive, Softening Neural Interfaces." Advanced Functional Materials 22, no. 16 (May 2, 2012): 3470–79. http://dx.doi.org/10.1002/adfm.201200200.

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34

Fairfield, Jessamyn A. "Nanostructured Materials for Neural Electrical Interfaces." Advanced Functional Materials 28, no. 12 (August 2, 2017): 1701145. http://dx.doi.org/10.1002/adfm.201701145.

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35

Song, Yong-Ak, Ahmed M. S. Ibrahim, Amr N. Rabie, Jongyoon Han, and Samuel J. Lin. "Microfabricated nerve–electrode interfaces in neural prosthetics and neural engineering." Biotechnology and Genetic Engineering Reviews 29, no. 2 (October 2013): 113–34. http://dx.doi.org/10.1080/02648725.2013.801231.

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36

Chapman, Christopher A. R., Noah Goshi, and Erkin Seker. "Multifunctional Neural Interfaces for Closed-Loop Control of Neural Activity." Advanced Functional Materials 28, no. 12 (August 28, 2017): 1703523. http://dx.doi.org/10.1002/adfm.201703523.

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37

Sridharan, Arati, and Jit Muthuswamy. "Soft, Conductive, Brain-Like, Coatings at Tips of Microelectrodes Improve Electrical Stability under Chronic, In Vivo Conditions." Micromachines 12, no. 7 (June 28, 2021): 761. http://dx.doi.org/10.3390/mi12070761.

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Several recent studies have reported improved histological and electrophysiological outcomes with soft neural interfaces that have elastic moduli ranging from 10 s of kPa to hundreds of MPa. However, many of these soft interfaces use custom fabrication processes. We test the hypothesis that a readily adoptable fabrication process for only coating the tips of microelectrodes with soft brain-like (elastic modulus of ~5 kPa) material improves the long-term electrical performance of neural interfaces. Conventional tungsten microelectrodes (n = 9 with soft coatings and n = 6 uncoated controls) and Pt/Ir microelectrodes (n = 16 with soft coatings) were implanted in six animals for durations ranging from 5 weeks to over 1 year in a subset of rats. Electrochemical impedance spectroscopy was used to assess the quality of the brain tissue–electrode interface under chronic conditions. Neural recordings were assessed for unit activity and signal quality. Electrodes with soft, silicone coatings showed relatively stable electrical impedance characteristics over 6 weeks to >1 year compared to the uncoated control electrodes. Single unit activity recorded by coated electrodes showed larger peak-to-peak amplitudes and increased number of detectable neurons compared to uncoated controls over 6–7 weeks. We demonstrate the feasibility of using a readily translatable process to create brain-like soft interfaces that can potentially overcome variable performance associated with chronic rigid neural interfaces.
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38

Valle, Giacomo. "The Connection Between the Nervous System and Machines: Commentary." Journal of Medical Internet Research 21, no. 11 (November 6, 2019): e16344. http://dx.doi.org/10.2196/16344.

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Decades of technological developments have populated the field of brain-machine interfaces and neuroprosthetics with several replacement strategies, neural modulation treatments, and rehabilitation techniques to improve the quality of life for patients affected by sensory and motor disabilities. This field is now quickly expanding thanks to advances in neural interfaces, machine learning techniques, and robotics. Despite many clinical successes, and multiple innovations in animal models, brain-machine interfaces remain mainly confined to sophisticated laboratory environments indicating a necessary step forward in the used technology. Interestingly, Elon Musk and Neuralink have recently presented a new brain-machine interface platform with thousands of channels, fast implantation, and advanced signal processing. Here, how their work takes part in the context of the restoration of sensory-motor functions through neuroprostheses is commented.
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39

Sridharan, Arati, Vikram Kodibagkar, and Jit Muthuswamy. "Penetrating Microindentation of Hyper-soft, Conductive Silicone Neural Interfaces in Vivo Reveals Significantly Lower Mechanical Stresses." MRS Advances 4, no. 46-47 (2019): 2551–58. http://dx.doi.org/10.1557/adv.2019.356.

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ABSTRACTThere is growing evidence that minimizing the mechanical mismatch between neural implants and brain tissue mitigates inflammatory, biological responses at the interface under long-term implant conditions. The goal of this study is to develop a brain-like soft, conductive neural interface and use an improvised, penetrating microindentation technique reported by us earlier to quantitatively assess the (a) elastic modulus of the neural interface after implantation, (b) mechanical stresses during penetration of the probe, and (c) periodic stresses at steady-state due to tissue micromotion around the probe. We fabricated poly- dimethylsiloxane (PDMS) matrices with multi-walled carbon nanotubes (MWCNTs) using two distinct but carefully calibrated cross-linking ratios, resulting in hard (elastic modulus∼484 kPa) or soft, brain-like (elastic modulus∼5.7 kPa) matrices, the latter being at least 2 orders of magnitude softer than soft neural interfaces reported so far. Subsequent loading of the hard and soft silicone based matrices with (100% w/w) low-molecular weight PDMS siloxanes resulted in further decrease in the elastic modulus of both matrices. Carbon probes with soft PDMS coating show significantly less maximum axial forces (-587 ± 51.5 µN) imposed on the brain than hard PDMS coated probes (-1,253 ± 252 µN) during and after insertion. Steady-state, physiological micromotion related stresses were also significantly less for soft- PDMS coated probes (55.5 ± 17.4 Pa) compared to hard-PDMS coated probes (141.0 ± 21.7 Pa). The penetrating microindentation technique is valuable to quantitatively assess the mechanical properties of neural interfaces in both acute and chronic conditions.
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40

Tong, Yuxin, Jamie M. Murbach, Vivek Subramanian, Shrirang Chhatre, Francisco Delgado, David C. Martin, Kevin J. Otto, Mario Romero-Ortega, and Blake N. Johnson. "A Hybrid 3D Printing and Robotic-assisted Embedding Approach for Design and Fabrication of Nerve Cuffs with Integrated Locking Mechanisms." MRS Advances 3, no. 40 (2018): 2365–72. http://dx.doi.org/10.1557/adv.2018.378.

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ABSTRACTThe ability to interface electronic materials with the peripheral nervous system is required for stimulation and monitoring of neural signals. Thus, the design and engineering of robust neural interfaces that maintain material-tissue contact in the presence of material or tissue micromotion offer the potential to conduct novel measurements and develop future therapies that require chronic interface with the peripheral nervous system. However, such remains an open challenge given the constraints of existing materials sets and manufacturing approaches for design and fabrication of neural interfaces. Here, we investigated the potential to leverage a rapid prototyping approach for the design and fabrication of nerve cuffs that contain supporting features to mechanically stabilize the interaction between cuff electrodes and peripheral nerve. A hybrid 3D printing and robotic-embedding (i.e., pick-and-place) system was used to design and fabricate silicone nerve cuffs (800 µm diameter) containing conforming platinum (Pt) electrodes. We demonstrate that the electrical impedance of the cuff electrodes can be reduced by deposition of the conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on cuff electrodes via a post-processing electropolymerization technique. The computer-aided design and manufacturing approach was also used to design and integrate supporting features to the cuff that mechanically stabilize the interface between the cuff electrodes and the peripheral nerve. Both ‘self-locking’ and suture-assisted locking mechanisms are demonstrated based on the principle of making geometric alterations to the cuff opening via 3D printing. Ultimately, this work shows 3D printing offers considerable opportunity to integrate supporting features, and potentially even novel electronic materials, into nerve cuffs that can support the design and engineering of next generation neural interfaces.
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41

Lago, Nicolò, and Andrea Cester. "Flexible and Organic Neural Interfaces: A Review." Applied Sciences 7, no. 12 (December 12, 2017): 1292. http://dx.doi.org/10.3390/app7121292.

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42

Dong, Li. "Learning natural language interfaces with neural models." AI Matters 7, no. 2 (June 2021): 14–17. http://dx.doi.org/10.1145/3478369.3478375.

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Language is the primary and most natural means of communication for humans. The learning curve of interacting with various services (e.g., digital assistants, and smart appliances) would be greatly reduced if we could talk to machines using human language. However, in most cases computers can only interpret and execute formal languages.
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43

Wunderlich, Hannah, and Kristen L. Kozielski. "Next generation material interfaces for neural engineering." Current Opinion in Biotechnology 72 (December 2021): 29–38. http://dx.doi.org/10.1016/j.copbio.2021.09.005.

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44

Frank, James A. "Optofluidic neural interfaces for in vivo photopharmacology." Current Opinion in Pharmacology 63 (April 2022): 102195. http://dx.doi.org/10.1016/j.coph.2022.102195.

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45

Kim, Geon, Kanghyun Kim, Eunji Lee, Taechang An, WooSeok Choi, Geunbae Lim, and Jung Shin. "Recent Progress on Microelectrodes in Neural Interfaces." Materials 11, no. 10 (October 16, 2018): 1995. http://dx.doi.org/10.3390/ma11101995.

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Brain‒machine interface (BMI) is a promising technology that looks set to contribute to the development of artificial limbs and new input devices by integrating various recent technological advances, including neural electrodes, wireless communication, signal analysis, and robot control. Neural electrodes are a key technological component of BMI, as they can record the rapid and numerous signals emitted by neurons. To receive stable, consistent, and accurate signals, electrodes are designed in accordance with various templates using diverse materials. With the development of microelectromechanical systems (MEMS) technology, electrodes have become more integrated, and their performance has gradually evolved through surface modification and advances in biotechnology. In this paper, we review the development of the extracellular/intracellular type of in vitro microelectrode array (MEA) to investigate neural interface technology and the penetrating/surface (non-penetrating) type of in vivo electrodes. We briefly examine the history and study the recently developed shapes and various uses of the electrode. Also, electrode materials and surface modification techniques are reviewed to measure high-quality neural signals that can be used in BMI.
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46

Jackson, Andrew, and Thomas M. Hall. "Decoding Local Field Potentials for Neural Interfaces." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 10 (October 2017): 1705–14. http://dx.doi.org/10.1109/tnsre.2016.2612001.

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47

Acarón Ledesma, Héctor, Xiaojian Li, João L. Carvalho-de-Souza, Wei Wei, Francisco Bezanilla, and Bozhi Tian. "An atlas of nano-enabled neural interfaces." Nature Nanotechnology 14, no. 7 (July 2019): 645–57. http://dx.doi.org/10.1038/s41565-019-0487-x.

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48

Grill, Warren M., Sharon E. Norman, and Ravi V. Bellamkonda. "Implanted Neural Interfaces: Biochallenges and Engineered Solutions." Annual Review of Biomedical Engineering 11, no. 1 (August 2009): 1–24. http://dx.doi.org/10.1146/annurev-bioeng-061008-124927.

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49

Frank, James A., Marc-Joseph Antonini, and Polina Anikeeva. "Next-generation interfaces for studying neural function." Nature Biotechnology 37, no. 9 (August 12, 2019): 1013–23. http://dx.doi.org/10.1038/s41587-019-0198-8.

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50

Park, Seongjun, Gabriel Loke, Yoel Fink, and Polina Anikeeva. "Flexible fiber-based optoelectronics for neural interfaces." Chemical Society Reviews 48, no. 6 (2019): 1826–52. http://dx.doi.org/10.1039/c8cs00710a.

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