Academic literature on the topic 'Neural interfaces'
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Journal articles on the topic "Neural interfaces"
Grill, Warren. "Neural Interfaces." American Scientist 98, no. 1 (2010): 48. http://dx.doi.org/10.1511/2010.82.48.
Full textWarden, 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.
Full textZhang, 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.
Full textZhang, 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.
Full textAhmed, 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.
Full textKuncel, 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.
Full textBellamkonda, 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.
Full textSheng, 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.
Full textWang, 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.
Full textWang, 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.
Full textDissertations / Theses on the topic "Neural interfaces"
Minev, Ivan Rusev. "Soft neural interfaces." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610257.
Full textPark, Seongjun. "Multifunctional fiber-based neural interfaces." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118086.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 161-174).
Neurological disorders affect up to a billion people worldwide, and their socioeconomic burden is projected to increase as the population ages. However, our ability to understand and to treat neural disorders is currently limited by the lack of tools capable of interfacing with the brain over extended periods of time. This is hypothesized to stem from the mismatch in mechanical and chemical properties between the neural probes and the neural tissues, which leads to foreign body response and functional device failure due to tissue scarring in the probe vicinity. To address the challenge, I developed fiber-based bioelectronic devices integrating diverse modalities within a single platform using thermal drawing process (TDP). All-polymer or hydrogel integrated probes with optical, electrical, and fluidic capabilities were developed all within the 100-200 [mu]m diameter, which allowed one-step surgery to the mouse brain and spinal cord for optogenetic experiments. This probe also addressed the challenge of biocompatibility and enabled the recording isolated action potentials for 3 months. In addition, I applied TPD to produce biocompatible polymer-based neural scaffold with various geometries (round, rectangular, micro-grooved) and dimensions between 50-200 [mu]m. This allowed for investigation of the enhancement of neurite growth as a function of fiber parameters. We found that the topographical features and the narrow channels generally led to enhanced growth. This thesis illustrated a variety of applications of multifunctional fiber-based devices in neuroscience and neural engineering, which anticipated to enable basic studies of the nervous system and future treatment of neurological disorders.
by Seongjun Park.
Ph. D.
Garcia, Cortadella Ramon. "High-Bandwidth Graphene Neural Interfaces." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673787.
Full textEl funcionamiento del cerebro se basa en procesos complejos, que aún no se han descrito y comprendido detalladamente. En las últimas décadas, la neurociencia ha experimentado un desarrollo acelerado, impulsado por nuevas neurotecnologías que permiten monitorear la dinámica de la actividad eléctrica en el cerebro con una mayor resolución espacio-temporal y un área de cobertura más amplia. Sin embargo, debido a la alta complejidad de las redes neuronales en el cerebro, que están compuestas por poblaciones neuronales fuertemente interconectadas en amplias regiones cerebrales, estamos lejos de monitorear una fracción significativa de neuronas que dan lugar a funciones complejas. Con el fin de investigar las dinámica neuronales a gran escala con alta resolución espacial, se han utilizado diversas tecnologías, que incluyen la resonancia magnética funcional (fMRI), imágenes con marcadores sensibles al voltaje o registros electrofisiológicos de alto conteo de sensores. Sin embargo, la resolución temporal del fMRI y los métodos ópticos se limita típicamente a unos pocos hercios, casi tres órdenes de magnitud por debajo de la de los potenciales de acción, y se limitan a condiciones en los que el sujeto se encuentra inmóvil. Por otro lado, los registros electrofisiológicos basados en matrices de microelectrodos proporcionan una alta resolución espacio-temporal, lo que permite detectar con precisión dinámicas rápidas de cientos de neuronas individuales simultáneamente en animales que se mueven libremente. Sin embargo, las interfaces de detección neuroelectrónica presentan una limitación en el producto entre la resolución espacial y el área de cobertura. Además, presentan una baja sensibilidad en la banda de frecuencia infra-lenta (<0.5Hz), que está relacionada con la conectividad funcional de largo alcance. En esta tesis se presenta una nueva tecnología basada en sensores activos de grafeno, que permite incrementar el área de cobertura y la resolución espacial de los registros electrofisiológicos conservando una alta sensibilidad en una amplia banda de frecuencia, desde la actividad infra-lenta hasta la de una sola célula electrogénica. Este desarrollo tecnológico se divide en tres etapas principales; en primer lugar, se obtiene una comprensión más profunda de las características intrínsecas del ruido y la respuesta en frecuencia de estos sensores basándose en el estado del arte en tecnología de sensores de grafeno. En la segunda etapa, se muestra un sistema cuasi-comercial basado en matrices de sensores de grafeno epi-cortical y transmisión inalámbrica para implantación crónica en ratas. Con este sistema, se demuestra la reproducibilidad de las matrices de sensores de grafeno, su estabilidad a largo plazo y su biocompatibilidad crónica. Además, se proporciona evidencia preliminar para una amplia gama de nuevos patrones electrofisiológicos debido a su sensibilidad en la banda de frecuencia infra-lenta. Finalmente, en la última etapa de esta tesis, el enfoque se centra en el desarrollo de nuevas estrategias de multiplexación para aumentar el número de sensores en las sondas neuronales. Estas tres etapas principales de desarrollo han llevado a la demostración del potencial de las matrices de sensores de grafeno multiplexados para el mapeado de las dinámicas neuronales a gran escala en una amplia banda de frecuencia en animales que se mueven libremente durante largos períodos. La combinación de estas capacidades hace que las matrices de sensores activos de grafeno sean una tecnología prometedora para interfaces cerebro-ordenador de alto ancho de banda y una herramienta única para investigar el papel de la actividad infra-lenta en la coordinación de las dinámicas neuronales de alta frecuencia.
Brain function is based on highly complex processes, which remain yet to be described and understood in detail. In the last decades, neuroscience has experienced an accelerated development, prompted by novel neurotechnologies that allow monitoring the dynamics of electrical activity in the brain with a higher spatio-temporal resolution and wider coverage area. However, due to the high complexity of neural networks in the brain, which are composed of strongly interconnected neural populations across large brain regions, we are far from monitoring a significant fraction of neurons mediating complex functions. In order to investigate large-scale brain dynamics with high spatial resolution several technologies have been extensively used, including functional magnetic resonance imaging (fMRI), voltage-sensitive dye imaging or high sensor-count electrophysiological recordings. However, the temporal resolution of fMRI and optical methods is typically limited to few hertz, almost three orders of magnitude below that of action potentials, and are limited to head-fixed conditions. On the other hand, electrophysiological recordings based on micro-electrode arrays provide a high spatio-temporal resolution, allowing to accurately detect fast dynamics from hundreds of individual neurons simultaneously in freely moving animals. However, neuroelectronic sensing interfaces present a trade-off between spatial resolution and coverage area. Moreover, they present a poor sensitivity in the infra-slow frequency band ($<0.5$\,$Hz$), which is related to long-range functional connectivity. In this thesis, a novel technology based on graphene active sensors is presented, which allows to increase the coverage area and spatial resolution of electrophysiological recordings while preserving a high sensitivity in a wide frequency band, from infra-slow to single electrogenic cell activity. This technological development is divided into three main stages; first, a deeper understanding of the intrinsic noise characteristics and frequency response of these sensors is obtained by building on prior graphene sensor technology. In the second stage, a quasi-commercial system based on epi-cortical graphene sensor arrays and a wireless headstage for chronic implantation in rats is shown. Using this system, the reproducibility of the graphene sensor arrays, their long-term stability and their chronic biocompatibility are demonstrated. Furthermore, preliminary evidence is provided for a wide range of novel electrophysiological patterns owing to their sensitivity in the infra-slow frequency band. Finally, in the last stage of this thesis, the focus is centred on the development of new multiplexing strategies to upscale the number of sensors on the neural probes. These three main development stages have led to the demonstration of the potential of multiplexed graphene sensor arrays for mapping of large-scale brain dynamics in a wide frequency band in freely moving animals over long periods. The combination of these capabilities makes graphene active sensor arrays a promising technology for high bandwidth brain computer interfaces and a unique tool to investigate the role of infra-slow activity on the coordination of higher frequency brain dynamics.
Universitat Autònoma de Barcelona. Programa de Doctorat en Enginyeria Electrònica i de Telecomunicació
Barrett, Richard. "Novel processing routes for neural interfaces." Thesis, University of Birmingham, 2014. http://etheses.bham.ac.uk//id/eprint/5137/.
Full textWatterson, William James. "Fractal Interfaces for Stimulating and Recording Neural Implants." Thesis, University of Oregon, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10636408.
Full textFrom investigating movement in an insect to deciphering cognition in a human brain to treating Parkinson's disease, hearing loss, or even blindness, electronic implants are an essential tool for understanding the brain and treating neural diseases. Currently, the stimulating and recording resolution of these implants remains low. For instance, they can record all the neuron activity associated with movement in an insect, but are quite far from recording, at an individual neuron resolution, the large volumes of brain tissue associated with cognition. Likewise, there is remarkable success in the cochlear implant restoring hearing due to the relatively simple anatomy of the auditory nerves, but are failing to restore vision to the blind due to poor signal fidelity and transmission in stimulating the more complex anatomy of the visual nerves. The critically important research needed to improve the resolution of these implants is to optimize the neuron-electrode interface. This thesis explores geometrical and material modifications to both stimulating and recording electrodes which can improve the neuron-electrode interface. First, we introduce a fractal electrode geometry which radically improves the restored visual acuity achieved by retinal implants and leads to safe, long-term operation of the implant. Next, we demonstrate excellent neuron survival and neurite outgrowth on carbon nanotube electrodes, thus providing a safe biomaterial which forms a strong connection between the electrode and neurons. Additional preliminary evidence suggests carbon nanotubes patterned into a fractal geometry will provide further benefits in improving the electrode-neuron interface. Finally, we propose a novel implant based off field effect transistor technology which utilizes an interconnecting fractal network of semiconducting carbon nanotubes to record from thousands of neurons simutaneously at an individual neuron resolution. Taken together, these improvements have the potential to radically improve our understanding of the brain and our ability to restore function to patients of neural diseases.
Tringides, Christina M. (Christina Myra). "Materials selection and processing for reliable neural interfaces." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98667.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 48-50).
The understanding of the brain would be revolutionized by a tool that can measure intra- and extra-cellular electrical potentials on a parallelized scale, without disrupting the neural physiology. Existing technologies do not sufficiently carry out these functions. Using a thermal drawing process (TDP), multimaterial fibers comprised of polymer-metal composites can be fabricated to create flexible, microelectrode arrays. These fibers can be further processed after the TDP, using selective etching to reduce the diameter of the probe. These devices have been implanted and have been used to record neural activity in vivo while evoking minimal tissue response. Additionally, electrodeposition of biocompatible metals onto the fiber-electrode tips can be implemented to increase the signal-to-noise ratio (SNR). Here, I describe the electroplating of gold onto the fiber-tips of tin and tin-indium electrodes, which were drawn using TDP. By adjusting the electrodeposition conditions, the electrode tip geometries can be tuned to optimize the interface between the device tips and neuronal membranes.
by Christina M. Tringides.
S.B.
Watterson, William. "Fractal Interfaces for Stimulating and Recording Neural Implants." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23169.
Full textBonaccini, Calia Andrea. "Graphene field-effect transistors as flexible neural interfaces for intracortical electrophysiology." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671635.
Full textEn los últimos años se han producido nuevos desarrollos tecnológicos en el campo de los implantes neuronales para aplicaciones médicas. La comprensión del cerebro humano se considera uno de los mayores desafíos científicos de nuestro tiempo; como consecuencia, estamos siendo testigos de una intensificación de la investigación en el desarrollo de las interfaces cerebro-máquina (IMC) para leer y estimular la actividad cerebral. No obstante, los implantes neuronales actualmente disponibles ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantea la invasividad de los materiales. Esos materiales comprometen la resolución de la interfaz, el rendimiento y la estabilidad a largo plazo de los implantes neurales. El desarrollo de una electrónica flexible que utilice materiales biocompatibles es clave para la realización de implantes neuronales mínimamente invasivos que puedan implantarse de forma crónica. Un campo de investigación muy prometedor es el uso de materiales bidimensionales, como el grafeno, para aplicaciones bioelectrónicas. El transistor de efecto de campo en solución de grafeno (gSGFET) es una de dichas nuevas tecnologías neurales emergentes. Estos dispositivos pueden superar las limitaciones mencionadas anteriormente gracias a las extraordinarias propiedades del grafeno, como su alta flexibilidad mecánica, estabilidad electroquímica, biocompatibilidad y sensibilidad. En esta tesis doctoral, se han fabricado matrices de gSGFET y se han optimizado iterativamente en términos de sensibilidad y relación señal/ruido, adoptando métodos de microfabricación a escala de oblea. Se ha caracterizado el ruido 1/f en los gSGFETs y optimizado haciendo un tratamiento UVO en la interfaz metal/grafeno y desacoplando el canal de grafeno del sustrato utilizando diferentes nanomateriales como la encapsulación con nitruro de boro hexagonal (hBN), monocapas autoensambladas y bicapas de grafeno. Además, se han fabricado con éxito sondas neurales epicorticales e intracorticales flexibles con matrices de gSGFET y se han utilizado durante las medidas de microelectrocorticografía in vivo en roedores. Se han insertado dispositivos intracorticales flexibles en el cerebro utilizando un protocolo de refuerzo de la capa posterior de los dispositivos con proteína de fibroína de seda biorresistente. Los resultados presentados en esta tesis demuestran la superior resolución espacio-temporal de los gSGFET en comparación con la tecnología estándar de microelectrodos; en particular, referente a la capacidad de mapear con alta fidelidad, la actividad de muy baja frecuencia (ISA, < 0,1 Hz) junto con las señales en el típico ancho de banda LFP. Hoy en día se sabe que la actividad cerebral de muy baja frecuencia, contribuye a la fisiopatología de varios trastornos neurológicos como el derrame cerebral, la lesión cerebral traumática, la migraña y la epilepsia. Sin embargo, esta actividad rara vez se registra debido a las limitaciones técnicas intrínsecas de los electrodos convencionales acoplados a la CA. Se han obtenido registros con sondas neuronales de profundidad de grafeno (gDNP) en modelos animales de epilepsia. Se detectó ISA a través de diferentes capas corticales y regiones subcorticales, registrando simultáneamente la actividad epiléptica en bandas de frecuencia más convencionales (1-600Hz). Además, se ha demostrado también la evaluación de la estabilidad y funcionalidad en registros crónicos, así como la biocompatibilidad del gDNP. La tecnología bioelectrónica basada en el grafeno aquí descrita tiene el potencial de convertirse en una herramienta de referencia para la electrofisiología de ancho de banda completo. Se prevé que esta tecnología tenga un gran impacto en una comunidad amplia y multidisciplinaria que incluya investigadores en neurotecnología, ingenieros biomédicos, neurocientíficos que estudien la dinámica cortical de banda ancha asociada con el comportamiento espontáneo y/o los estados cerebrales, así como investigadores clínicos interesados en la actividad de baja frecuencia en la epilepsia, los accidentes cerebrovasculares y la migraña.
Recent years have witnessed novel technology developments of neural implants for medical applications which are expected to pave the way to unveil functionalities of the central nervous system. Understanding the human brain is commonly considered one of the biggest scientific challenges of our time; as a consequence, we are witnessing an intensified research in the development of brain-machine-interfaces (BMIs), which would allow us to both read and stimulate brain activity. Nevertheless, currently available neural implants offer a modest clinical efficacy, partly due to the limitations posed by the invasiveness of the implants materials and technology and by the metals used at the electrical interface with the tissue. Such materials compromise the interfacing resolution, the performance and the long term stability of neural implants. Development of flexible electronics using biocompatible materials is key for the realisation of minimally invasive neural implants, which can be chronically implanted without causing rejection from the immune system. A relatively young yet very promising research field, that is increasingly drawing attention is the use of two dimensional materials, such as graphene, for bioelectronic applications. Graphene solution-gated field effect transistor (gSGFET) is one of several emerging new neural technologies. These devices can overcome the above-mentioned limitations thanks to the outstanding properties of graphene, such as mechanical flexibility, electrochemical inertness, biocompatibility and high sensitivity. In this PhD thesis, arrays of gSGFETs have been fabricated and iteratively optimized in terms of sensitivity and signal-to-noise ratio, adopting wafer-scale micro-fabrication methods. The 1/f noise in gSGFETs has been characterised and the optimisation of both, contact and channel noises was achieved by UVO-treatment at the metal/graphene interface, as well as by decoupling the graphene channel from the substrate, using different nanomaterials such as graphene encapsulation with hexagonal boron nitride (hBN), self assembled monolayers and double transferred graphene. Moreover, flexible and ultra-thin epicortical and intracortical neural probes, containing arrays of gSGFETs, have been successfully fabricated and used during in vivo microelectrocorticography recordings in anaesthesized and awake rodents. Flexible intracortical devices were inserted into the brain using a back-coating stiffening protocol with bioresobable silk fibroin protein, developed during this PhD thesis. The results presented in this PhD demonstrate the superior spatio-temporal resolution of gSGFETs compared to standard microelectordes technology; particularly the ability to map with high fidelity, infraslow activity (ISA, < 0.1 Hz) together with signals in the typical local field potential bandwidth. Today it is known that infraslow brain activity, including spreading depolarisations, contribute to the pathophysiology of several neurological disorders such as stroke, traumatic brain injury, migraine and epilepsy. However, this activity is seldom recorded due to intrinsic technical limitations of conventional AC-coupled electrodes. To demonstrate the usefulness of the developed flexible gSGFET arrays technology, recordings have been obtained with multichannel flexible graphene depth neural probes (gDNP) in relevant awake animal models of seizures and established epilepsy. ISA was detected and mapped through different cortical layers and subcortical regions, whilst simultaneously recording epileptiform activity in more conventional frequency bands (1-600Hz). Furthermore, the assessment of the long term recording stability and functionality, as well as biocompatibility of the gDNP has also been demonstrated as part of this thesis. The graphene based bioelectronic technology here described has the potential to become a gold standard tool for full bandwidth electrophysiology. This technology is envisioned to have a great impact on a broad and multidisciplinary community including neurotechnology researchers, biomedical engineers, neuroscientists studying wide-band cortical dynamics associated with spontaneous behaviour and/or brain states, as well as clinical researchers interested in the role of infraslow activity in epilepsy, stroke and migraine.
Richards, Stephen M. "End-user interfaces to electronic books." Thesis, Teesside University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358404.
Full textValdar, William Seth Jermy. "Residue conservation in the prediction of protein-protein interfaces." Thesis, University College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246927.
Full textBooks on the topic "Neural interfaces"
I, Bey, ed. Neutral interfaces in design, simulation, and programming for robotics. Berlin: Springer-Verlag, 1994.
Find full textHolleman, Jeremy, Fan Zhang, and Brian Otis. Ultra Low-Power Integrated Circuit Design for Wireless Neural Interfaces. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6727-5.
Full textFan, Zhang, Otis Brian, and SpringerLink (Online service), eds. Ultra Low-Power Integrated Circuit Design for Wireless Neural Interfaces. New York, NY: Springer Science+Business Media, LLC, 2011.
Find full textFels, S. Sidney. Building adaptive interfaces with neural networks: The Glove-Talk pilot study. Toronto: University of Toronto, Dept. of Computer Science, 1990.
Find full textRan, Ginosar, and SpringerLink (Online service), eds. The NeuroProcessor: An Integrated Interface to Biological Neural Networks. Dordrecht: Springer Science+Business Media B.V., 2008.
Find full textCoates, Thomas D. Neural interfacing: Forging the human-machine connection. San Rafael, Calif. (1537 Fourth St, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2008.
Find full textTaylor, Cynthia E. Documentation of TSMC software that interfaces with traffic analysis problems. [Olympia, Wash.]: Washington State Dept. of Transportation, 1997.
Find full textBrain machine interfaces: Implications for science, clinical practice and society. Amsterdam: Elsevier, 2011.
Find full textVasquez, Daniel. Hierarchical Neural Network Structures for Phoneme Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Find full textFels, S. Sidney. Glove-Talk II: Mapping hard gestures to speech using neural networks : an approach to building adaptive interfaces. Toronto: University of Toronto, Dept. of Computer Science, 1994.
Find full textBook chapters on the topic "Neural interfaces"
Yoda, Minami, Jean-Luc Garden, Olivier Bourgeois, Aeraj Haque, Aloke Kumar, Hans Deyhle, Simone Hieber, et al. "Neural Interfaces." In Encyclopedia of Nanotechnology, 1895. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-90-481-9751-4_100593.
Full textHe, Bin, Han Yuan, Jianjun Meng, and Shangkai Gao. "Brain–Computer Interfaces." In Neural Engineering, 131–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43395-6_4.
Full textHe, Bin, Shangkai Gao, Han Yuan, and Jonathan R. Wolpaw. "Brain–Computer Interfaces." In Neural Engineering, 87–151. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5227-0_2.
Full textKang, Woo Hyeun, Wenzhe Cao, Sigurd Wagner, and Barclay Morrison. "Stretchable Neural Interfaces." In Stretchable Electronics, 379–99. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527646982.ch16.
Full textMoore Jackson, Melody, and Rudolph Mappus. "Neural Control Interfaces." In Brain-Computer Interfaces, 21–33. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-272-8_2.
Full textLebedev, Mikhail A., and Alexei Ossadtchi. "Bidirectional Neural Interfaces." In Brain–Computer Interfaces Handbook, 701–20. Boca Raton : Taylor & Francis, CRC Press, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351231954-37.
Full textBroccard, Frédéric D., Siddharth Joshi, Jun Wang, and Gert Cauwenberghs. "Neuromorphic Neural Interfaces." In Handbook of Neuroengineering, 1–33. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-15-2848-4_41-1.
Full textOby, Emily R., Jay A. Hennig, Aaron P. Batista, Byron M. Yu, and Steven M. Chase. "Intracortical Brain–Machine Interfaces." In Neural Engineering, 185–221. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43395-6_5.
Full textBrockmeier, Austin J., and José C. Príncipe. "Decoding Algorithms for Brain–Machine Interfaces." In Neural Engineering, 223–57. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5227-0_4.
Full textLaiwalla, Farah, and Arto Nurmikko. "Future of Neural Interfaces." In Advances in Experimental Medicine and Biology, 225–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2050-7_9.
Full textConference papers on the topic "Neural interfaces"
Балакин, Петр Владимирович, Светлана Анатольевна Микаева, and Юлия Алексеевна Журавлева. "NEURAL INTERFACES." In Высокие технологии и инновации в науке: сборник избранных статей Международной научной конференции (Санкт-Петербург, Май 2022). Crossref, 2022. http://dx.doi.org/10.37539/vt197.2022.39.20.012.
Full textWalker, Ross M., Loren Rieth, Subramanian S. Iyer, Adeel A. Bajwa, Jason Silver, Taufiq Ahmed, Naila Tasneem, Mohit Sharma, and A. Tye Gardner. "Integrated neural interfaces." In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2017. http://dx.doi.org/10.1109/mwscas.2017.8053106.
Full textViventi, Jonathan. "Flexible electronics for neural interfaces." In Neural Interfaces and Artificial Senses. València: Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.002.
Full textRogers, John. "Soft, Biocompatible Optoelectronic Neural Interfaces." In Neural Interfaces and Artificial Senses. València: Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.004.
Full text"Session IV: Neural interfaces, neural-inspired architectures and resistive sensor interfaces." In 2015 6th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI). IEEE, 2015. http://dx.doi.org/10.1109/iwasi.2015.7184999.
Full text"Session I: Neural interfaces." In 2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI). IEEE, 2017. http://dx.doi.org/10.1109/iwasi.2017.7974199.
Full textHarrison, Reid. "F8: Integrated neural interfaces." In 2009 IEEE International Solid-State Circuits Conference (ISSCC 2009). IEEE, 2009. http://dx.doi.org/10.1109/isscc.2009.4977536.
Full textPerez Fornos, Angelica, Nils Guinand, Raymond Van de Berg, Maurizio Ranieri, Samuel Cavuscens, Anissa Boutabla, Julie Corre, and Herman Kingma. "Vestibular Implants in Humans: Steps Towards a Clinical Application." In Neural Interfaces and Artificial Senses. València: Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.001.
Full textZhao, Zifang, Claudia Cea, Jennifer Gelinas, and Dion Khodagholy. "Ions-based high bandwidth communication for implantable bioelectronics." In Neural Interfaces and Artificial Senses. València: Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.010.
Full textSeo, DJ. "Minimally invasive brain-machine interface at Neuralink." In Neural Interfaces and Artificial Senses. València: Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.021.
Full textReports on the topic "Neural interfaces"
Kipke, Daryl R., Jeffrey Carrier, and David J. Anderson. Implantable Neural Interfaces for Sharks. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada470127.
Full textShea, Thomas B. Optimization of Neuronal-Computer Interface. Fort Belvoir, VA: Defense Technical Information Center, June 2009. http://dx.doi.org/10.21236/ada515409.
Full textWeber, Douglas J. A New Animal Model for Developing a Somatosensory Neural Interface for Prosthetic Limbs. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada482995.
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