Conferences related to Neural Engineering

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.


2020 IEEE Global Engineering Education Conference (EDUCON)

The IEEE Global Engineering Education Conference (EDUCON) 2020 is the eleventh in a series of conferences that rotate among central locations in IEEE Region 8 (Europe, Middle East and North Africa). EDUCON is one of the flagship conferences of the IEEE Education Society. It seeks to foster the area of Engineering Education under the leadership of the IEEE Education Society.


2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

All topics related to engineering and technology management, including applicable analytical methods and economical/social/human issues to be considered in making engineering decisions.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


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Periodicals related to Neural Engineering

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Dielectrics and Electrical Insulation, IEEE Transactions on

Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.


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Most published Xplore authors for Neural Engineering

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Xplore Articles related to Neural Engineering

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Big data resources for EEGs: Enabling deep learning research

2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2017

The Temple University Hospital (TUH) electroencephalography (EEG) Corpus is the world's largest open source EEG corpus of its kind [1]. This corpus consists of over 25,000 EEG studies and over 14,000 patients, and includes a neurologist's interpretation of the test, a brief medical history of the patient, and demographic information about the patients such as gender and age. This database ...


Concurrent Optical Imaging and Computational Modeling: A Path to Reverse Neural Engineering

2007 3rd International IEEE/EMBS Conference on Neural Engineering, 2007

None


NeuroTalktrade: an interface for multifunctional neural engineering ASICs

2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

With the availability of modern application specific integrated circuit (ASIC) design tools, simulation packages, and low-cost commercial silicon foundry processes, it is becoming increasingly easy for any laboratory, or small company, to develop a custom ASIC. For stimulation, as well as recording, chips that perform specialized functions can be designed, fabricated, and tested within a time period of 2-3 months. ...


Neural engineering utility with adaptive algorithms

Proceedings of AUTOTESTCON '94, 1994

The neural engineering utility with adaptive algorithms (NEUWAA) is a machine- based intelligence system for automatic test equipment, which integrates various technologies in an adaptive fault-detection environment. Computer enhancements and mathematical algorithms allow for the use of man-machine and intelligent applications. The human/machine interface optimizes the test environment by providing state-of-the-art adaptive algorithms to streamline test sequences and diagnostics. NEUWAA ...


Neural engineering in management application

1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929), 1996

Describes the concept of neural engineering which supports decision-making in the systematic construction of neural networks. We gave some guidelines on building neural networks tailored to a specific applications, such as: what is the optimal number of hidden nodes, how to prune the input nodes, segregation and transformation of data etc. Experiments were carried on cost estimation using a multi-layer ...


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Educational Resources on Neural Engineering

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IEEE-USA E-Books

  • Big data resources for EEGs: Enabling deep learning research

    The Temple University Hospital (TUH) electroencephalography (EEG) Corpus is the world's largest open source EEG corpus of its kind [1]. This corpus consists of over 25,000 EEG studies and over 14,000 patients, and includes a neurologist's interpretation of the test, a brief medical history of the patient, and demographic information about the patients such as gender and age. This database represents the efforts of the Department of Neurology and the Neural Engineering Data Consortium to support the use of EEG data in machine learning research. The data was collected in normal clinical settings and hence includes many non-epileptic features such as muscle and movement artifacts, and a variety of channel configurations that cannot be found in currently available, more sanitized datasets. This is the first dataset of its kind to contain a sufficient amount of EEG data to support the application of state of the art deep learning algorithms. The most recent release of this corpus is vl.0.0 which includes 13,550 patients, 23,218 EEG sessions with reports and 61,634 EEG files.

  • Concurrent Optical Imaging and Computational Modeling: A Path to Reverse Neural Engineering

    None

  • NeuroTalktrade: an interface for multifunctional neural engineering ASICs

    With the availability of modern application specific integrated circuit (ASIC) design tools, simulation packages, and low-cost commercial silicon foundry processes, it is becoming increasingly easy for any laboratory, or small company, to develop a custom ASIC. For stimulation, as well as recording, chips that perform specialized functions can be designed, fabricated, and tested within a time period of 2-3 months. In many cases, the desired functionality can only be obtained by using VLSI design methods. Despite this increase in ASIC functionality, as related to neural engineering applications, there exists no common interface protocol for communicating with, and controlling, neural engineering ASICs. This would be analogous to each company that manufactures PC-based systems to have no common method of communication, e.g. USB, GBIB, RS-232, etc. While it might seem elusive, we propose the specification and development of a universal interface protocol for neural engineering ASICs. We have named this interface, NeuroTalktrade.

  • Neural engineering utility with adaptive algorithms

    The neural engineering utility with adaptive algorithms (NEUWAA) is a machine- based intelligence system for automatic test equipment, which integrates various technologies in an adaptive fault-detection environment. Computer enhancements and mathematical algorithms allow for the use of man-machine and intelligent applications. The human/machine interface optimizes the test environment by providing state-of-the-art adaptive algorithms to streamline test sequences and diagnostics. NEUWAA employs state-of-the-art diagnostics methodologies coupled with self-organizing evolution to provide an efficient test environment at any level. Highlighted visual images with dialogue of the unit under test provide interactive fault-isolation including guided-probe sequences to streamline fault/diagnosis. The system is completely interoperable with all other standard software packages. This paper outlines the neural engineering utility with adaptive algorithms system including its various characteristics and the techniques involved in its creation.<<ETX>>

  • Neural engineering in management application

    Describes the concept of neural engineering which supports decision-making in the systematic construction of neural networks. We gave some guidelines on building neural networks tailored to a specific applications, such as: what is the optimal number of hidden nodes, how to prune the input nodes, segregation and transformation of data etc. Experiments were carried on cost estimation using a multi-layer perceptron (backpropagation algorithm).

  • The Neural Engineering of ATE

    This paper describes a neural network-based software configuration implemented on a VXI automatic test equipment platform. The purpose of this unique software configuration, incorporating neural network and other artificial intelligence (AI) technologies, is to enhance ATE capability end efficiency by providing an intelligent interface for a variety of functions that are controlled or monitored by the software. This includes automated end user- directed control of the ATE end diagnostic strategy to streamline test sequences through the use of advanced diagnostic strategies. The use of Neural Engineering techniques are stressed which, in this context, foster the integration of diverse sensor technology capable of analyzing units under test (UUT) from different perspectives that provide new insight into static, dynamic, and historical UUT performance. Such methods can achieve greater accuracy in failure diagnosis and fault prediction; reduction in cannot duplicate (CND), retest-OK (RTOK) rates, and ambiguity group size; and improved confidence in performance testing that results in the determination of UUT ready for issue status. The hardware configuration of the ATE consists of an embedded 486 100 MHz PC controller and an instrument suite as follows: Power Supply, DMM, Digitizer, Counter/Timer, Digital I/O, Pulse Generator, Switching Matrix, Relay, Function Generator and Arbitrary Function Generator.

  • A simple generative model applied to motor-imagery brain-computer interfacing

    In this study, a generative model is developed in order to translate neural activity into predictable device commands for brain-computer interface (BCI) applications. Generative approaches to BCI translation differ from widely-used discriminative approaches because they develop a model of brain activity dependent on the mental state of the user. Preliminary results indicate that two of three subjects were able to control the system at a level (&gt;;70% accurate) that makes it a viable option for practical use. The accuracy rate of the generative model is compared to the accuracy rate calculated offline using a linear discriminant approach. The advantages of such a system are discussed, and the ongoing opportunities for paradigm improvement are outlined.

  • The Temple University Hospital EEG corpus

    The recently established Neural Engineering Data Consortium (NEDC) is in the process of developing its first large-scale corpus. This corpus, known as the Temple University Hospital EEG Corpus, upon completion, will total over 20,000 EEG studies, and include patient information, medical histories and physician assessments, making it the largest and most comprehensive publicly released EEG corpus. For the first time, there will be sufficient data to support the application of state of the art machine learning algorithms. In this paper, we present pilot results of experiments in which we attempted to predict some basic attributes of an EEG from the raw EEG data using a pilot database of 100 EEGs. Standard machine learning approaches are shown to be capable of predicting commonly occurring events from simple features with high accuracy on closed-loop testing, and can deliver error rates slightly below 50% on a 12-way open set classification problem.

  • An overview of neural engineering

    A comprehensive definition of Neural Engineering is presented from scientific, technological, clinical and end-user perspectives. Neural Engineering is the synergistic and highly interdisciplinary marriage of the neuroscience disciplines and those of engineering and computer science. It seeks to tap directly or indirectly into the nervous system to obtain sensory or command and control signals, to activate outgoing neural signals, and/or to influence processing within the central nervous system. Neural Engineering also seeks methods to restore lost or compromised neurological function. Neural Engineering is involved in designing, analyzing, and testing functional interfaces between neuroprosthetic systems and neurobiological systems. Neural Engineering also tests all of these components as systems, both in an engineering sense and in a physiological sense. Neural Engineering's design goals, achievable through rigorous in vitro, in vivo and clinical research, advance the understanding of sensorimotor neuroscience; and produce neural prostheses that are reliable, robust, safe, functionally transparent and cosmetically acceptable.

  • Open Source Resources to Advance EEG Research

    Since 2012, the Neural Engineering Data Consortium (NEDC) at Temple University has been providing many key data resources to support machine learning research in bioengineering [1]. In this poster, we present an update on several significant resources available from NEDC that were established to support a new generation of electroencephalogram (EEG) technology development.



Standards related to Neural Engineering

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No standards are currently tagged "Neural Engineering"