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Transactions of Japanese Society for Medical and Biological engineering 2012年第1期50卷 162-167页

作者： Tsuru, Koji Pfurtscheller, Gert Department of Computer and Control Engineering Oita National College of Technology Japan Laboratory of Brain-Computer Interfaces Institute for Knowledge Discovery Graz University of Technology Austria

Biometric personal authentication is an emerging technology in information security. One resulting problem has been a recent increase in fraud based on falsified biometric data concerning biological information. brainwave-based identification is a promising biometric tool to prevent impostor attacks. Many researchers have reported biometric results using electroencephalogram (EEG) activity. The brainwave features of each individual are unique and have the potential for use in biometric authentication. Security can be enhanced by employing as many EEG features of an individual as possible. Although it takes time to measure brain waves at present, this authentication method can potentially be used for special security areas in the future. There are several approaches to brainwave biometrics using cognitive processes. We investigated the motor imagery for movements of the left hand, right hand, tongue, and both feet for brainwave biometrics. The nature of brain signal analysis parallels voice signal analysis in some respects. Hence, we applied the cepstral analysis method, which is commonly used in speech recognition, for feature extraction for brainwave biometrics. In our results, we identified almost all nine of the subjects correctly. We tested the performance of our biometric system using the Mahalanobis distance as the threshold and estimated the equal error rate (EER) value to be 0.17.

关键词： Biometrics

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An on-line BCI system for hand movement control using real-time recurrent probabilistic neural network

An on-line BCI system for hand movement control using real-t...

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International IEEE/EMBS Conference on neural engineering, CNE

作者： Mohammad Ahmadi Abbas Erfanian Brain-Computer Interface Laboratory Neural Technology Research Center Department of Biomedical Engineering Iran University of Science and Technology Tehran Iran

This paper presents a new EEG-based brain-computer Interface (BCI) for on-line controlling the hand movement in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. For this purpose, two classifiers are designed. The first classifier which is based on the imagination of right-hand movement is for controlling the hand grasping, holding and opening. The second classifier, which is based on the imagination of left-hand movement is designed for error correction and activating the first classifier. One important issue in developing an on-line BCI is the robust and accurate classification of EEG signal which is characterized with a time-varying distribution. In this work, we present a real-time recurrent probabilistic neural network for classifying the EEG signals. The results show that the subjects were able to achieve an accuracy more than 80% during the first session of experiment without off-line training and 73%-91% during the last session using single-trial classification with no adaptation.

关键词： Control systems Real time systems neural networks Recurrent neural networks Biological neural networks Electroencephalography brain computer interfaces Virtual reality Grasping Error correction

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Conveying tactile feedback in sensorized hand neuroprostheses using a biofidelic model of mechanotransduction

Conveying tactile feedback in sensorized hand neuroprosthese...

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作者： Kim, Sung Soo Sripati, Arun P. Vogelstein, R. Jacob Armiger, Robert S. Russell, Alexander F. Bensmaia, Sliman J. Krieger Mind/Brain Institute Johns Hopkins University Baltimore MD 21218 United States Center for the Neural Basis of Cognition Carnegie Melon University Pittsburgh PA 15213 United States Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD 21218 United States Johns Hopkins Applied Physics Laboratory Laurel MD United States Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD 20723 United States Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 United States

One approach to conveying tactile feedback from sensorized neural prostheses is to characterize the neural signals that would normally be produced in an intact limb and reproduce them through electrical stimulation of the residual peripheral nerves. Toward this end, we have developed a model that accurately replicates the neural activity evoked by any dynamic stimulus in the three types of mechanoreceptive afferents that innervate the glabrous skin of the hand. The model takes as input the position of the stimulus as a function of time, along with its first (velocity), second (acceleration), and third (jerk) derivatives. This input is filtered and passed through an integrate-and-fire mechanism to generate a train of spikes as output. The major conclusion of this study is that the timing of individual spikes evoked in mechanoreceptive fibers innervating the hand can be accurately predicted by this model. We discuss how this model can be integrated in a sensorized prosthesis and show that the activity in a population of simulated afferents conveys information about the location, timing, and magnitude of contact between the hand and an object. © 2009 IEEE.

关键词： Conveying

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Multimodal imaging of human brain activity: Rational, biophysical aspects and modes of integration

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Computational Intelligence and Neuroscience 2009年第1期2009卷 813607页

作者： Blinowska, Katarzyna Müller-Putz, Gernot Kaiser, Vera Astolfi, Laura Vanderperren, Katrien Van Huffel, Sabine Lemieux, Louis Department of Biomedical Physics Warsaw University 00-681 Warszawa Hoza 69 Poland Laboratory of Brain-Computer Interfaces Institute for Knowledge Discovery Graz University of Technology 8010 Graz Austria Department of Computer Science and Systems University of Rome Sapienza 00185 Rome Via Ariosto 25 Italy Fondazione S. Lucia 00179 Rome Via Ardeatina 306 Italy Research Division SCD Department of Electrical Engineering Katholieke Universiteit Leuven 3001 Leuven Belgium Department of Clinical and Experimental Epilepsy UCL Institute of Neurology London WC1N 3BG Queen Square United Kingdom

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship. Copyright © 2009 Katarzyna Blinowska et al.

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Interplay between a phase response curve and spike-timing-dependent plasticity leading to wireless clustering

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Physical Review E 2008年第5期77卷 051909-051909页

作者： Hideyuki Câteau Katsunori Kitano Tomoki Fukai Laboratory for Neural Circut Theory RIKEN Brain Science Institute 2–1 Hirowasa Wako Saitama 351-0198 Japan Department of Human and Computer Intelligence Ritsumeikan University 1-1-1 Nojihigashi Kusatsu Shiga 525-8577 Japan Department of Complexity Science and Engineering University of Tokyo Kashiwa Chiba 277–8561 Japan

A phase response curve (PRC) characterizes the signal transduction between oscillators such as neurons on a fixed network in a minimal manner, while spike-timing-dependent plasiticity (STDP) characterizes the way of rewiring networks in an activity-dependent manner. This paper demonstrates that these two key properties both related to the interaction times of oscillators work synergetically to carve functionally useful circuits. STDP working on neurons that prefer asynchrony converts the initial asynchronous firing to clustered firing with synchrony within a cluster. They get synchronized within a cluster despite their preference to asynchrony because STDP selectively disrupts intracluster connections, which we call wireless clustering. Our PRC analysis reveals a triad mechanism: the network structure affects how the PRC is read out to determine the synchrony tendency, the synchrony tendency affects how the STDP works, and STDP affects the network structure, closing the loop.

关键词： Plasticity

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Relationship between synaptic and functional connections of a local cortical network model

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BMC Neurosci 2007年第SUPPL 2期8卷 P69-P69页

作者： Katsunori Kitano Kazuhiro Yamada Tomoki Fukai Department of Human and Computer Intelligence Ritsumeikan University Kusatsu Japan Graduate School of Science and Engineering Ritsumeikan University Kusatsu Japan Laboratory for Neural Circuit Theory RIKEN Brain Science Institute Wako Japan

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BCI Meeting 2005 - Workshop on technology: Hardware and software

BCI Meeting 2005 - Workshop on technology: Hardware and soft...

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作者： Cincotti, Febo Bianchi, Luigi Birch, Gary Guger, Christoph Mellinger, Jürgen Scherer, Reinhold Schmidt, Robert N. Suárez, Oscar Yáñez Schalk, Gerwin Fondazione Santa Lucia 00179 Rome Italy Department of Neuroscience Tor Vergata University 00133 Rome Italy Neil Squire Society Burnaby BC V5M 3Z3 Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver BC V6T 1Z4 Canada g.tec - Guger Technologies OEG 8020 Graz Austria Institute of Medical Psychology University of Tübingen D-72074 Tübingen Germany Laboratory of Brain-Computer Interfaces Graz University of Technology A-8010 Graz Austria Cleveland Medical Devices Inc. Cleveland OH 44106 United States Neuroimaging Laboratory Universidad Autonoma Metropolitana México City DF 09340 Mexico Wadsworth Center New York State Department of Health Albany NY 12201 United States

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research. © 2006 IEEE.

关键词： Technical presentations

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THE COMMAND SUPPORT AT-SEA EXPERIMENT

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NAVAL ENGINEERS JOURNAL 1990年第3期102卷 25-36页

作者： BUSCHER, DJ SUNDAY, DM David J. Buscher:is an electrical engineer on the Principal Professional Staff of the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He earned his BS degree in electrical engineering from Villanova University in 1967 an MSEE degree from the University of Maryland in 1972 and an MS degree in technical management from The Johns Hopkins University in 1985. He was the lead engineer on the Command Support At-Sea Experiment Project. He started his career in 1967 with the U.S. Army Harry Diamond Laboratory working on computer and communications projects. During this time he worked in Vietnam with the United States Army Vietnam Science Advisor Office. Since joining JHU/APL in 1978 he has worked on a variety of projects associated with computer graphics hospital patient information systems computer networks autonomous underwater vehicles and satellite communications. He is currently assistant group supervisor of the Computer Systems Group. Daniel M. Sunday:is a mathematician on the Principal Professional Staff of the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He earned his Ph.D. in mathematics from the University of Minnesota in 1971 and did postdoctoral research on neural networks at the University of California at Berkeley. Since joining JHU/APL in 1980 Dr. Sunday has worked as a designer and developer of software systems with emphasis on medical imaging interactive graphics natural language interfaces and software standards. He was the network designer for the Headquarters Integrated Office System (HIOS) Network for the Army at the Pentagon. He is currently the lead software engineer on the Command Support At-Sea Experiment Project.

The Command Support At-Sea Experiment (CS@SE) provides experimental advanced graphics display systems consisting of large screen color displays and operator console color displays in the combat information center (CIC) of an Aegis cruiser and in the tactical flag command center (TFCC) of an aircraft carrier. CS@SE systems are designed to prototype potential command support capabilities in an at-sea environment to validate and refine requirements for planned production system upgrades. These systems use sophisticated color graphics techniques to provide real-time tactical displays that improve the availability of information to an operator by reducing clutter through the use of color, area fill, transparen overlays and intensity coding of track symbols. interfaces wen developed with the Aegis Display System (ADS), Shipboarc Gridlock System with Auto-correlation (SGS/AC), Flag Dats Display System (FDDS) and Tomahawk Engagement Planning and Exercise Evaluation System (TEPEE) that provided the data for the presentation of a tactical display. Display elements included both real-time and over-the-horizon (OTH) surface track data, velocity leaders, tags, uncertainty ellipses, and history trails. The display also included filled land masses, country boundaries, commercial airways, cities, graphics overlays (i.e., operational areas), weapon system missile performance contours, and engagement plans. This paper describes the experiment, its installation and integration into the shipboard environments of an Aegis cruiser (USS Leyte Gulf ) and an aircraft carrier (USS America , its usage by the ships companies and embarked staffs, and the experiment result! and findings. Key conclusions of the experiment are: 1 Advanced graphics color displays can significantly enhance the ability of the warfighter to assimilate a complex tactical display. 2 Both ships reported a requirement for a correlated OTH and real-time track display with the ability to clearly differentiate the two types

关键词： computer Graphics

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Computational Neuroscience: Mathematical and Statistical Perspectives

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Statistics and Its Application 1000年第1期5卷 183-214页

作者： Robert E. Kass Shun-Ichi Amari Kensuke Arai Emery N. Brown Casey O. Diekman Markus Diesmann Brent Doiron Uri T. Eden Adrienne L. Fairhall Grant M. Fiddyment Tomoki Fukai Sonja Grün Matthew T. Harrison Moritz Helias Hiroyuki Nakahara Jun-nosuke Teramae Peter J. Thomas Mark Reimers Jordan Rodu Horacio G. Rotstein Eric Shea-Brown Hideaki Shimazaki Shigeru Shinomoto Byron M. Yu Mark A. Kramer 1Department of Statistics Machine Learning Department and Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: kass@stat.cmu.edu 2Mathematical Neuroscience Laboratory RIKEN Brain Science Institute Wako Saitama Prefecture 351-0198 Japan 3Department of Mathematics and Statistics Boston University Boston Massachusetts 02215 USA 5Department of Anesthesia Harvard Medical School Boston Massachusetts 02115 USA 4Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 6Department of Mathematical Sciences New Jersey Institute of Technology Newark New Jersey 07102 USA 8Department of Theoretical Systems Neurobiology Institute of Biology RWTH Aachen University 52062 Aachen Germany 7Institute of Neuroscience and Medicine Jülich Research Centre 52428 Jülich Germany 9Department of Mathematics University of Pittsburgh Pittsburgh Pennsylvania 15260 USA 10Department of Physiology and Biophysics University of Washington Seattle Washington 98105 USA 11Division of Applied Mathematics Brown University Providence Rhode Island 02912 USA 12Department of Integrated Theoretical Neuroscience Osaka University Suita Osaka Prefecture 565-0871 Japan 13Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University Cleveland Ohio 44106 USA 14Department of Neuroscience Michigan State University East Lansing Michigan 48824 USA 15Department of Statistics University of Virginia Charlottesville Virginia 22904 USA 17Institute for Brain and Neuroscience Research New Jersey Institute of Technology Newark New Jersey 07102 USA 16Federated Department of Biological Sciences Rutgers University/New Jersey Institute of Technology Newark New Jersey 07102 USA 19Department of Physics Kyoto University Kyoto Kyoto Prefecture 606-8502 Japan 18Honda Research Institute Japan Wako Saitama Prefecture 351-0188 Japan 20Department of Electrical and Compute

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

关键词： neural data analysis neural modeling neural networks theoretical neuroscience

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