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Neural Systems and Rehabilitation Engineering, IEEE Transactions on

Issue 6 • Date Dec. 2008

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Displaying Results 1 - 16 of 16
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information

    Page(s): C2
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    Freely Available from IEEE
  • Guest Editorial Neuroeconomics: A Neural Engineering Perspective

    Page(s): 521
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    Freely Available from IEEE
  • Neural Basis for Brain Responses to TV Commercials: A High-Resolution EEG Study

    Page(s): 522 - 531
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB) |  | HTML iconHTML  

    We investigated brain activity during the observation of TV commercials by tracking the cortical activity and the functional connectivity changes in normal subjects. The aim was to elucidate if the TV commercials that were remembered by the subjects several days after their first observation elicited particular brain activity and connectivity compared with those generated during the observation of TV commercials that were quickly forgotten. High-resolution electroencephalogram (EEG) recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). The patterns of cortical connectivity were obtained in the four principal frequency bands, Theta (3-7 Hz), Alpha (8-12 Hz), Beta (13-30 Hz), Gamma (30-40 Hz) and the directed influences between any given pair of the estimated cortical signals were evaluated by use of a multivariate spectral technique known as partial directed coherence. The topology of the cortical networks has been identified with tools derived from graph theory. Results suggest that the cortical activity and connectivity elicited by the viewing of the TV commercials that were remembered by the experimental subjects are markedly different from the brain activity elicited during the observation of the TV commercials that were forgotten. In particular, during the observation of the TV commercials that were remembered, the amount of cortical spectral activity from the frontal areas (BA 8 and 9) and from the parietal areas (BA 5, 7, and 40) is higher compared with the activity elicited by the observation of TV commercials that were forgotten. In addition, network analysis suggests a clear role of the parietal areas as a target of the incoming flow of information from all the other parts of the cortex during the observation of TV commercials that have been remembered. The techniques presented here shed new light on al- - l the cortical networks and their behavior during the memorization of TV commercials. Such techniques could also be relevant in neuroeconomics and neuromarketing for the investigation of the neural substrates subserving other decision-making and recognition tasks. View full abstract»

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  • How Neuroscience Can Inform Consumer Research

    Page(s): 532 - 538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB) |  | HTML iconHTML  

    Recently, a rapidly growing approach within consumer research has developed under the label of ldquoconsumer neuroscience.rdquo Its goal is to use insights and methods from neuroscience to enhance the understanding of consumer behavior. In this paper we aim to provide an overview of questions of interest to consumer researchers, to present initial research findings, and to outline potential implications for consumer research. In order to do so, we first discuss the term ldquoconsumer neurosciencerdquo and give a brief description of recently discussed issues in consumer research. We then provide a review and short description of initial empirical evidence from past studies in consumer neuroscience. Next, we present an example of how consumer research or, more specifically, customer loyalty research, may benefit from the consumer neuroscience approach. The paper concludes with a discussion of potential implications and suggestions for future research in the nascent field of consumer neuroscience. View full abstract»

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  • Characterizing the EEG Correlates of Exploratory Behavior

    Page(s): 549 - 556
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1088 KB) |  | HTML iconHTML  

    This study aims to characterize the electroencephalography (EEG) correlates of exploratory behavior. Decision making in an uncertain environment raises a conflict between two opposing needs: gathering information about the environment and exploiting this knowledge in order to optimize the decision. Exploratory behavior has already been studied using functional magnetic resonance imaging (fMRI). Based on a usual paradigm in reinforcement learning, this study has shown bilateral activation in the frontal and parietal cortex. To our knowledge, no previous study has been done on it using EEG. The study of the exploratory behavior using EEG signals raises two difficulties. First, the labels of trial as exploitation or exploration cannot be directly derived from the subject action. In order to access this information, a model of how the subject makes his decision must be built. The exploration related information can be then derived from it. Second, because of the complexity of the task, its EEG correlates are not necessarily time locked with the action. So the EEG processing methods used should be designed in order to handle signals that shift in time across trials. Using the same experimental protocol as the fMRI study, results show that the bilateral frontal and parietal areas are also the most discriminant. This strongly suggests that the EEG signal also conveys information about the exploratory behavior. View full abstract»

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  • Interpretable Classifiers for fMRI Improve Prediction of Purchases

    Page(s): 539 - 548
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (637 KB) |  | HTML iconHTML  

    Despite growing interest in applying machine learning to neuroimaging analyses, few studies have gone beyond classifying sensory input to directly predicting behavioral output. With spatial resolution on the order of millimeters and temporal resolution on the order of seconds, functional magnetic resonance imaging (fMRI) is a promising technology for such applications. However, fMRI data's low signal-to-noise ratio, high dimensionality, and extensive spatiotemporal correlations present formidable analytic challenges. Here, we apply different machine-learning algorithms to previously acquired data to examine the ability of fMRI activation in three regions—the nucleus accumbens (NAcc), medial prefrontal cortex (MPFC), and insula—to predict purchasing. Our goal was to improve spatiotemporal interpretability as well as classification accuracy. To this end, sparse penalized discriminant analysis (SPDA) enabled automatic selection of correlated variables, yielding interpretable models that generalized well to new data. Relative to logistic regression, linear discriminant analysis, and linear support vector machines, SPDA not only increased interpretability but also improved classification accuracy. SPDA promises to allow more precise inferences about when specific brain regions contribute to purchasing decisions. More broadly, this approach provides a general framework for using neuroimaging data to build interpretable models, including those that predict choice. View full abstract»

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  • The Coordination Dynamics of Economic Decision Making: A Multilevel Approach to Social Neuroeconomics

    Page(s): 557 - 571
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (341 KB) |  | HTML iconHTML  

    The basic reciprocity between individual parts and collective organization constitutes a key scientific question spanning the biological and social sciences. Such reciprocity is accompanied by the absence of direct linkages between levels of description giving rise to what is often referred to as the aggregation or nonequivalence problem between levels of analysis. This issue is encountered both in neuroscience and economics. So far, in spite of being identified and extensively discussed in various (other) scientific fields, the problem of understanding the nature of the interactions and coordination dynamics between individual (neuron ~ agent) and collective (neural networks ~ population of humans) behaviors has received little, if any attention in the growing field of neuroeconomics. The present contribution focuses on bringing a theoretical perspective to the interpretation of experiments recently published in this field and addressing how the concepts and methods of coordination dynamics may impact future research. First, we very briefly discuss the links between biology and economics. Second, we address the nonequivalence problem between different levels of analysis and the concept of reciprocal causality. Third, neuroeconomics studies that investigate the neural underpinnings of social decision making in the context of two economic games (trust and ultimatum) are reviewed to highlight issues that arise when experimental results exist at multiple scales of observation and description. Finally, in the last two sections, we discuss how coordination dynamics might provide novel routes to studying and modelling the relation between brain activity and decision making. View full abstract»

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  • Fascicular Perineurium Thickness, Size, and Position Affect Model Predictions of Neural Excitation

    Page(s): 572 - 581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1360 KB) |  | HTML iconHTML  

    The number of applications using neural prosthetic interfaces is expanding. Computer models are a valuable tool to evaluate stimulation techniques and electrode designs. Although our understanding of neural anatomy has improved, its impact on the effects of neural stimulation is not well understood. This study evaluated the effects of fascicle perineurial thickness, diameter, and position on axonal excitation thresholds and population recruitment using finite element models and NEURON simulations. The perineurial thickness of human fascicles was found to be 3.0% plusmn 1.0% of the fascicle diameter. Increased perineurial thickness and fascicle diameter increased activation thresholds. The presence of a large neighboring fascicle caused a significant change in activation of a smaller target fascicle by as much as 80% plusmn 11% of the total axon population. Smaller fascicles were recruited at lower amplitudes than neighboring larger fascicles. These effects were further illustrated in a realistic model of a human femoral nerve surrounded by a nerve cuff electrode. The data suggest that fascicular selectivity is strongly dependent upon the anatomy of the nerve being stimulated. Therefore, accurate representations of nerve anatomy are required to develop more accurate computer models to evaluate and optimize nerve electrode designs for neural prosthesis applications. View full abstract»

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  • HandCARE: A Cable-Actuated Rehabilitation System to Train Hand Function After Stroke

    Page(s): 582 - 591
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1684 KB) |  | HTML iconHTML  

    We have developed a robotic interface to train hand and finger function. HandCARE is a Cable-actuated rehabilitation system, in which each finger is attached to an instrumented cable loop allowing force control and a predominantly linear displacement. The device, whose designed is based on biomechanical measurements, can assist the subject in opening and closing movements and can be adapted to accommodate various hand shapes and finger sizes. Main features of the interface include a differential sensing system, and a clutch system which allows independent movement of the five fingers with only one actuator. The device is safe, easily transportable, and offers multiple training possibilities. This paper presents the biomechanical measurements carried out to determine the requirements for a finger rehabilitation device, and the design and characterization of the complete system. View full abstract»

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  • A Smart Multisensor Approach to Assist Blind People in Specific Urban Navigation Tasks

    Page(s): 592 - 594
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (523 KB) |  | HTML iconHTML  

    Visually impaired people are often discouraged in using electronic aids due to complexity of operation, large amount of training, nonoptimized degree of information provided to the user, and high cost. In this paper, a new multisensor architecture is discussed, which would help blind people to perform urban mobility tasks. The device is based on a multisensor strategy and adopts smart signal processing. View full abstract»

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  • 2008 Index IEEE Transactions on Neural Systems and Rehabilitation Engineering Vol. 16

    Page(s): 595 - 606
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    Freely Available from IEEE
  • Leading the field since 1884 [advertisement]

    Page(s): 607
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  • [Front cover]

    Page(s): C1
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  • Have you visited lately? www.ieee.org [advertisement]

    Page(s): 608
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    Freely Available from IEEE
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors

    Page(s): C3
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  • Table of contents

    Page(s): C4
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Aims & Scope

IEEE Transactions on Neural Systems and Rehabilitation Engineering focuses on the rehabilitative and neural aspects of biomedical engineering.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Paul Sajda
Columbia University