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Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
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Asian Journal of Applied Sciences     Open Access   (Followers: 3)
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ATZextra worldwide     Hybrid Journal   (Followers: 1)
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Avances en Ciencias e Ingeniería     Open Access  
Bangladesh Journal of Scientific and Industrial Research     Open Access  
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Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
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BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)

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Journal Cover   Autonomous Mental Development, IEEE Transactions on
  [5 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1943-0604
   Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [177 journals]
  • IEEE Transactions on Autonomous Mental Development information for authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • IEEE Computational Intelligence Society Information
    • Abstract: Provides a listing of board members, committee members and society officers.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Table of Contents
    • Abstract: Presents the table of contents for this issue of the publication.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • IEEE Transactions on Autonomous Mental Development publication information
    • Abstract: Provides a listing of the editors, board members, and current staff for this issue of the publication.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Editorial Announcing the Title Change of the IEEE Transactions on
           Autonomous Mental Development in 2016
    • Authors: Salah; A;
      Pages: 157 - 157
      Abstract: Presents information regarding the title change of the IEEE Transactions on Autonomous Mental Development to will change its name to the IEEE Transactions on Cognitive and Developmental Systems in 2016.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Guest Editorial Multimodal Modeling and Analysis Informed by Brain
           Imaging—Part I
    • Authors: J;T;C;J;
      Pages: 158 - 161
      Abstract: Human brains are the ultimate recipients and assessors of multimedia contents and semantics. Recent developments of neuroimaging techniques have enabled us to probe human brain activities during free viewing of multimedia contents. This special issue mainly focuses on the synergistic combinations of cognitive neuroscience, brain imaging, and multimedia analysis. It aims to capture the latest advances in the research community working on brain imaging informed multimedia analysis, as well as computational model of the brain processes driven by multimedia contents.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Investigating Critical Frequency Bands and Channels for EEG-Based Emotion
           Recognition with Deep Neural Networks
    • Authors: Wei-Long Zheng;Bao-Liang Lu;
      Pages: 162 - 175
      Abstract: To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9, and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65%, which is even better than that of the original 62 channels. The critical frequency bands and channels determined by using the weights of trained DBNs are consistent with the existing observations. In addition, our experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals. We compare the performance of deep models with shallow models. The average accuracies of DBN, SVM, LR, and KNN are 86.08%, 83.99%, 82.70%, and 72.60%, respectively.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • What Strikes the Strings of Your Heart?–Multi-Label
           Dimensionality Reduction for Music Emotion Analysis via Brain Imaging
    • Authors: Yang Liu;Yan Liu;Chaoguang Wang;Xiaohong Wang;Peiyuan Zhou;Yu; G.;Chan, K.C.C.;
      Pages: 176 - 188
      Abstract: After 20 years of extensive study in psychology, some musical factors have been identified that can evoke certain kinds of emotions. However, the underlying mechanism of the relationship between music and emotion remains unanswered. This paper intends to find the genuine correlates of music emotion by exploring a systematic and quantitative framework. The task is formulated as a dimensionality reduction problem, which seeks the complete and compact feature set with intrinsic correlates for the given objectives. Since a song generally elicits more than one emotion, we explore dimensionality reduction techniques for multi-label classification. One challenging problem is that the hard label cannot represent the extent of the emotion and it is also difficult to ask the subjects to quantize their feelings. This work tries utilizing the electroencephalography (EEG) signal to solve this challenge. A learning scheme called EEG-based emotion smoothing ( E2S) and a bilinear multi-emotion similarity preserving embedding (BME-SPE) algorithm are proposed. We validate the effectiveness of the proposed framework on standard dataset CAL-500. Several influential correlates have been identified and the classification via those correlates has achieved good performance. We build a Chinese music dataset according to the identified correlates and find that the music from different cultures may share similar emotions.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Emotion Recognition with the Help of Privileged Information
    • Authors: Shangfei Wang;Yachen Zhu;Lihua Yue;Qiang Ji;
      Pages: 189 - 200
      Abstract: In this article, we propose a novel approach to recognize emotions with the help of privileged information, which is only available during training, but not available during testing. Such additional information can be exploited during training to construct a better classifier. Specifically, we recognize audience's emotion from EEG signals with the help of the stimulus videos, and tag videos' emotions with the aid of electroencephalogram (EEG) signals. First, frequency features are extracted from EEG signals and audio/visual features are extracted from video stimulus. Second, features are selected by statistical tests. Third, a new EEG feature space and a new video feature space are constructed simultaneously using canonical correlation analysis (CCA). Finally, two support vector machines (SVM) are trained on the new EEG and video feature spaces respectively. During emotion recognition from EEG, only EEG signals are available, and the SVM classifier obtained on EEG feature space is used; while for video emotion tagging, only video clips are available, and the SVM classifier constructed on video feature space is adopted. Experiments of EEG-based emotion recognition and emotion video tagging are conducted on three benchmark databases, demonstrating that video content, as the context, can improve the emotion recognition from EEG signals and EEG signals available during training can enhance emotion video tagging.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Decoding Semantics Categorization during Natural Viewing of Video Streams
    • Authors: Xintao Hu;Lei Guo;Junwei Han;Tianming Liu;
      Pages: 201 - 210
      Abstract: Exploring the functional mechanism of the human brain during semantics categorization and subsequently leverage current semantics-oriented multimedia analysis by functional brain imaging have been receiving great attention in recent years. In the field, most of existing studies utilized strictly controlled laboratory paradigms as experimental settings in brain imaging data acquisition. They also face the critical problem of modeling functional brain response from acquired brain imaging data. In this paper, we present a brain decoding study based on sparse multinomial logistic regression (SMLR) algorithm to explore the brain regions and functional interactions during semantics categorization. The setups of our study are two folds. First, we use naturalistic video streams as stimuli in functional magnetic resonance imaging (fMRI) to simulate the complex environment for semantics perception that the human brain has to process in real life. Second, we model brain responses to semantics categorization as functional interactions among large-scale brain networks. Our experimental results show that semantics categorization can be accurately predicted by both intrasubject and intersubject brain decoding models. The brain responses identified by the decoding model reveal that a wide range of brain regions and functional interactions are recruited during semantics categorization. Especially, the working memory system exhibits significant contributions. Other substantially involved brain systems include emotion, attention, vision and language systems.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • An Iterative Approach for EEG-Based Rapid Face Search: A Refined Retrieval
           by Brain Computer Interfaces
    • Authors: Yiwen Wang;Lei Jiang;Yun Wang;Bangyu Cai;Yueming Wang;Weidong Chen;Sanyuan Zhang;Xiaoxiang Zheng;
      Pages: 211 - 222
      Abstract: Recent face recognition techniques have achieved remarkable successes in fast face retrieval on huge image datasets. But the performance is still limited when large illumination, pose, and facial expression variations are presented. In contrast, the human brain has powerful cognitive capability to recognize faces and demonstrates robustness across viewpoints, lighting conditions, even in the presence of partial occlusion. This paper proposes a closed-loop face retrieval system that combines the state-of-the-art face recognition method with the powerful cognitive function of the human brain illustrated in electroencephalography signals. The system starts with a random face image and outputs the ranking of all of the images in the database according to their similarity to the target individual. At each iteration, the single trial event related potentials (ERP) detector scores the user's interest in rapid serial visual presentation paradigm, where the presented images are selected from the computer face recognition module. When the system converges, the ERP detector further refines the lower ranking to achieve better performance. In total, 10 subjects participated in the experiment, exploring a database containing 1,854 images of 46 celebrities. Our approach outperforms existing methods with better average precision, indicating human cognitive ability complements computer face recognition and contributes to better face retrieval.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Age Effect in Human Brain Responses to Emotion Arousing Images: The EEG
           3D-Vector Field Tomography Modeling Approach
    • Authors: Papadaniil; C.D.;Kosmidou, V.E.;Tsolaki, A.C.;Hadjileontiadis, L.J.;Tsolaki, M.;Yiannis Kompatsiaris, I.;
      Pages: 223 - 235
      Abstract: Understanding of the brain responses to emotional stimulation remains a great challenge. Studies on the aging effect in neural activation report controversial results. In this paper, pictures of two classes of facial affect, i.e., anger and fear, were presented to young and elderly participants. High-density 256-channel EEG data were recorded and an innovative methodology was used to map the activated brain state at the N170 event-related potential component. The methodology, namely 3D Vector Field Tomography, reconstructs the electrostatic field within the head volume and requires no prior modeling of the individual's brain. Results showed that the elderly exhibited greater N170 amplitudes, while age-based differences were also observed in the topographic distribution of the EEG recordings at the N170 component. The brain activation analysis was performed by adopting a set of regions of interest. Results on the maximum activation area appeared to be emotion-specific; the anger emotional conditions induced the maximum activation in the inferior frontal gyrus, while fear activated more the superior temporal gyrus. The approach used here shows the potential of the proposed computational model to reveal the age effect on the brain activation upon emotion arousing images, which could be further transferred to the design of assistive clinical applications.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Perceptual Experience Analysis for Tone-mapped HDR Videos Based on EEG and
           Peripheral Physiological Signals
    • Authors: Seong-Eun Moon;Jong-Seok Lee;
      Pages: 236 - 247
      Abstract: High dynamic range (HDR) imaging has been attracting much attention as a technology that can provide immersive experience. Its ultimate goal is to provide better quality of experience (QoE) via enhanced contrast. In this paper, we analyze perceptual experience of tone-mapped HDR videos both explicitly by conducting a subjective questionnaire assessment and implicitly by using EEG and peripheral physiological signals. From the results of the subjective assessment, it is revealed that tone-mapped HDR videos are more interesting and more natural, and give better quality than low dynamic range (LDR) videos. Physiological signals were recorded during watching tone-mapped HDR and LDR videos, and classification systems are constructed to explore perceptual difference captured by the physiological signals. Significant difference in the physiological signals is observed between tone-mapped HDR and LDR videos in the classification under both a subject-dependent and a subject-independent scenarios. Also, significant difference in the signals between high versus low perceived contrast and overall quality is detected via classification under the subject-dependent scenario. Moreover, it is shown that features extracted from the gamma frequency band are effective for classification.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Predicting Purchase Decisions Based on Spatio-Temporal Functional MRI
           Features Using Machine Learning
    • Authors: Yunzhi Wang;Chattaraman; V.;Hyejeong Kim;Deshpande, G.;
      Pages: 248 - 255
      Abstract: Machine learning algorithms allow us to directly predict brain states based on functional magnetic resonance imaging (fMRI) data. In this study, we demonstrate the application of this framework to neuromarketing by predicting purchase decisions from spatio-temporal fMRI data. A sample of 24 subjects were shown product images and asked to make decisions of whether to buy them or not while undergoing fMRI scanning. Eight brain regions which were significantly activated during decision-making were identified using a general linear model. Time series were extracted from these regions and input into a recursive cluster elimination based support vector machine (RCE-SVM) for predicting purchase decisions. This method iteratively eliminates features which are unimportant until only the most discriminative features giving maximum accuracy are obtained. We were able to predict purchase decisions with 71% accuracy, which is higher than previously reported. In addition, we found that the most discriminative features were in signals from medial and superior frontal cortices. Therefore, this approach provides a reliable framework for using fMRI data to predict purchase-related decision-making as well as infer its neural correlates.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • A Robust Gradient-Based Algorithm to Correct Bias Fields of Brain MR
    • Authors: Qiang Ling;Zhaohui Li;Qinghua Huang;Xuelong Li;
      Pages: 256 - 264
      Abstract: We developed a novel algorithm to estimate bias fields from brain magnetic resonance (MR) images using a gradient-based method. The bias field is modeled as a multiplicative and slowly varying surface. We fit the bias field by a low-order polynomial. The polynomial's parameters are directly obtained by minimizing the sum of square errors between the gradients of MR images (both in the x-direction and y-direction) and the partial derivatives of the desired polynomial in the log domain. Compared to the existing retrospective algorithms, our algorithm combines the estimation of the gradient of the bias field and the reintegration of the obtained gradient polynomial together so that it is more robust against noise and can achieve better performance, which are demonstrated through experiments with both real and simulated brain MR images.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Call for nominations applications for the position of the editor-in-chief
           of IEEE transactions on cognitive and developmental system
    • Pages: 265 - 265
      Abstract: Presents a call for nominations for the position of the editor-in-chief for the IEEE transactions on cognitive and developmental systems.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Introducing IEEE Collabratec
    • Pages: 266 - 266
      Abstract: IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical interests, or career pursuits. You can also create and share a professional identity that showcases key accomplishments and participate in groups focused around mutual interests, actively learning from and contributing to knowledgeable communities. All in one place! Learn about IEEE Collabratec at
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • IEEE membership can help you reach your personal goals
    • Pages: 267 - 267
      Abstract: Advertisement, IEEE.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
  • Expand your network, get rewarded
    • Pages: 268 - 268
      Abstract: Advertisement, IEEE.
      PubDate: Sept. 2015
      Issue No: Vol. 7, No. 3 (2015)
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