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Types, Locations, and Scales from Cluttered Natural Video and Actions 类型,位置和比例从杂乱的自然视频和动作
Pub Date : 2015-11-09 DOI: 10.1109/TAMD.2015.2478377
Xiaoying Song, Wenqiang Zhang, J. Weng
We model the autonomous development of brain-inspired circuits through two modalities-video stream and action stream that are synchronized in time. We assume that such multimodal streams are available to a baby through inborn reflexes, self-supervision, and caretaker's supervision, when the baby interacts with the real world. By autonomous development, we mean that not only that the internal (inside the “skull”) self-organization is fully autonomous, but the developmental program (DP) that regulates the computation of the network is also task nonspecific. In this work, the task-nonspecificity is reflected by the fact that the actions associated with an attended object in a cluttered, natural, and dynamic scene is taught after the DP is finished and the “life” has begun. The actions correspond to neuronal firing patterns representing object type, object location and object scale, but learning is directly from unsegmented cluttered scenes. Along the line of where-what networks (WWN), this is the first one that explicitly models multiple “brain” areas-each for a different range of object scales. Among experiments, large natural video experiments were conducted. To show the power of automatic attention in unknown cluttered backgrounds, the last experimental group demonstrated disjoint tests in the presence of large within-class variations (object 3-D-rotations in very different unknown backgrounds), but small between-class variations (small object patches in large similar and different unknown backgrounds), in contrast with global classification tests such as ImageNet and Atari Games.
我们通过同步的视频流和动作流两种模式来模拟脑启发回路的自主发展。我们假设,当婴儿与现实世界互动时,这种多模态流可以通过先天反射、自我监督和看护人的监督获得。通过自主发展,我们不仅意味着内部(在“头骨”内部)自组织是完全自主的,而且调节网络计算的发展程序(DP)也是任务非特异性的。在这项工作中,任务非特异性反映在这样一个事实:在一个混乱的、自然的、动态的场景中,与被关注对象相关的动作是在DP完成和“生活”开始之后教授的。动作对应于代表物体类型、物体位置和物体大小的神经元放电模式,但学习是直接从未分割的杂乱场景中进行的。沿着“在哪里-什么网络”(WWN)的路线,这是第一个明确地为多个“大脑”区域建模的模型——每个区域针对不同范围的对象尺度。实验中,进行了大型自然视频实验。为了展示在未知的杂乱背景下自动注意的力量,最后一个实验组展示了在类内大变化(在非常不同的未知背景下的物体三维旋转)和类间小变化(在大型相似和不同的未知背景下的小物体补丁)存在的不连贯测试,与ImageNet和Atari Games等全局分类测试形成对比。
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引用次数: 4
Guest Editorial Multimodal Modeling and Analysis Informed by Brain Imaging—Part 1 基于脑成像的多模态建模与分析——第一部分
Pub Date : 2015-11-04 DOI: 10.1109/TAMD.2015.2498458
Junwei, Tianming, Christine, Juyang
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.
人脑是多媒体内容和语义的最终接受者和评估者。神经成像技术的最新发展使我们能够在免费观看多媒体内容时探测人类的大脑活动。本期特刊主要关注认知神经科学、脑成像和多媒体分析的协同结合。它的目的是捕捉在脑成像的研究社区工作的最新进展通知多媒体分析,以及由多媒体内容驱动的大脑过程的计算模型。
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引用次数: 0
Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data 基于SVM-FoBa的双相情感障碍与重度抑郁症鉴别:基于多模态脑成像数据的高效特征选择
Pub Date : 2015-10-26 DOI: 10.1109/TAMD.2015.2440298
Nan-Feng Jie, Mao-Hu Zhu, Xiao-Ying Ma, E. Osuch, M. Wammes, J. Théberge, Huan-Dong Li, Yu Zhang, Tianzi Jiang, J. Sui, V. Calhoun
Discriminating between bipolar disorder (BD) and major depressive disorder (MDD) is a major clinical challenge due to the absence of known biomarkers; hence a better understanding of their pathophysiology and brain alterations is urgently needed. Given the complexity, feature selection is especially important in neuroimaging applications, however, feature dimension and model understanding present serious challenges. In this study, a novel feature selection approach based on linear support vector machine with a forward-backward search strategy (SVM-FoBa) was developed and applied to structural and resting-state functional magnetic resonance imaging data collected from 21 BD, 25 MDD and 23 healthy controls. Discriminative features were drawn from both data modalities, with which the classification of BD and MDD achieved an accuracy of 92.1% (1000 bootstrap resamples). Weight analysis of the selected features further revealed that the inferior frontal gyrus may characterize a central role in BD-MDD differentiation, in addition to the default mode network and the cerebellum. A modality-wise comparison also suggested that functional information outweighs anatomical by a large margin when classifying the two clinical disorders. This work validated the advantages of multimodal joint analysis and the effectiveness of SVM-FoBa, which has potential for use in identifying possible biomarkers for several mental disorders.
由于缺乏已知的生物标志物,区分双相情感障碍(BD)和重度抑郁症(MDD)是一个重大的临床挑战;因此,迫切需要更好地了解它们的病理生理和大脑变化。鉴于复杂性,特征选择在神经成像应用中尤为重要,然而,特征维度和模型理解提出了严峻的挑战。本研究提出了一种基于线性支持向量机的前向向后搜索策略(SVM-FoBa)特征选择方法,并将其应用于21例BD、25例MDD和23例健康对照的结构和静息状态功能磁共振成像数据。从两种数据模式中提取判别特征,其中BD和MDD的分类准确率达到92.1%(1000个bootstrap样本)。对所选特征的权重分析进一步揭示,除了默认模式网络和小脑外,额下回可能在BD-MDD分化中发挥核心作用。一种模式明智的比较也表明,在分类两种临床疾病时,功能信息比解剖信息重要。这项工作验证了多模态联合分析的优势和SVM-FoBa的有效性,该方法有可能用于识别几种精神障碍的可能生物标志物。
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引用次数: 80
Editorial Announcing the Title Change of the IEEE Transactions on Autonomous Mental Development in 2016 2016年IEEE自主心理发展汇刊标题变更公告
Pub Date : 2015-09-01 DOI: 10.1109/TAMD.2015.2495801
Angelo Salah
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.
介绍了关于IEEE自主心理发展汇刊的标题变更的信息,将于2016年更名为IEEE认知与发展系统汇刊。
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引用次数: 0
A Robust Gradient-Based Algorithm to Correct Bias Fields of Brain MR Images 基于鲁棒梯度的脑磁共振图像偏场校正算法
Pub Date : 2015-09-01 DOI: 10.1109/TAMD.2015.2416976
Q. Ling, Zhaohui Li, Qinghua Huang, Xuelong Li
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.
本文提出了一种基于梯度的脑磁共振图像偏置场估计算法。偏置场被建模为一个乘法和缓慢变化的曲面。我们用一个低阶多项式拟合偏置场。通过最小化MR图像(x方向和y方向)的梯度与期望多项式在对数域中的偏导数之间的误差平方和,直接获得多项式的参数。与现有的回溯算法相比,我们的算法将偏置场的梯度估计和得到的梯度多项式的重新整合结合在一起,对噪声具有更强的鲁棒性,可以获得更好的性能,并通过真实和模拟的脑MR图像进行了实验。
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引用次数: 6
Emotion Recognition with the Help of Privileged Information 基于特权信息的情感识别
Pub Date : 2015-07-30 DOI: 10.1109/TAMD.2015.2463113
Shangfei Wang, Yachen Zhu, Lihua Yue, Q. Ji
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.
在本文中,我们提出了一种利用特权信息识别情绪的新方法,这种信息仅在训练期间可用,而在测试期间不可用。在训练期间可以利用这些附加信息来构建更好的分类器。具体来说,我们借助刺激视频从脑电图信号中识别观众的情绪,并借助脑电图信号对视频的情绪进行标记。首先从脑电信号中提取频率特征,从视频刺激中提取音频/视觉特征。其次,通过统计检验选择特征。第三,利用典型相关分析(canonical correlation analysis, CCA)同时构建新的脑电特征空间和新的视频特征空间。最后,分别在新的EEG和视频特征空间上训练两个支持向量机(SVM)。在对脑电信号进行情绪识别时,只使用脑电信号,利用脑电信号特征空间得到的SVM分类器;而对于视频情感标注,只选取视频片段,采用基于视频特征空间构建的SVM分类器。在三个基准数据库上进行了基于脑电图的情感识别和情感视频标注实验,结果表明,视频内容作为上下文可以提高对脑电图信号的情感识别,训练过程中可用的脑电图信号可以增强情感视频标注。
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引用次数: 33
Perceptual Experience Analysis for Tone-mapped HDR Videos Based on EEG and Peripheral Physiological Signals 基于脑电图和外周生理信号的色调映射HDR视频感知体验分析
Pub Date : 2015-06-24 DOI: 10.1109/TAMD.2015.2449553
Seong-eun Moon, Jong-Seok Lee
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.
高动态范围(HDR)成像技术作为一种能够提供沉浸式体验的技术一直备受关注。其最终目标是通过增强对比度提供更好的体验质量(QoE)。在本文中,我们分析了色调映射HDR视频的感知体验,通过进行主观问卷评估显式,并利用脑电图和外周生理信号隐式。主观评价结果表明,色调映射的HDR视频比低动态范围(LDR)视频更有趣、更自然,画质也更好。在观看音调映射的HDR和LDR视频时记录生理信号,并构建分类系统来探索生理信号捕获的感知差异。在受试者依赖和受试者独立场景下,色调映射HDR和LDR视频的生理信号在分类上存在显著差异。此外,在主体依赖情景下,通过分类检测到高与低感知对比度和整体质量之间信号的显着差异。此外,从伽马频段提取的特征对分类是有效的。
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引用次数: 26
An Iterative Approach for EEG-Based Rapid Face Search: A Refined Retrieval by Brain Computer Interfaces 一种基于脑电图的快速人脸搜索迭代方法:基于脑机接口的精细检索
Pub Date : 2015-06-17 DOI: 10.1109/TAMD.2015.2446499
Yiwen Wang, Lei Jiang, Yun Wang, Bangyu Cai, Yueming Wang, Weidong Chen, S. Zhang, Xiaoxiang Zheng
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.
近年来的人脸识别技术在海量图像数据集上的快速人脸检索方面取得了显著的成功。但是,当出现大的照明、姿势和面部表情变化时,性能仍然受到限制。相比之下,人类大脑具有强大的识别人脸的认知能力,并且即使在部分遮挡的情况下,也能在视点、光照条件下表现出鲁棒性。本文提出了一种闭环人脸检索系统,该系统将最先进的人脸识别方法与脑电图信号所显示的人类大脑强大的认知功能相结合。系统从一张随机的人脸图像开始,并根据与目标个体的相似度输出数据库中所有图像的排名。在每次迭代中,单试验事件相关电位(ERP)检测器对用户在快速串行视觉呈现范式中的兴趣进行评分,其中呈现的图像是从计算机人脸识别模块中选择的。当系统收敛时,ERP检测器进一步细化较低的排序以获得更好的性能。总共有10名受试者参与了这项实验,他们在一个包含46位名人的1854张照片的数据库中进行研究。我们的方法在平均精度上优于现有的方法,表明人类的认知能力是对计算机人脸识别的补充,有助于更好的人脸检索。
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引用次数: 10
Beyond Subjective Self-Rating: EEG Signal Classification of Cognitive Workload 超越主观自评:认知负荷的脑电图信号分类
Pub Date : 2015-06-04 DOI: 10.1109/TAMD.2015.2441960
P. Zarjam, J. Epps, N. Lovell
Cognitive workload is an important indicator of mental activity that has implications for human-computer interaction, biomedical and task analysis applications. Previously, subjective rating (self-assessment) has often been a preferred measure, due to its ease of use and relative sensitivity to the cognitive load variations. However, it can only be feasibly measured in a post-hoc manner with the user's cooperation, and is not available as an online, continuous measurement during the progress of the cognitive task. In this paper, we used a cognitive task inducing seven different levels of workload to investigate workload discrimination using electroencephalography (EEG) signals. The entropy, energy, and standard deviation of the wavelet coefficients extracted from the segmented EEGs were found to change very consistently in accordance with the induced load, yielding strong significance in statistical tests of ranking accuracy. High accuracy for subject-independent multichannel classification among seven load levels was achieved, across the twelve subjects studied. We compare these results with alternative measures such as performance, subjective ratings, and reaction time (response time) of the subjects and compare their reliability with the EEG-based method introduced. We also investigate test/re-test reliability of the recorded EEG signals to evaluate their stability over time. These findings bring the use of passive brain-computer interfaces (BCI) for continuous memory load measurement closer to reality, and suggest EEG as the preferred measure of working memory load.
认知负荷是心理活动的一个重要指标,在人机交互、生物医学和任务分析应用中具有重要意义。以前,主观评分(自我评估)往往是首选的测量方法,因为它易于使用和相对敏感的认知负荷变化。然而,它只能在用户的配合下以一种事后的方式进行测量,而不能在认知任务的过程中作为一种在线的、连续的测量。本文采用认知任务诱导7种不同水平的工作负荷,利用脑电图(EEG)信号研究工作负荷歧视。从分割的脑电图中提取的小波系数的熵、能量和标准差与诱导负荷的变化非常一致,在排序准确性的统计检验中具有很强的显著性。在研究的12个受试者中,在7个负载水平中实现了与受试者无关的多通道分类的高精度。我们将这些结果与其他测量方法,如表现、主观评分和受试者的反应时间(反应时间)进行比较,并将其与基于脑电图的方法的可靠性进行比较。我们还研究了记录的脑电图信号的测试/再测试可靠性,以评估其随时间的稳定性。这些发现使被动脑机接口(BCI)用于连续记忆负荷测量更加接近现实,并建议EEG作为工作记忆负荷的首选测量方法。
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引用次数: 85
Design of a Multimodal EEG-based Hybrid BCI System with Visual Servo Module 基于多模态脑电图的视觉伺服模块混合脑机接口系统设计
Pub Date : 2015-05-19 DOI: 10.1109/TAMD.2015.2434951
F. Duan, Dongxue Lin, Wenyu Li, Zhao Zhang
Current EEG-based brain-computer interface technologies mainly focus on how to independently use SSVEP, motor imagery, P300, or other signals to recognize human intention and generate several control commands. SSVEP and P300 require external stimulus, while motor imagery does not require it. However, the generated control commands of these methods are limited and cannot control a robot to provide satisfactory service to the user. Taking advantage of both SSVEP and motor imagery, this paper aims to design a hybrid BCI system that can provide multimodal BCI control commands to the robot. In this hybrid BCI system, three SSVEP signals are used to control the robot to move forward, turn left, and turn right; one motor imagery signal is used to control the robot to execute the grasp motion. In order to enhance the performance of the hybrid BCI system, a visual servo module is also developed to control the robot to execute the grasp task. The effect of the entire system is verified in a simulation platform and a real humanoid robot, respectively. The experimental results show that all of the subjects were able to successfully use this hybrid BCI system with relative ease.
目前基于脑电图的脑机接口技术主要集中在如何独立利用SSVEP、运动意象、P300等信号来识别人的意图并生成若干控制命令。SSVEP和P300需要外部刺激,而运动意象不需要外部刺激。然而,这些方法生成的控制命令是有限的,无法控制机器人为用户提供满意的服务。利用SSVEP和运动图像的优势,本文旨在设计一个混合BCI系统,为机器人提供多模态BCI控制命令。在该混合BCI系统中,使用三个SSVEP信号控制机器人前进、左转和右转;利用一个运动图像信号控制机器人执行抓取动作。为了提高混合BCI系统的性能,还开发了视觉伺服模块来控制机器人执行抓取任务。在仿真平台和真人机器人上分别验证了整个系统的效果。实验结果表明,所有被试都能相对轻松地成功使用该混合脑机接口系统。
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引用次数: 57
期刊
IEEE Transactions on Autonomous Mental Development
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