An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.     

想象左右手运动的脑电特征提取

针对脑机接口中脑电信号特征提取的传统方法特征数量多、计算量大及分类正确率低等不足,提出了一种基于时域、频域、空域结合的方法用于提取大脑在想象左右手运动时所产生的事件相关去同步(ERD)和事件相关同步(ERS)信号.分别用独立分量分析(Independent Component Analysis, ICA)和小波变换提取原始脑电信号的空域特征及时频域特征,并用BP(Back Propagation )神经网络对提取的特征进行分类.分类实验结果表明,运用提出的方法提取的想象左右手运动脑电的特征,有效克服了传统的仅基于时频域特征提取方法在描述脑电信号本质特征方面的不足,具有较好的分类正确率.     

A comparative study of wavelet families for EEG signal classification

Over the past two decades, wavelet theory has been used for the processing of biomedical signals for feature extraction, compression and de-noising applications. However the question as to which wavelet family is the most suitable for analysis of non-stationary bio-signals is still prevalent among researchers. This paper attempts to find the most useful wavelet function among the existing members of the wavelet families for electroencephalogram signal (EEG) analysis. The EEGs considered for this study belong to both normal as well as abnormal signals like epileptic EEG. Important features such as energy, entropy and standard deviation at different sub-bands were computed using the wavelet functions—Haar, Daubechies (orders 2-10), Coiflets (orders 1-10), and Biorthogonal (orders 1.1, 2.4, 3.5, and 4.4). Feature vectors were used to model and train the Probabilistic Neural Network (PNN) and the classification accuracies were evaluated for each case. The results obtained from PNN classifier were compared with Support Vector Machine (SVM) classifier. From the statistical analysis, it was found that Coiflets 1 is the most suitable candidate among the wavelet families considered in this study for accurate classification of the EEG signals. In this work, we have attempted to improve the computing efficiency as it selects the most suitable wavelet function that can be used for EEG signal processing efficiently and accurately with lesser computational time.     

基于改进三元组损失的伪造人脸视频检测方法

当前大部分伪造人脸检测技术使用深度学习来鉴别真实视频与伪造视频之间的特征差异,此类方法在未压缩视频上取得了不错的效果,但在检测经过压缩处理的视频时检测效果就会严重下降.针对此类问题,提出了基于改进三元组损失的伪造人脸视频检测方法.首先,使用伪影图生成器生成一幅伪影图来加深伪造人脸与真实人脸之间的特征差异;其次,使用改进的三元组损失来解决难例样本难以被正确检测的问题;最后,选用更适合人脸鉴伪的深度学习网络提取卷积特征.在FaceForensics++数据集上与目前领先的人脸鉴伪方法的对比表明,该方法检测准确率优于对比方法.     

Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

Electroencephalography signals are typically used for analyzing epileptic seizures. These signals are highly nonlinear and nonstationary, and some specific patterns exist for certain disease types that are hard to develop an automatic epileptic seizure detection system. This paper discussed statistical mechanics of complex networks, which inherit the characteristic properties of electroencephalography signals, for feature extraction via a horizontal visibility algorithm in order to reduce processing time and complexity. The algorithm transforms a time series signal into a complex network, which some features are abbreviated. The statistical mechanics are calculated to capture distinctions pertaining to certain diseases to form a feature vector. The feature vector is classified by multiclass classification via a k‐nearest neighbor classifier, a multilayer perceptron neural network, and a support vector machine with a 10‐fold cross‐validation criterion. In performance evaluation of proposed method with healthy, seizure‐free interval, and seizure signals, firstly, input data length is regarded among some practical signal samples by optimizing between accuracy‐processing time, and the proposed method yields outstanding performance on the average classification accuracy for 3‐class problems mainly for detection of seizure‐free interval and seizure signals and acceptable results for 2‐class and 5‐class problems comparing with conventional methods. The proposed method is another tool that can be used for classifying signal patterns, as an alternative to time/frequency analyses.     

A new approach for product evaluation based on integration of EEG and eye-tracking

Exploiting biometric measures, especially neurophysiological data of evaluator for product evaluation is advantageous at avoiding bias and subjectivity in expert scoring process. This paper proposes an approach that integrates electroencephalograph (EEG) and eye-tracking (ET) data in a new way to derive multi-faceted supportive information for product evaluation. Firstly, emotion recognition from EEG signals of evaluator is carried out with a spatial–temporal neural network. Then, based on correlations between emotions and preferential judgement, general customer preference toward product design scheme is inferred from emotions by fuzzy system. Finally, general preference is integrated with ET data at application-level to quantify fine-grained customer preferences toward design modules and visual attractiveness. This approach is verified with a case study which evaluates six designs of frontal area of automotive interior, and valuable supportive information for design decision-making is yielded. Also, comprehensive analysis is conducted and the results verify the effectiveness of proposed approach.     

基于孪生网络和双向最大边界排序损失的行人再识别

针对在实际场景中存在的不同行人图像之间比相同行人图像之间更相似所造成的行人再识别准确率较低的问题，提出一种基于孪生网络并结合识别损失和双向最大边界排序损失的行人再识别方法。首先，对在超大数据集上预训练过的神经网络模型进行结构改造，主要是对最后的全连接层进行改造，使模型可以在行人再识别数据集上进行识别判断；其次，联合识别损失和排序损失监督网络在训练集上的训练，并通过正样本对的相似度值减去负样本对的相似度值大于预定阈值这一判定条件，来使得负例图像对之间的距离大于正例图像对之间的距离；最后，使用训练好的神经网络模型在测试集上测试，提取特征并比对特征之间的余弦相似度。在公开数据集Market-1501、CUHK03和DukeMTMC-reID上进行的实验结果表明，所提方法分别取得了89.4%、86.7%、77.2%的rank-1识别率，高于其他典型的行人再识别方法，并且该方法在基准网络结构下最高达到了10.04%的rank-1识别率提升。     

Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG

Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. We apply recurrent neural networks (RNN) combined with signal wavelet decomposition to the problem. We input raw EEG and its wavelet-decomposed subbands into RNN training/testing, as opposed to specific signal features extracted from EEG. To the best of our knowledge this approach has never been attempted before. The data used included both scalp and intracranial EEG recordings obtained from two epileptic patients. We demonstrate that the existence of a “preictal” stage (immediately preceding seizure) of some minutes duration is quite feasible.     

基于改进的卷积神经网络脑电信号情感识别

针对传统机器学习需要人工构建特征及特征质量较低等问题,提出一种新颖的基于一维卷积神经网络（Convolutional Neural Network,CNN）的特征提取方法。采用编码思想,由卷积层和下采样层构成编码器网络提取脑电信号情感特征,随后与特征图一起输入Leaky ReLU激活函数。对于卷积预训练过程,使用交叉熵和正则化项双目标优化损失函数,之后采用随机森林分类器以获得情感分类标签。在国际公开数据集SEED上进行实验,达到94.7%的情感分类准确率,实验结果表明了该方法的有效性和鲁棒性。     

Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study

This paper presents the experimental pilot study to investigate the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) in response to photoplethysmographic (PPG), electrocardiographic (ECG), electroencephalographic (EEG) activity. The assessment of wavelet transform (WT) as a feature extraction method was used in representing the electrophysiological signals. Considering that classification is often more accurate when the pattern is simplified through representation by important features, the feature extraction and selection play an important role in classifying systems such as neural networks. The PPG, ECG, EEG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and the statistical features were calculated to depict their distribution. Our pilot study investigation for any possible electrophysiological activity alterations due to ELF PEMF exposure, was evaluated by the efficiency of DWT as a feature extraction method in representing the signals. As a result, this feature extraction has been justified as a feasible method.     

基于脑功能网络和共空间模式分析脑电情绪识别

传统脑网络的情绪分类将聚类系数、平均最短路径等拓扑属性作为分类特征。针对这些属性易受网络连接阈值和特征选择的影响,难以完全表征不同情绪状态下的网络空间拓扑结构差异的问题,提出了一种基于脑网络和共空间模式的脑电情绪识别方法(EEG emotion classification based on common spatial patterns of brain networks topology,EEC-CSP-BNT)。该算法基于互信息在各个子频段内计算电极间的功能连接矩阵,同时利用共空间模式(common spatial pattern,CSP)分析学习空间滤波器,构建分类特征,最后通过分类器(如Fisher线性判别、支持向量机、K最近邻)实现基于脑电的情绪分类。基于DEAP和SEED数据集的实验结果表明,相比于脑网络拓扑属性,EEC-CSP-BNT能有效提取脑网络拓扑结构的分类信息,提高脑电情绪识别性能。     

Development of PARS-EX pilot plant to study control strategies

A PARtially Simulated EXothermic chemical reactor (PARS-EX) pilot plant is developed in this work to carry out and evaluate various conventional and advanced control strategies. In this reactor, the heat generated from the assumed exothermic reaction was simulated through the use of a controlled steam flow rate into the reactor. Since there is no actual reaction involved, the system is defined as a ‘partially simulated’ reactor. The temperature of the reactor was regulated by an external plate heat exchanger that both cools the process fluid and recycles it back into the reactor. A software interface was also developed to exchange real online data and implement the various control strategies. The advanced control strategies used to control the temperature of the reactor in this work are the neural network-based controllers, which overcome the hassle in periodically tuning conventional controllers. An adaptive method is also incorporated to cater for changes in the process conditions. Tests involving set point tracking and various external and internal disturbance changes were carried out to evaluate and demonstrate the robustness of the neural network-based controllers on the PARS-EX plant. For all of the realistic online cases studied, the neural network-based controllers exhibit better control results compared to the conventional controllers.     

区域损失函数的孪生网络目标跟踪

针对预训练卷积神经网络提取的深度特征空间分辨率低,快速运动造成运动目标空间细节信息丢失等问题,提出用区域损失函数构建孪生网络的目标跟踪,进一步降低深度特征通道之间的冗余性,并减少高层信息丢失。利用线下预训练的VGG-16卷积神经网络提取深度特征,构成初始深度特征空间。通过区域损失函数构建特征和尺度选择网络,根据反向传播的梯度大小进行特征选择。对筛选后的特征进行拼接,融入到孪生网络中匹配跟踪。在OTB-2013、OTB-2015、VOT2016、TempleColor数据集上与其他算法对比。实验结果表明,该算法在快速运动、低分辨率等场景中表现出较好的跟踪精度和鲁棒性。     

Automatic classification of sleep stages based on the time-frequency image of EEG signals

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner–Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.     

基于物联网平台的动态权重损失函数入侵检测系统

随着物联网（IoT）接入设备越来越多,以及网络管理维护人员缺乏对IoT设备的安全意识,针对IoT环境和设备的攻击逐渐泛滥。为了加强IoT环境下的网络安全性,利用基于IoT平台制作的入侵检测数据集,采用卷积神经网络（CNN）+长短期记忆（LSTM）网络为模型架构,利用CNN提取数据的空间特征,LSTM提取数据的时序特征,并将交叉熵损失函数改进为动态权重交叉熵损失函数,制作出一个针对IoT环境的入侵检测系统（IDS）。经实验设计分析,并使用准确率、精确率、召回率和F1-measure作为评估参数。实验结果表明在CNN-LSTM网络架构下采用了动态权重损失函数的模型与采用传统的交叉熵损失函数的模型相比,前者比后者在使用数据集的地址解析协议（ARP）类样本中在F1-Measure上提升了47个百分点,前者比后者针对数据集中的其他少数类样本则提升了2个百分点~10个百分点。实验结果表明,动态权重损失函数能够增强模型对少数类样本的判别能力,且该方法可以提升IDS对少数类攻击样本的判断能力。     

Surrogate data test for nonlinearity of EEG signals: A newborn EEG burst suppression case study

This paper applies the surrogate data method to investigate the presence of nonlinearity in neonatal electroencephalogram (EEG) burst suppression (B/S) patterns in order to rationalize the use of nonlinear methods for automated detection of such patterns. To generate surrogate data, the statically transformed autoregressive process (STAP) algorithm is deployed, and, the correlation dimension (CD) and asymmetry due to time reversal (REV) are applied as discriminating statistics. The results of the surrogate data test demonstrate the nonlinearity characteristic of real neonatal EEG signals during both burst and suppression phases at the 0.05 significance level. The evidence of nonlinearity is found in 90% and 87% of bursts and suppressions respectively. Furthermore, the ability of nonlinear tools in detecting B/S patterns in multichannel neonatal EEG signals is investigated using receiver operating characteristic analysis. The experimental results show that the CD outperforms existing methods based on the nonlinear energy operator.     

精分EEG脑网络同步稳定性研究

为了深入表征和刻画精神分裂症患者大脑活动时各个电极通道的状态变化，通过利用复杂网络同步稳定理论以及精分工作记忆实验范式对EEG信号进行分析。从复杂网络角度出发构建脑功能网络，并利用特征谱比值法分析脑网络及其同步性随时间的演化过程。对比实验表明精分患者和正常对照组同步能力具有很大差异且差异主要源于对应脑网络的一个局部化区域S的不同，并通过设计对比实验进一步验证此区域对脑网络同步影响的有效性。脑网络同步稳定区域S的发现对研究神经精神性疾病下脑网络的演化过程提供了新的思路。     

基于FPGA的E1信号失帧检测研究

针对E1信号传输过程中出现的失帧问题进行研究．提出以FPGA为控制核心的检测方案,当信号出现失帧状况时能及时进行检测并作出相应措施,以保障E1信号传输的稳定、流畅。     

Position referenced force augmentation in teleoperated hydraulic manipulators operating under delayed and lossy networks: A pilot study

Position error between motions of the master and slave end-effectors is inevitable as it originates from hard-to-avoid imperfections in controller design and model uncertainty. Moreover, when a slave manipulator is controlled through a delayed and lossy communication channel, the error between the desired motion originating from the master device and the actual movement of the slave manipulator end-effector is further exacerbated. This paper introduces a force feedback scheme to alleviate this problem by simply guiding the operator to slow down the haptic device motion and, in turn, allows the slave manipulator to follow the desired trajectory closely. Using this scheme, the master haptic device generates a force, which is proportional to the position error at the slave end-effector, and opposite to the operator’s intended motion at the master site. Indeed, this force is a signal or cue to the operator for reducing the hand speed when position error, due to delayed and lossy network, appears at the slave site. Effectiveness of the proposed scheme is validated by performing experiments on a hydraulic telemanipulator setup developed for performing live-line maintenance. Experiments are conducted when the system operates under both dedicated and wireless networks. Results show that the scheme performs well in reducing the position error between the haptic device and the slave end-effector. Specifically, by utilizing the proposed force, the mean position error, for the case presented here, reduces by at least 92% as compared to the condition without the proposed force augmentation scheme. The scheme is easy to implement, as the only required on-line measurement is the angular displacement of the slave manipulator joints.     

基于动态检测技术的配电网输电线网损评估

通过直接估计输电线路电阻实现配电网电线网损评估功能,存在难以精准把握的问题,因而提出了将配电网输电线最高允许温度转化为有功功率损耗的限制,从而确立输电线短期热定值的方法。以输电线路热平衡方程中环境参量作为状态变量,根据输电线有功功率损耗与电阻、温度的的耦合关系,推导出输电线有功功率损耗变化的微分方程,以此建立系统的量测方程;同时,考虑热平衡方程式参数变化的迟缓性,建立状态变量空间表达式;继而,依据输电线有功功率损耗变化率,采用卡尔曼滤波的方法,实现对状态变量的估计。由此,完成了配电网电线网损评估的功能,为配电网输电线短期最大电流运行方式是否可行提供了解决依据。以贵州省凯里某地区220kv配电网输电线的实测数据为例,验证了本文方法的可行性与有效性。