Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm

This paper proposes a fast weighted horizontal visibility graph constructing algorithm (FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated by comparing with Fast Fourier Transform (FFT) and sample entropy (SampEn) method. Two noise-robustness graph features based on the FWHVA, mean degree and mean strength, are investigated using two chaos signals and five groups of EEG signals. Experimental results show that feature extraction using the FWHVA is faster than that of SampEn and FFT. And mean strength feature associated with ictal EEG is significant higher than that of healthy and inter-ictal EEGs. In addition, an 100% classification accuracy for identifying seizure from healthy shows that the features based on the FWHVA are more promising than the frequency features based on FFT and entropy indices based on SampEn for time series classification.     

Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signals

Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%).     

免疫多域特征融合的多核学习SVM运动想象脑电信号分类

针对多通道四类运动想象(Motor imagery, MI)脑电信号(Electroencephalography, EEG)的分类问题, 提出免疫多域特征融合的多核学习SVM (Support vector machine)运动想象脑电信号分类算法.首先, 通过离散小波变换(Discrete wavelet transform, DWT)提取脑电信号的时频域特征, 并利用一对多公共空间模式(One versus the rest common spatial patterns, OVR-CSP)提取脑电信号的空域特征, 融合时频空域特征形成特征向量.其次, 利用多核学习支持向量机(Multiple kernel learning support vector machine, MKL-SVM)对提取的特征向量进行分类.最后, 利用免疫遗传算法(Immune genetic algorithm, IGA)对模型的相关参数进行优化, 得到识别率更高的脑电信号分类模型.采用BCI2005desc-Ⅲa数据集进行实验验证, 对比结果表明, 本文所提出的分类模型有效地解决了传统单域特征提取算法特征单一、信息描述不足的问题, 更准确地表达了不同受试者个性化的多域特征, 取得了94.21%的识别率, 优于使用相同数据集的其他方法.     

Wavelet transform analytics for RF-based UAV detection and identification system using machine learning

In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal-to-noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR.     

基于双树复小波变换和GBDT的运动想象脑电识别

针对脑机接口研究中的脑电信号特征提取与分类问题,提出了一种基于双树复小波变换结合GBDT的想象左右手运动脑电识别的方法。该方法首先深入研究了双树复小波变换相比于小波包变换在脑电信号特征提取方面的优势并验证了ERD/ERS现象;实验数据采用了2003年国际脑机接口竞赛的标准数据集DataSetⅢ,然后,选取了4个典型的时间段进行实验对比,利用双树复小波变换分解与重构提取运动感知节律相关信号分量的能量均值作为特征进行GBDT分类。最后,实验取得了较好的分类准确度,验证了双树复小波变换结合GBDT的方法在脑电信号识别应用中的有效性。     

The application of the Hilbert spectrum to the analysis of electromyographic signals

This paper investigates the application of the Hilbert spectrum (HS), which is a recent tool for the analysis of nonlinear and nonstationary time-series, to the study of electromyographic (EMG) signals. The HS allows for the visualization of the energy of signals through a joint time-frequency representation. In this work we illustrate the use of the HS in two distinct applications. The first is for feature extraction from EMG signals. Our results showed that the instantaneous mean frequency (IMNF) estimated from the HS is a relevant feature to clinical practice. We found that the median of the IMNF reduces when the force level of the muscle contraction increases. In the second application we investigated the use of the HS for detection of motor unit action potentials (MUAPs). The detection of MUAPs is a basic step in EMG decomposition tools, which provide relevant information about the neuromuscular system through the morphology and firing time of MUAPs. We compared, visually, how MUAP activity is perceived on the HS with visualizations provided by some traditional (e.g. scalogram, spectrogram, Wigner-Ville) time-frequency distributions. Furthermore, an alternative visualization to the HS, for detection of MUAPs, is proposed and compared to a similar approach based on the continuous wavelet transform (CWT). Our results showed that both the proposed technique and the CWT allowed for a clear visualization of MUAP activity on the time-frequency distributions, whereas results obtained with the HS were the most difficult to interpret as they were extremely affected by spurious energy activity.     

基于偏最小二乘法的事件相关电位单次提取研究

为满足脑-机接口特征提取实时性以及临床脑电检测高效性的要求,探讨事件相关电位的单试次高效提取技术尤为重要。将小波分析、经验模态分解、极限学习机以及偏最小二乘(PLS)应用于仿真和真实脑电信号,完成特征提取。结果显示:仿真实验中,不同信噪比下PLS提取性能稳定,P300潜伏期误差小于4 ms;真实脑电中,PLS少次迭代,特征提取更为精确,峰值误差0.551μV,峰值潜伏期偏移量27 ms,均小于小波、经验模态分解以及极限学习机多试次迭代结果(P<0.01)。结果表明偏最小二乘法在事件相关电位单试次提取中具有显著优势。     

Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were deployed for the analysis of the EEG signal: Choi-Williams distribution (CWD), continuous wavelet transform (CWT), and Hilbert-Huang Transform (HHT). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time-frequency representation of the EEG signal using Renyi's entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time-frequency distributions: CWD, CWT, and HHT. The CWT time-frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.     

A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault

A novel methodology for early diagnosis of rolling element bearing fault is employed based on continuous wavelet transform (CWT) and support vector machine (SVM). CWT is especially suited for analyzing non-stationary signals in time–frequency domain where time information is retained as well as frequency content. To better approximate non-stationary vibration signals from rolling element bearing, a wavelet choice criterion is established to select an appropriate mother wavelet for feature extraction. The Shannon wavelet is picked out of several considered wavelets. The classification tree kernels (CTK) are constructed to address nonlinear classification of the characteristic samples derived from the wavelet coefficients. By using Fuzzy pruning strategy, a large variety of classification trees are generated. The trees with diverse structures can effectively explore intrinsic information among samples. Then, the tree kernel matrices can be acquired through ensemble statistical learning, which eventually reveal the similarity of samples objectively and stably. Under such architecture of kernel methods, a classification tree kernel based support vector machine (CTKSVM) is proposed to identify bearing fault. The performance of the methodology involving CWT and CTKSVM (CWT–CTKSVM) is evaluated by cross validation and independent test. The results show that the CWT–CTKSVM totally is superior to other SVM methods with common kernels. Therefore, it is a prospective technique for detection and identification of rolling element bearing fault.     

基于受限玻尔兹曼机的疲劳脑电特性分析

为了准确提取和分类视觉疲劳所引起的脑电特征，以此提醒过度用眼的工作人员及时休息，提出了多通道受限玻尔兹曼机算法和卷积神经网络(CNN)算法结合的深度学习混合模型，利用该模型对枕叶区10个通道的脑电信号进行自动提取内在特征和分类。在基于SSVEP的视觉疲劳脑电数据集上进行评估，深度学习混合模型的平均准确率达到88.63%,比传统的特征提取和分类方法高10%。实验结果证明了深度学习混合模型取得的分类效果较好，并且克服了传统手动提取特征方法不全面的不足，对疲劳脑电的研究具有现实的意义。     

Deep learning analysis of mobile physiological,environmental and location sensor data for emotion detection

The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)     

A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)

In last year’s, the expert target recognition has been become very important topic in radar literature. In this study, a target recognition system is introduced for expert target recognition (ATR) using radar target echo signals of High Range Resolution (HRR) radars. This study includes a combination of an adaptive feature extraction and classification using optimum wavelet entropy parameter values. The features used in this study are extracted from radar target echo signals. Herein, a genetic wavelet extreme learning machine classifier model (GAWELM) is developed for expert target recognition. The GAWELM composes of three stages. These stages of GAWELM are genetic algorithm, wavelet analysis and extreme learning machine (ELM) classifier. In previous studies of radar target recognition have shown that the learning speed of feedforward networks is in general much slower than required and it has been a major disadvantage. There are two important causes. These are: (1) the slow gradient-based learning algorithms are commonly used to train neural networks, and (2) all the parameters of the networks are fixed iteratively by using such learning algorithms. In this paper, a new learning algorithm named extreme learning machine (ELM) for single-hidden layer feedforward networks (SLFNs) Ahern et al., 1989, Al-Otum and Al-Sowayan, 2011, Avci et al., 2005a, Avci et al., 2005b, Biswal et al., 2009, Frigui et al., in press, Cao et al., 2010, Guo et al., 2011, Famili et al., 1997, Han and Huang, 2006, Huang et al., 2011, Huang et al., 2006, Huang and Siew, 2005, Huang et al., 2009, Jiang et al., 2011, Kubrusly and Levan, 2009, Le et al., 2011, Lhermitte et al., in press, Martínez-Martínez et al., 2011, Matlab, 2011, Nelson et al., 2002, Nejad and Zakeri, 2011, Tabib et al., 2009, Tang et al., 2011, which randomly choose hidden nodes and analytically determines the output weights of SLFNs, to eliminate the these disadvantages of feedforward networks for expert target recognition area. Then, the genetic algorithm (GA) stage is used for obtaining the feature extraction method and finding the optimum wavelet entropy parameter values. Herein, the optimal one of four variant feature extraction methods is obtained by using a genetic algorithm (GA). The four feature extraction methods proposed GAWELM model are discrete wavelet transform (DWT), discrete wavelet transform–short-time Fourier transform (DWT–STFT), discrete wavelet transform–Born–Jordan time–frequency transform (DWT–BJTFT), and discrete wavelet transform–Choi–Williams time–frequency transform (DWT–CWTFT). The discrete wavelet transform stage is performed for optimum feature extraction in the time–frequency domain. The discrete wavelet transform stage includes discrete wavelet transform and calculating of discrete wavelet entropies. The extreme learning machine (ELM) classifier is performed for evaluating the fitness function of the genetic algorithm and classification of radar targets. The performance of the developed GAWELM expert radar target recognition system is examined by using noisy real radar target echo signals. The applications results of the developed GAWELM expert radar target recognition system show that this GAWELM system is effective in rating real radar target echo signals. The correct classification rate of this GAWELM system is about 90% for radar target types used in this study.     

EEG efficient classification of imagined right and left hand movement using RBF kernel SVM and the joint CWT_PCA

Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine (SVM) classifier to control a robot arm in order to help a person to find an object in the environment. The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform (CWT) and the empirical modal decomposition (EMD), combined with the principal component analysis (PCA) that were fed through a linear and radial basis function (RBF) kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction.     

掌纹识别研究进展综述

掌纹图像蕴含丰富特征,容易与手背静脉、指节纹及手形特征进行多模态融合,因此成为生物特征识别领域的热点.文中主要从掌纹的采集、感兴趣区域的检测、特征提取与匹配3方面介绍掌纹识别的基本流程.探讨基于不同特征融合的多模态识别策略.根据特征提取方法的不同,掌纹识别算法可分为基于手工设计的算法(如编码特征、结构特征、统计特征、子空间特征)和基于特征学习的算法(如机器学习和深度学习),文中对上述算法进行详细对比和分析.最后讨论未来掌纹识别面临的挑战和发展,特别是复杂场景下跨平台的掌纹识别系统.     

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.     

基于希尔伯特变换联合卷积神经网络的脑电信号识别方法

传统运动想象脑电信号识别方法需要人为提取大量特征,识别性能受研究人员经验影响较大,主观性强;提出一种基于希尔伯特变换(HT)联合卷积神经网络(CNN)的运动想象脑电信号自动识别方法,首先利用HT对原始EEG信号进行分析,实现一维数据向二维幅-相图像转换的同时增加信息提取维度;然后将其作为输入利用CNN层次化的对幅-相二维图像进行理解和解译,自动提取特征并完成分类识别,基于BCI竞赛中所用Graz数据集开展试验,结果表明相对于传统特征提取方法,文章所提算法在低、中、高信噪比条件下均能获得更好的识别性能,具有更强的噪声鲁棒性.     

Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks

The implementation of recurrent neural network (RNN) employing eigenvector methods is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the EEG signals. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and the RNN. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the EEG signals and the RNN trained on these features achieved high classification accuracies.     

Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.     

Single-channel SEMG using wavelet deep belief networks for upper limb motion recognition

Surface electromyography (SEMG) has been widely used in different fields such as human machine interaction and motion recognition. A hybrid classification model based on singular value decomposition (SVD) and wavelet deep belief networks (WDBN) is firstly proposed in this paper, which allows the machine to recognize the single-joint motions of upper limb by using one channel. In this experiment, the three-joint SEMG signals of upper limb are respectively recorded through different two channels, which are employed for subsequent comparison to obtain the best single-channel of each joint. Afterwards, the collected raw signals are enhanced by SVD processing. Wavelet function is applied to replace sigmoid function as activation function for feature learning, and the spectrum signals processed by fast Fourier transform (FFT) are input to WDBN model. The results demonstrate that the recognition rates of three joint movements can be up to 100% by SVD-WDBN method, which is much better than support vector machine (SVM), back propagation (BP) neural network and extreme learning machine (ELM) model. The proposed method makes it more possible to control wearable devices with different single-channel SEMG signals, thereby the work efficiency of smart wearable devices can be improved, as well as the complexity of operations between human and machine can be reduced.     

多频带频域深度置信网络脑电特征识别算法

针对DBN（深度置信网络）脑电信号识别率不高的问题,提出了多频带频域深度置信网络（multi-band FDBN）算法进行特征提取.不同频带存在个体性差异,它们对于分类结果的贡献不完全相同,本文利用带通滤波器将原始的脑电信号分成多个频段,再采用FFT（快速傅里叶变换）将时域信号转换为频域信号并作归一化处理,最后将每个频段的频域数据输入DBN进行训练识别.线下实验证明,相比FDBN（频域深度置信网络）算法,多频带FDBN的平均准确率提高了3.25%,且标准差更小,鲁棒性更好.最后,在智能轮椅平台上,利用多频带FDBN算法基于左右手运动想象脑电信号控制轮椅完成了"8"字形路径,证明了该算法在脑电信号特征提取中的有效性.