Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both modalities. With resting state threshold circle, VPA has been used to detect a hemodynamic response in fNIRS signals, whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each trial. Multiple threshold circles are drawn in the vector plane, where each circle is drawn after task completion in each trial of EEG signal. Finally, both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for activity. Results of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy, that is 91.35%, as compared to other classifiers such as support vector machine (SVM), convolutional neural networks (CNN), deep neural networks (DNN) and VPA (with dual-threshold circles) with classification accuracies 82%, 89%, 87% and 86% respectively. Outcomes of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.     

基于线性判别分析的决策融合脑电意识动态分类

脑电信号的识别与分类是脑机接口技术的热点研究问题,单一分类器不能很好利用特征以及分类器的适应性,导致识别的准确率很难进一步提高,基于线性判别分析的分类决策级融合策略,可用于提高脑-机接口系统的分类准确率。首先,通过分离出两种分类器的假性试验特征,从这两种方法中选择更有可能正确决策提高分类准确性;其次为了测量每个决策的不确定性,使用与所对应分类器的最大和第二大相关系数提取特征向量。基于这一思想,提出了一种新的决策选择器,该方法通过整合两种基于线性判别分析的算法选择更有可能是准确的决策,从而达到提高脑电信号分类准确度。实验结果表明,该方法通过与精度相近的算法相结合在运动想象数据分类上获得了较好的分类准确率。     

A novel two-band equilateral wavelet filter bank method for an automated detection of seizure from EEG signals

One can determinate the occurrence of epileptic seizure from the electroencephalogram (EEG) signal. Nonautomatic epilepsy detection is onerous and may be prone to error. They have augmented automated detection of seizure methods to attain accurate results. In view of this research work, we designed a frequency localized optimal filter bank to assess their effectiveness for automatic detection of seizures from EEG records. The basic preferred requirement of optimal filters relies on low bandwidth in the discipline of biomedical signal processing. This work provides a novel filter bank method called optimal equilateral wavelet filter bank (OEWFB) to satisfy the regularity criteria. This regularity constraint is being satisfied with semi-definite programming (SDP) framework, which specifically does nothing with any parameterization. Implementing the proposed filter banks, it disbands EEG signals into five wavelet sub-bands. The fuzzy entropy (FuEn), Renyi's entropy (ReEn), and the Kraskov entropy (KrEn) are being used for extracting the features from the wavelet sub-bands. The P values provide the distinctive ability of the features. Classification with 10-fold cross-validation for several classifiers such as quadratic discriminant, linear quadratic discriminant, K-nearest neighbor, support vector machine, logistic regression, and complex tree is utilized to classify the EEG signals into seizure vs non-seizure class and seizure-free vs seizure affected class. The proposed research work has gained the highest accuracy, specificity, sensitivity, and positive predictive values of 99.4%, 99%, 99.66%, and 99.35%, respectively, for class-1 (ABCD vs E). The performances of the proposed work using the Bonn EEG data set ensure validation concerning compatibility and robustness.     

Performance analysis of EM,SVD, and SVM classifiers in classification of carcinogenic regions of medical images

In this article, the performance analysis of Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM) classifiers for classification of carcinogenic regions from various medical images is carried out. Cancer detection is one of the critical issues where excessive care needs to be taken for better diagnosis. Any classifier needs to detect the cancer with respect to the efficiency in time of detection and performance. Due to these, three classifiers are selected: Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM). EM classifier performs as the optimizer and SVD classifier performs as the dual class classifier. SVM classifier is used as both optimizer and classifier for multiclass classification procedure and for wide stage cancer detection procedures. The performance analysis of all the three classifiers are analyzed for a group of 100 cancer patients based on the benchmark parameter such as Performance Measures and Quality Metrics. From the experimental results it is evident, that the SVM classifier significantly outperforms other classifiers in the classification of carcinogenic regions of medical images.     

Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMM and KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD feature combining with GMM and KELM based classifiers for the brainwave classification in terms of the Character-Writing application, which do not require any limb movement and stimulus.     

Classification of motor imagery electroencephalography signals using continuous small convolutional neural network

As an important part of brain-computer interface (BCI), the electroencephalography (EEG) technology of motor imagery (MI) has been gradually recognized for its great theoretical value and practical application. In this study, in view of the different MI tasks corresponding to active region of the EEG signals, we adopt a two-dimensional form including time, frequency, and electrode location information, then we design a classification method containing continuous small convolutional neural network (CSCNN). This method is mainly used for feature extraction through continuous small convolutional kernels and one rectangle convolutional kernel, and the softmax classifier for classification. In the experiment, classification accuracy and kappa value are used as evaluation criteria to verify the effectiveness of the method proposed in this study. For classification accuracy, BCI competition IV data set 2b is used to compare with the other five classification methods (CNN, CNN-SAE, stacked autoencoder [SAE], support vector machine [SVM], and one-dimensional convolution combined with gated recurrent unit [1DCGRU]). The results demonstrate that the overall accuracy of CSCNN is higher than other methods, and CSCNN obtains an average accuracy of 82.8%. For kappa value, BCI competition IV data set 2b is used to compare with the other three methods (filter bank common spatial pattern [FBCSP], Twin SVM, and CNN-SAE). The performance of CSCNN is better with an average value of 0.663. Overall, the results show that CSCNN maintains a small number of parameters and improves the classification accuracy.     

Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier

Prediction of protein secondary structures is an important problem in bioinformatics and has many applications. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). The SVM method is a comparatively new learning system which has mostly been used in pattern recognition problems. In this study, SVM is used as a machine learning tool for the prediction of secondary structure and several encoding schemes, including orthogonal matrix, hydrophobicity matrix, BLOSUM62 substitution matrix, and combined matrix of these, are applied and optimized to improve the prediction accuracy. Also, the optimal window length for six SVM binary classifiers is established by testing different window sizes and our new encoding scheme is tested based on this optimal window size via sevenfold cross validation tests. The results show 2% increase in the accuracy of the binary classifiers when compared with the instances in which the classical orthogonal matrix is used. Finally, to combine the results of the six SVM binary classifiers, a new tertiary classifier which combines the results of one-versus-one binary classifiers is introduced and the performance is compared with those of existing tertiary classifiers. According to the results, the Q/sub 3/ prediction accuracy of new tertiary classifier reaches 78.8% and this is better than the best result reported in the literature.     

An intelligent mining system for diagnosing medical images using combined texture‐histogram features

The aim of this article is to design an expert system for medical image diagnosis. We propose a method based on association rule mining combined with classification technique to enhance the diagnosis of medical images. This system classifies the images into two categories namely benign and malignant. In the proposed work, association rules are extracted for the selected features using an algorithm called AprioriTidImage, which is an improved version of Apriori algorithm. Then, a new associative classifier CLASS_Hiconst ( CL assifier based on ASS ociation rules with Hi gh Con fidence and S uppor t ) is modeled and used to diagnose the medical images. The performance of our approach is compared with two different classifiers Fuzzy‐SVM and multilayer back propagation neural network (MLPNN) in terms of classifier efficiency with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. The experimental result shows 96% accuracy, 97% sensitivity, and 96% specificity and proves that association rule based classifier is a powerful tool in assisting the diagnosing process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 194–203, 2013     

Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response to audiovisual stimuli for the mentioned emotions. Using a bandpass filter with a bandwidth of 1–100 Hz, recorded raw EEG signals are preprocessed. The preprocessed signals then further analyzed and twelve selected features in different domains are extracted. The Random forest (RF) and multilayer perceptron (MLP) algorithms are then used for the classification of the emotions through extracted features. The proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80% and 88% using MLP and RF classifiers respectively on hybrid features for experimental signals of different subjects. The proposed model outperforms in terms of cost and accuracy.     

基于动态集成的遥感图像分类

鉴于多分类器集成能够获得比单个分类器更好的性能,但是对于支持向量机(support vector ma-chine,SVM),一般的集成方法很难达到效果.特提出了基于局部精度(local accuracy,LA)的动态集成算法.首先,通过多种方法产生个体分类器;其次,利用验证数据集来定义LA,LA用来衡量各个体分类器的权重以及判断是否挑选该个体分类器的标准;最后,在某研究区的遥感图像数据集上进行实验.实验结果表明,动态集成的效果要优于静态集成,特别是异类动态集成效果最好.静态集成只考虑了分类器在训练样本中的表现而没有考虑测试样本的特征,对于动态集成,可以根据测试样本在验证集上的表现来选择个体分类器,因此它展现出更好的性能.     

Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.     

Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection

The electroencephalogram (EEG) is the frequently used signal to detect epileptic seizures in the brain. For a successful epilepsy surgery, it is very essential to localize epileptogenic area in the brain. The signals from the epileptogenic area are focal signals and signals from other area of the brain region nonfocal signals. Hence, the classification of focal and nonfocal signals is important for locating the epileptogenic area for epilepsy surgery. In this article, we present a computer aided automatic detection and classification method for focal and nonfocal EEG signal. The EEG signal is decomposed by Dual Tree Complex Wavelet Transform (DT‐CWT) and the features are computed from the decomposed coefficients. These features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system achieves 98% sensitivity, 100% specificity, and 99% accuracy for EEG signal classification. The experimental results are presented to show the effectiveness of the proposed classification method to classify the focal and nonfocal EEG signals. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 277–283, 2016     

基于支持向量机的印品缺陷分类方法

目的研究印品图像的各类形状缺陷,建立基于支持向量机(Support vector machine,SVM)的印品形状缺陷分类模型。方法对印品进行符合人眼视觉特性的缺陷识别,并对提取缺陷进行特征分析。将特征数据导入支持向量机进行训练学习,SVM分类器对缺陷图像进行测试。结果分类器对点缺陷和面缺陷的识别率为100%,对线缺陷的分类准确率达93.94%。结论基于SVM的缺陷分类方法能较好地满足印品质量检测的需求。     

三种鱼类发声信号频带能量特征提取与分类研究

海洋渔业资源的开发和利用对经济鱼类的分类识别提出了迫切的技术需求.根据鱼类的不同发声特征,文章采用有监督机器学习方法实现了三个不同鱼种发声信号的分类.基于小波包分解技术提取了黄花鱼、大米鱼和黄姑鱼三种鱼类发声信号的频带能量特征,并利用不同的分类器进行分类.结果表明:三种鱼的发声信号频率主要集中在300～800 Hz之间...     

Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.     

Fractional gravitational search-radial basis neural network for bone marrow white blood cell classification

Automotive image segmentation systems are becoming an important tool in the medical field for disease diagnosis. The white blood cell (WBC) segmentation is crucial, because it plays an important role in the determination of the diseases and helps experts to diagnose the blood disease disorders. The precise segmentation of the WBCs is quite challenging because of the complex contents in the bone marrow smears. In this paper, a novel neural network (NN) classifier is proposed for the classification of the bone marrow WBCs. The proposed NN classifier integrates the fractional gravitation search (FGS) algorithm for updating the weight in the radial basis function mapping for the classification of the WBC based on the cell nucleus feature. The experimentation of the proposed FGS-RBNN classifier is carried on the images collected from the publically available dataset. The performance of the proposed methodology is evaluated over the existing classifier approaches using the measures accuracy, sensitivity, and specificity. The results show that the classification using the nucleus features alone can be utilized to achieve the classification with the better accuracy. Moreover, the classification performance of the proposed FGS-RBNN is better than the existing classifiers, and it is proved to be the efficacious classifier with a classification accuracy of 95%.     

Timing and Classification of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset, and time duration for the Transversus Abdominus (TrA), Vastus Medialis Obliquus (VMO), Gluteus Medius (GM), Vastus Lateralis (VL), and Multifidus Muscles (ML) were acquired to construct a classification model. The proposed classification model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space. The activation feature space of muscle timing is used to train several large margin classifiers to modulate muscle activations and account for such activation measurements. The fast large margin classifier achieved higher performance and faster convergence than support vector machines (SVMs) and other state-of-the-art classifiers. The proposed sEMG classification framework achieved an average accuracy of 98.8% after 7 s training time, improving other classification techniques in previous literature.     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

基于GST-ECNN的运动想象脑电信号识别方法

在对脑电信号的解码研究中,存在着现有时频分析方法对高频信号处理能力有限,多通道信号信息冗余,常用卷积神经网络分类器ReLU激活函数受学习速率的影响较大,对不同层采用相同的正则化很难获得满意结果等问题。为此,提出了一种基于广义S变换特征提取和增强卷积神经网络分类相结合的方法,同时提出一种结合Relief算法和向前选择搜索策略的包裹式方法进行通道选择。结果表明,提出的方法利用较少的信号通道,具有更强的特征提取和分类的能力,在第Ⅳ届BCI的数据集I上取得最高98.44±1.5%的分类准确率,高于其他现有算法。该方法良好的分类性能不仅减少了计算消耗,也有效提高了分类准确率,对脑电信号特征提取和分类具有一定的参考意义。