Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG

One of the challenges in developing a Brain Computer Interface (BCI) is dealing with the high dimensionality of the data when extracting features from EEG signals. Different feature selection algorithms have been proposed to overcome this problem but most of them involve complex transformed features, which require high computation and also result in increasing size of the feature set. In this paper, we present a new hybrid method to select features that involves a Differential Evolution (DE) optimization algorithm for searching the feature space to generate the optimal feature subset, with performance evaluated by a classifier. We provide a comprehensive study of the significance of evolutionary algorithm in selecting the best features for EEG signals. The BCI competition III, dataset IVa has been used to evaluate the method. Experimental results demonstrate that the proposed method performs well with Support Vector Machine (SVM) classifier, with an average classification accuracy of above 95% with a minimum of just 10 features. We also present a comparison of Differential Evolution (DE) with other evolutionary algorithms, and the results show the superiority of DE which implies that, with the selection of a good searching algorithm, a simple Common Spatial Pattern filter features can produce good results.     

基于模糊分割的支持向量机分类器

支持向量机算法对噪声点和异常点是敏感的,为了解决这个问题,人们提出了模糊支持向量机,但其中的模糊隶属度函数需要人为设置。提出基于模糊分割的支持向量机分类器。在该算法中,首先根据聚类有效性用模糊c-均值聚类分别对训练集中的正负类数据聚类;然后,选择距离最近的c个聚类对构成c个二分类问题;最后,对c个二分类器用加权平均策略得到最终分类结果。为了验证所提算法的有效性,对三个UCI数据集进行了数值实验,结果表明,该算法能有效提高带噪声点和异常点数据集分类的预测精度。     

异步BCI系统中运动想象诱发脑电特征识别

针对异步运动想象脑机交互（Brain Computer Interface,BCI）系统中空闲状态检测和不同想象任务分类的问题,在小波变换提取脑电信号特征基础上,设计了阈值判别结合支持向量机的二级分类器。由于大脑想象单侧肢体运动时,会导致同侧和对侧运动皮层脑区EEG信号在μ节律上分别出现事件相关同步和去同步,而大脑处于空闲状态时则无此现象。基于大脑活动的这一特性,提出了小波能量阈值判别法,进行空闲状态检测,径向基核函数和交叉检验的支持向量机方法,进行左、右手运动想象任务分类。结果表明该分类器最佳分类正确率达到了80.7%,且整个时间消耗仅为3.0 s,可以较好地满足异步在线运动想象BCI系统的应用。     

基于皮尔逊最优电极选择的ADHD患者脑电特征提取及分类研究

事件相关电位(ERP)可用于注意缺陷多动障碍儿童(ADHD)和正常儿童的脑电特征 提取与分类。首先,采用赌博任务范式,采集2 类儿童的脑电信号;其次,基于皮尔逊相关系 数算法选择最优电极,并预处理最优电极脑电信号;然后,提取预处理脑电信号的时域特征(均 值、方差、峰值)和频域特征(Theta 波段功率、Alpha 波段功率);最后,利用传统分类方法支持 向量机(SVM)、自适应增强(AdaBoost)、自举汇聚法(Bagging)、线性判别式分析(LDA)、反向传 播(BP)和组合分类器的分类方法(LDA-SVM,BP-SVM)完成对2 种脑电信号的分类。研究结果 表明,传统方法BP 分类器的分类准确率可达80.52%,组合分类器BP-SVM 的分类准确率可达 88.88%。组合分类方法能提高ADHD 儿童的分类准确率,为基于脑机接口技术的ADHD 神经 反馈康复治疗提供技术支持。     

High performance EEG signal classification using classifiability and the Twin SVM

Classification of Electroencephalogram (EEG) data for imagined motor movements has been a challenge in the design and development of Brain Computer Interfaces (BCIs). There are two principle challenges. The first is the variability in the recorded EEG data, which manifests across trials as well as across individuals. Consequently, features that are more discriminative need to be identified before any pattern recognition technique can be applied. The second challenge is in the pattern recognition domain. The number of data samples in a class of interest, e.g. a specific action, is a small fraction of the total data, which is composed of samples corresponding to all actions of all users. Building a robust classifier when learning from a highly unbalanced dataset is very difficult; minimizing the classification error typically causes the larger class to overwhelm the smaller one. We show that the combination of ‘classifiability’ for selecting the optimal frequency band and the use of the Twin Support Vector Machine (Twin SVM) for classification, yields significantly improved generalization. On benchmark BCI Competition datasets, the proposed approach often yields up to 20% improvement over the state-of-the-art.     

基于模糊分割和邻近对的支持向量机分类器

支持向量机算法对噪声点和异常点是敏感的,为了解决这个问题,人们提出了模糊支持向量机,但其中的模糊隶属度函数需要人为设置。提出基于模糊分割和邻近对的支持向量机分类器。在该算法中,首先根据聚类有效性用模糊c-均值聚类算法分别对训练集中的正负类数据聚类;然后,根据聚类结果构造c个二分类问题,求解得c个二分类器;最后,用邻近对策略对样本点进行识别。用4个著名的数据集进行了数值实验,结果表明该算法能有效提高带噪声点和异常点数据集分类的预测精度。     

Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces

Neural networks (NNs) can be deployed in many different ways in signal processing applications. This paper illustrates how neural networks are employed in a prediction based preprocessing framework, referred to as neural-time-series-prediction-preprocessing (NTSPP), in an electroencephalogram (EEG)-based brain-computer interface (BCI). NTSPP has been shown to increase feature separability by mapping the original EEG signals via time-series-prediction to a higher dimensional space. Preliminary results of a similar novel framework are also presented where, instead of using predictive NNs, auto-associative NNs are employed and features are extracted from the output of auto-associative NNs trained to specialize on EEG signals for particular brain states. The results show that this preprocessing framework referred to as auto-associative NN preprocessing (ANNP) also has the potential to improve the performance of BCIs. Both the NTSPP and ANNP are compared with and deployed in conjunction with the well know common spatial patterns (CSP) to produce a BCI system which significantly outperforms either approach operating independently and has the potential to produce good performances even with a lower number of EEG channels compared to a multichannel BCI. Multichannel BCIs normally perform better that 2-3 channel BCIs however reducing the number of EEG channels required can positively impact on the time needed to mount electrodes and minimize the obtrusiveness of the electrode montage for the user. It is also shown that NTSPP can improve the potential for employing existing BCI methods with minimal subject-specific parameter tuning to deploy the BCI autonomously. Results are presented with six different classification approaches including various statistical classifiers such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and a Bayes classifier.     

运动想象脑电信号特征提取与分类算法研究

针对运动想象脑电信号特征提取困难,分类正确率低的问题,提出了利用小波熵进行特征提取并采用支持向量机（SVM）来分类的算法。计算运动想象脑电信号的功率,通过理论分析选择小波包尺度,对信号功率进行小波包分解并计算其小波包熵（WPE）,提取C3、C4导联的小波包熵插值组成特征向量,将特征向量作为分类器的输入送入支持向量机进行分类。采用国际BCI竞赛2003中的Graz数据进行验证,算法的最高分类正确率达97.56%。算法特征向量维数低、数据量小、分类正确率高,对运动想象脑电信号特征提取及分类的任务可以提供参考方法。     

基于SVM和小波分析的脑电信号分类方法

根据癫痫脑电信号与正常脑电信号波形和能量特征的不同,研究了两种的脑电信号分类方法,一种采用支持向量机SVM(Support Vector Machines)分类器对正常脑电和癫痫脑电进行分类;另一种使用小波分析和支持向量机相结合的方法对脑电进行分类,并比较了这两种方法对正常脑电和癫痫脑电分类的正确率。实验结果表明,小波分析和SVM结合的方法对脑电信号分类可以取得更好的效果,能有效区分癫痫脑电和正常脑电。     

基于因子分析模型的噪声稳健脑电信号分类方法

脑电信号的非线性、非平稳性和微弱性造成对运动想象脑电信号的分类存在特征提取困难,分类结果不理想,分类性能受噪声影响明显等问题。为此,提出了一种基于因子分析(Factor Analysis,FA)模型的噪声稳健运动脑电信号分类方法。首先利用FA模型对脑电信号中存在的噪声分量进行抑制,针对重构信号可分性较差的问题,将其转换至功率谱域,进而提取三维能够反映不同运动状态的功率谱特征,最后利用支撑向量机(Support Vector Machine,SVM)分类器对所提特征向量进行分类判决。基于Graz数据的验证实验表明,所提方法可以明显提升低信噪比条件下的分类性能,在实际工程应用中具备较强的推广泛化能力。     

A hybrid expert system approach for telemonitoring of vocal fold pathology

Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology.     

基于分数阶傅里叶变换的运动脑电信号分类方法

运动想象脑电信号作为一种典型的非线性、非平稳信号,在传统基于单一特征提取的分类方法中难以取得理想的分类性能。针对该问题,将分数阶傅里叶变换(Fractional Fourier Transform, FrFT)引入到脑电信号特征提取过程中。首先利用FrFT对信号进行分析,在扩展特征域的同时从不同维度提取信号中的有用信息并构成特征向量,然后利用支持向量机(Support Vector Machine, SVM)分类器对所提取的特征向量进行分类,最后采用Graz数据开展实验。实验结果表明所提方法能够获得高达92.57%的正确分类结果,明显高于传统采用单一特征提取的分类方法。     

Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

支持向量机在脑电信号分类中的应用

首先采用小波变换提取精神分裂症与健康人的脑电信号频率和空间的能量特征,然后用基于统计学习理论的支持向量机(SVM)分类器进行训练和分类测试,并比较了不同核函数和参数对脑电信号分类正确率的影响,最后与RBF神经网络的分类能力进行了实验比较。试验结果表明,利用基于支持向量机和能量特征的方法实现对脑电信号的分类可以取得理想的效果,精神分裂症患者和健康人的16导脑电信号在能量特征上表现出较高的模式可分性。这种分类方法在精神分裂症患者的病理诊断中具有一定的应用价值。     

An Ordinal Multi-Dimensional Classification (OMDC) for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed. Although machine learning techniques have been frequently implemented in this area, the existing studies disregard to the natural order between the target attribute values of the historical sensor data. Thus, these methods cause losing the inherent order of the data that positively affects the prediction performances. To deal with this problem, a novel approach, named Ordinal Multi-dimensional Classification (OMDC), is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values. To demonstrate the prediction ability of the proposed approach, eleven different multi-dimensional classification algorithms (traditional Binary Relevance (BR), Classifier Chain (CC), Bayesian Classifier Chain (BCC), Monte Carlo Classifier Chain (MCC), Probabilistic Classifier Chain (PCC), Classifier Dependency Network (CDN), Classifier Trellis (CT), Classifier Dependency Trellis (CDT), Label Powerset (LP), Pruned Sets (PS), and Random k-Labelsets (RAKEL)) were implemented using the Ordinal Class Classifier (OCC) algorithm. Besides, seven different classification algorithms (Multilayer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Decision Tree (C4.5), Bagging, Random Forest (RF), and Adaptive Boosting (AdaBoost)) were chosen as base learners for the OCC algorithm. The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy. Also, it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.     

Vicinal support vector classifier: A novel approach for robust classification based on SKDA

In this paper, we present a detailed study and comparison of different classification algorithms. Our main purpose is the study of the Vicinal Support Vector Classifier (VSVC) and its relations to the other state-of-the-art classifiers. To this end, we start by the historical development of each classifier, derivation of the mathematics behind it and describing the relations that exist between some of them, in particular the relation between the VSVC and the other classifiers. Thereafter, we apply them to two famous learning datasets very used by the research community, namely the MIT-CBCL face and the Wisconsin Diagnostic Breast Cancer (WDBC) datasets. We show that despite its simplicity compared to the other state-of-the-art classifiers, the VSVC leads to very robust classification results and provide some practical advantages compared to the other classifiers.     

Similarity-based attribute weighting methods via clustering algorithms in the classification of imbalanced medical datasets

In the fields of pattern recognition and machine learning, the use of data preprocessing algorithms has been increasing in recent years to achieve high classification performance. In particular, it has become inevitable to use the data preprocessing method prior to classification algorithms in classifying medical datasets with the nonlinear and imbalanced data distribution. In this study, a new data preprocessing method has been proposed for the classification of Parkinson, hepatitis, Pima Indians, single proton emission computed tomography (SPECT) heart, and thoracic surgery medical datasets with the nonlinear and imbalanced data distribution. These datasets were taken from UCI machine learning repository. The proposed data preprocessing method consists of three steps. In the first step, the cluster centers of each attribute were calculated using k-means, fuzzy c-means, and mean shift clustering algorithms in medical datasets including Parkinson, hepatitis, Pima Indians, SPECT heart, and thoracic surgery medical datasets. In the second step, the absolute differences between the data in each attribute and the cluster centers are calculated, and then, the average of these differences is calculated for each attribute. In the final step, the weighting coefficients are calculated by dividing the mean value of the difference to the cluster centers, and then, weighting is performed by multiplying the obtained weight coefficients by the attribute values in the dataset. Three different attribute weighting methods have been proposed: (1) similarity-based attribute weighting in k-means clustering, (2) similarity-based attribute weighting in fuzzy c-means clustering, and (3) similarity-based attribute weighting in mean shift clustering. In this paper, we aimed to aggregate the data in each class together with the proposed attribute weighting methods and to reduce the variance value within the class. Thus, by reducing the value of variance in each class, we have put together the data in each class and at the same time, we have further increased the discrimination between the classes. To compare with other methods in the literature, the random subsampling has been used to handle the imbalanced dataset classification. After attribute weighting process, four classification algorithms including linear discriminant analysis, k-nearest neighbor classifier, support vector machine, and random forest classifier have been used to classify imbalanced medical datasets. To evaluate the performance of the proposed models, the classification accuracy, precision, recall, area under the ROC curve, κ value, and F-measure have been used. In the training and testing of the classifier models, three different methods including the 50–50% train–test holdout, the 60–40% train–test holdout, and tenfold cross-validation have been used. The experimental results have shown that the proposed attribute weighting methods have obtained higher classification performance than random subsampling method in the handling of classifying of the imbalanced medical datasets.

     

Two-phase classification: ANN and A-SVM classifiers on motor imagery BCI

Brain–Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor mental activity with the ultimate objective of allowing people to communicate with computers only via their thoughts. Users must create precise cerebral activity patterns that the system uses as control signals to do this. A common activity used to elicit such signals is Motor Imagery (MI), in which certain signals are created in the sensorimotor cortex while imagining the movements. The three phases of the traditional EEG–BCI processing pipeline are preprocessing, feature extraction, and classification. We provide categorization advances and track performance gains in 4-class MI-based BCIs. In this study, 4-class MI events are produced via an illusory elevation of the left hand, right hand, feet, and tongue. Finally, a two-phase classification technique is provided with ANN classifiers being used in the first phase to discriminate between different pair-wise MI tasks. Secondly, an adaptive SVM classifier is used to assess the user's end task based on the weighted outputs of the classifiers. An adaptive classifier is one technique to maintain consistency in performance, reduce training time, and eliminate non-stationaries, all of which are required for efficient BCI performance. The suggested approach outperformed conventional two-stage classification algorithms on MI data, according to experimental findings. The average classification accuracy of this technique is 96% for datasets BCI competition IV 2a. This is a 4% improvement over the comparison approach.     

Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm

Brain–computer interfacing is an emerging field of research where signals extracted from the human brain are used for decision making and generation of control signals. Selection of the right classifier to detect the mental states from electroencephalography (EEG) signal is an open area of research because of the signal’s non-stationary and Ergodic nature. Though neural network based classifiers, like Adaptive Neural Fuzzy Inference System (ANFIS), act efficiently, to deal with the uncertainties involved in EEG signals, we have introduced interval type-2 fuzzy system in the fray to improve its uncertainty handling. Also, real-time scenarios require a classifier to detect more than two mental states. Thus, a multi-class discriminating algorithm based on the fusion of interval type-2 fuzzy logic and ANFIS, is introduced in this paper. Two variants of this algorithm have been developed on the basis of One-Vs-All and One-Vs-One methods. Both the variants have been tested on an experiment involving the real-time control of robot arm, where both the variants of the proposed classifier, produces an average success rate of reaching a target to 65% and 70% respectively. The result shows the competitiveness of our algorithm over other standard ones in the domain of non-stationary and uncertain signal data classification.     

基于PCA融合PSO-SVM的运动想象脑电信号分类方法

运动想象脑电信号的分类识别是当前脑机接口(BCI)技术面临的难点。针对该问题,提出一种融合主成分分析(PCA)和粒子群优化-支撑向量机(PSO-SVM)的运动想象脑电信号分类方法。首先利用PCA对采集到的高维脑电信号进行分析,剔除其中噪声分量并提取三维反应不同脑电信号差异特性的特征向量。然后利用SVM对特征向量进行分类,同时针对SVM分类性能受核参数影响较大的问题,利用PSO算法的全局寻优能力对其进行优化,从而提升SVM的分类性能。最后采用BCI竞赛中所用Graz数据进行实验,结果表明所提的PCA融合PSO-SVM方法可以获得95.3%的分类性能,在低信噪比条件下具有鲁棒性和较高的应用前景。