脑-机接口技术综述

脑-机接口是在人脑与计算机或其它电子设备之间建立的直接的交流和控制通道,通过这种通道,人就可以直接通过脑来表达想法或操纵设备,而不需要语言或动作,这可以有效增强身体严重残疾的患者与外界交流或控制外部环境的能力,以提高患者的生活质量.脑-机接口技术是一种涉及神经科学、信号检测、信号处理、模式识别等多学科的交叉技术.本文对脑-机接口技术的发展、研究现状、工作原理以及涉及的关键技术进行了较为详细地综述,在总结脑-机接口技术存在问题的基础上,探讨了该领域进一步研究的方向.     

Real-Time Brain-Computer Interface System Based on Motor Imagery

A brain-computer interface (BCI) realtime system based on motor imagery translates the user's motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.     

在线脑机接口中脑电信号的特征提取与分类方法

在脑机接口研究中,针对运动想象脑电信号的特征抽取,提出了一种基于离散小波变换和AR模型的方法.利用Daubechies类小波函数对脑电信号进行3层分解,抽取小波变换系数的统计特征;利用Burg算法提取脑电信号6阶AR模型系数.将这两类特征进行组合后使用神经网络、支持向量机、马氏距离线性判别进行分类并比较分析.采用BCI...     

基于粗糙集离散化的多频带脑电特征选择方法的研究

不同的受试在进行运动想象时,脑电模式在频带分布上的差异较大,只有找到特定受试的有效特征,才能得到较好的实验效果。文中结合共同空间模型和粗糙集离散化算法的特征选择方法,来选取受试左右手运动想象的多频带脑电特征。与单频带特征相比,文中提出的方法提取的多频带脑电特征,能够有效的剔除了冗余特征量的干扰。实验结果表明(五位受试),文中提出的方法可以有效提高分类准确率。     

基于运动想象脑机接口的AuFBCSP方法

针对基于运动想象的脑机接口问题,提出了一种新的特征提取方法,即加强的滤波带宽共同空间模式方法。与传统的滤波带宽共同空间模式方法相比,文中提出的方法充分考虑了被试在进行运动想象时发生事件相关去同步时段的特异性,从而得到更多的特征,并利用这些特征分类,取得更高的准确率和kappa值。在特征的选择上,利用BCI的竞赛数据,将基于互信息的方法和基于准确率的方法进行对比,发现基于准确率的特征选择方法优于基于互信息的特征选择方法。     

模糊多类SVM模型

利用SVM处理多类分类问题,是当前的研究热点之一.本文提出了一种模糊多类支持向量机模型,即FMSVM.该方法是在Weston等人提出的多类SVM模型中引入模糊成员函数,针对每个输入数据对分类结果的不同影响,该模糊成员函数得到相应的值,由此得到不同的惩罚值.从而在构造分类超平面时,可以忽略那些对分类结果影响很小的数据.理论分析与数值实验都表明,该算法具有良好的鲁棒性.     

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery (MI) brain-computer interface (BCI) system. In this paper, we propose a common spatial pattern (CSP) and chaotic particle swarm optimization (CPSO) twin support vector machine (TWSVM) scheme for classification of MI electroencephalography (EEG). The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine (SVM), particle swarm optimization (PSO) TWSVM, linear discriminant analysis LDA), back propagation (BP) and probabilistic neural network (PNN), respectively. Furthermore, it achieved a faster or comparable central processing unit (CPU) running time over the traditional SVM methods.     

Hadamard 纠错码结合支持向量机在多分类问题中的应用

多分类问题一直是模式识别领域的一个热点,本文提出了将Hadamard纠错码同二元分类器相结合的方法来解决此问题,相对于其它类型的纠错码多分类器法,该方法的实现简单快捷,且更容易构造出性能优越的纠错码本.本文将Hadamard纠错码和支持向量机相结合,应用于说话人辨认这样一个多分类问题中,并同传统的"1对余"的多类推广方式进行了比较.实验结果表明在多分类任务中,Hadamard纠错码对于不同的类别都表现出了很强的分类能力,且性能优于"1对余"法,对于类间码字的不同分配方式也具有良好的鲁棒性.     

支持向量鉴别分析及在人脸表情识别中的应用

模式识别一般首先要对数据进行降维,PCA和LDA及其对应的核化算法是其中应用广泛的方法,但这些算法的应用前提是假设样本数据为高斯分布,在少样本训练时它们的推广性能有很大局限.本文提出了一种基于支持向量机的鉴别分析算法,该算法首先寻找有限样本情况下最优分类面,以其法线方向为投影轴对数据进行投影降维,在多类情况下提供了极其丰富的方案选择投影轴.该算法体现了支持向量机的内在优良推广性能,克服了PCA和LDA等算法的局限性.本文将所提算法应用于人脸表情特征提取,并与PCA、LDA、KPCA、GDA等算法进行了比较,结果表明该算法的有效性.     

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Common spatial pattern (CSP) algorithm is a successful tool in feature estimate of brain-computer interface (BCI). However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble (CSPE) classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.     

Probabilistic Support Vector Machine Localization in Wireless Sensor Networks

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)‐based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.     

KFDA and clustering based multiclass SVM for intrusion detection

To improve the classification accuracy and reduce the training time, an intrusion detection technology is proposed, which combines feature extraction technology and multiclass support vector machine （SVM） classification algorithm. The intrusion detection model setup has two phases. The first phase is to project the original training data into kernel fisher discriminant analysis （KFDA） space. The second phase is to use fuzzy clustering technology to cluster the projected data and construct the decision tree, based on the clustering results. The overall detection model is set up based on the decision tree. Results of the experiment using knowledge discovery and data mining （KDD） from 99 datasets demonstrate that the proposed technology can be an an effective way for intrusion detection.     

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.     

基于主成分分析的支持向量机回归预测模型

首先利用主成分分析法降低样本数据的维数,建立主成分的多元回归预测模型,其次利用支持向量机方法确定回归模型的系数,最后实例说明了该模型具有较高预测精度.     

一种基于FPGA的脑电分类算法实现

通过对脑电信号特征的分析,利用小波变换的多尺度分析技术对脑电信号进行特征提取,进而使用主成分分析算法对特征进行降维,并对降维后的信号使用Fisher线性判别方法进行分类。最后,利用VerilogHDL硬件编程语言设计实现了Mallat分解算法、PCA算法和LDA算法模块,并在FPGA应用板上实现了脑电分类功能。系统对2008年BCI大赛的数据进行了测试,分类准确率达到92.31%,表明该方法对开发便携式脑机接口系统具有良好的应用价值。     

基于Gabor小波和SVM的人脸表情识别算法

针对包含表情信息的静态图像,提出基于Gabor小波和SVM的人脸表情识别算法。根据先验知识,并使用形态学和积分投影相结合定位眉毛眼睛区域,采用模板内计算均值定位嘴巴区域,自动分割出表情子区域。对分割出的表情子区域进行Gabor小波特征提取,在利用Fisher线性判别对特征进行降维,去除冗余和相关。利用支持向量机对人脸表情进行分类。用该算法在日本表情数据库上进行测试,获得了较高的识别准确率。证明了该算法的有效性。     

3T‐FASDM: Linear discriminant analysis‐based three‐tier face anti‐spoofing detection model using support vector machine

In recent years, to solve the problem of face spoofing, momentous work has been done in this field, but still, there is a need for establishing counter measures to the biometric spoofing attacks. Although trained and evaluated on different databases, impressive results have been achieved in existing face anti‐spoofing techniques, but biometric authentication is a very significant problem as imposters are using lots of reconstructed samples or fake synthetic material or structure that can be used for various attack purposes. For the first time, to the best of our knowledge, this paper explains the security for face anti‐spoofing detection using linear discriminant analysis and validates the results by calculating HTER and accuracy on different databases (i.e., REPLAY ATTACK and CASIA). The proposed model, that is, three‐tier face anti‐spoofing detection model (3T‐FASDM), is used for the detection of the fake biometric user and works well for real‐time applications. The proposed methods tested on a set of state‐of‐the‐art anti‐spoofing features for the face mode gives a very low degree of complexity as 26 general image quality measures are applied to differentiate among legitimate and imposter samples. The outcomes obtained from publically available data show that this technique has improved performance and accuracy by analyzing the HTER and machine learning classifiers that are helpful to differentiate among real and fake traits.     

基于中心矩特征的空间目标识别方法

目标的雷达散射截面（RCS）包含了丰富的目标类别信息,有效地利用目标RCS特征对空间目标的雷达识别具有重要的意义。该文利用空间目标回波的距离维信号来进行识别。中心矩特征具有平移不变性,是一种简单有效的波形特征提取算法。文中首先提取中心矩作为特征向量,再采用Fisher判据进一步进行特征压缩,最后利。用支撑矢量机（SVM）分类算法实现识别。基于实测数据的仿真实验结果表明,该方法具有较好的识别性能和推广能力。     

基于SVM的入侵检测系统中特征权重优选方法综述

基于统计学习理论的支持向量机有较好的泛化能力,然而当样本含有与该问题不完全相关甚至完全无关的特征时,会使得各个特征对问题的相关程度差异很大,为了提高分类的正确率,对各个特征进行加权尤为重要。在入侵检测系统中,网络中的特征对分类结果的影响程度也是不同的,本文列举了对这些特征进行加权的几种方法。     

基于全局和局部保持的半监督支持向量机

 支持向量机（SVM）作为正则化方法的一个特例在模式识别领域得到了成功地运用,然而传统的SVM方法作为一种有监督的学习方法主要依据最大间隔原则得到决策超平面的法向量,而并没有充分考虑样本内在的几何结构以及所蕴含的判别信息. 因此,本文将线性判别分析（LDA）的类内散度和保局投影（LPP）的基本原理引入到SVM中,提出基于全局和局部保持的半监督支持向量机：GLSSVM,该方法在继承传统的SVM方法的特点的基础上,充分考虑样本间具有的全局和局部几何结构,体现样本间所蕴含的局部和全局判别信息,同时满足作为半监督方法的必须依据的一致性假设,从而在一定程度上提高了分类精度．通过在人造数据集和真实数据集上的测试表明该方法具有上述优势．