面向特征融合的脑卒中脑电信号分类方法

为了实现脑卒中患者中脑梗死、脑出血两类疾病脑电信号的高效分类与检测,提出了一种基于小波包能量与近似熵特征提取结合的脑电自动分类预测方法。将输入的脑卒中病人的脑电信号进行小波包分解,提取各个频段的能量并降维,而后与近似熵融合作为特征向量,并用支持向量机算法对其进行分类。研究结果表明该方法有较强的脑电特征分类识别能力,进一步单独提取原始脑电信号α波段的信号进行分类,得到了更优的分类效果,脑卒中脑电信号的分类准确率可以达到98.36%。这对临床上实现脑卒中疾病的智能预测具有辅助决策作用。     

基于新型能量算子和多尺度熵的癫痫自动检测

提出癫痫检测的新算法,创新地使用新型能量算子和多尺度熵,不仅能够实时跟踪脑电信号能量,而且可以在脑电信号未知情况下提取其有效的特征信息。对脑电信号进行一定尺度的小波变换;利用新型Teager能量算子和多尺度熵提取脑电信号的非线性特征,将特征信号输入到极限学习机中,用来识别正常脑电信号与癫痫脑电信号。实验结果表明,此方法具有高分类精度,可达到98.89%,优于文献中所报道的多种检测方法,可以应用于癫痫临床检测。     

脑电信号的小波变换和样本熵特征提取方法

针对现有的采用单一的特征提取算法对运动想象脑电信号识别率不高的问题,提出一种结合小波变换和样本熵的特征提取方法.通过小波变换,把脑电信号进行3层分解,抽取出对应于脑电β节律频带的小波系数的能量均值和能量均值差,并结合脑电信号的样本熵组成特征向量,使用支持向量机分类器对左右手运动想象脑电信号进行分类.结果表明,结合小波变换和样本熵的特征提取方法明显优于仅采用小波变换、样本熵以及其他传统的特征提取方法,得到的最高正确识别率为91.43%.     

小波包熵和BP神经网络在意识任务识别中的应用

探索了小波包熵和BP神经网络在识别左右手想象运动中的作用.采用脑-机接口2003竞赛中Graz科技大学提供的脑电数据,计算C3、C4电极8～16Hz频带脑电信号的小波包熵,将其作为反应想象左右手运动的特征量,用BP神经网络对大脑想象左右手运动任务进行分类,最大分类正确率可达88.57%,与使用线性判别式算法分类结果相比,效果更好.脑电信号小波包熵随时间的变化与事件相关去同步和事件相关同步现象相一致,可在线识别左右手想象运动,为大脑运动意识任务的特征提取及肢残患者的临床康复提供了新思路.     

结合非线性全局特征和谱特征的脑电情感识别

针对现有表征情感信息的脑电信号的非线性特征提取不完善的问题,将相空间重构技术引入情感脑电的识别中,提取了在相空间重构下基于轨迹的描述轮廓的三种非线性几何特征作为新的情感脑电特征。结合脑电信号的功率谱熵以及非线性属性特征（近似熵、最大Lyapunov指数、Hurst指数）,提出了基于主成分分析（PCA）的非线性全局特征（非线性几何特征+非线性属性特征）和功率谱熵的融合算法,以支持向量机（SVM）为分类器进行情感识别。结果显示,非线性全局特征能更有效地实现情感识别,二分类情感识别率约90%左右。基于PCA的融合情感特征相比单一特征能达到更佳的情感识别性能,四分类实验中平均识别率可达86.42%。结果表明,非线性全局特征相比非线性属性特征情感识别率有所提高,非线性全局特征以及功率谱熵的结合可以构造出更佳的情感脑电特征参数。     

基于样本熵的运动想象分类研究

提出了基于脑电的样本熵特征进行运动想象分类的思想,分析了左右手运动想象时感觉运动皮层的脑电信号样本熵及其动态变化规律．结果表明,样本熵能够较好地反映左右手运动想象时脑电特征的变化,具有明确的生理意义．在此基础上,利用Fisher线性分类器对基于样本熵的左右手运动想象进行了动态分类,得到的平均最大分类正确率达到87.8%．最后,提出了一种样本熵的快速算法,其计算量小、速度快,满足BCI实时系统要求．     

多任务运动想象脑电特征的融合分类研究

针对运动想象脑-机交互任务模式单一、识别精度低、实用性较差等问题,采用改进的共空间模式（CSP）的特征提取方法,并利用支持向量机（SVM）与CSP融合分类方法对多类任务运动想象脑电特征进行分类识别。首先,选择特定导联上的脑电信号进行小波分解与重构,去除冗余信息;其次,利用特征参数做差的方法,得到较为明显的脑电特征;最后,通过SVM融合CSP的分类模式,对脑电特征进行多任务分类。利用BCI竞赛数据,对左手,右手,舌和脚四类运动想象任务的脑电进行识别。结果表明：分类正确率最高达到90.9%,平均正确率为86.8%,Kappa系数为0.8867,信息传输速率可达0.68 bit/trial,能够有效的获得脑电特征并较好的实现多任务运动想象脑电识别。     

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MSE-PCA的脑电睡眠分期方法研究

针对传统的自动睡眠分期准确率不足问题,提出一种将多尺度熵(MSE)和主成分分析(PCA)联合使用的自动睡眠分期方法。以8例受试者睡眠脑电(EEG)监测数据及专家人工分期结果作为样本,首先使用MSE表征受试者脑电信号不同睡眠期的非线性动力学特征;然后使用PCA的前两个主成分向量代替MSE特征进行降维,实现降低数据冗余的同时保留绝大多数EEG非线性特征;最终将新向量的特征参数输入到反馈神经网络(BPNN)分类器中实现MSE-PCA模型的脑电睡眠状态的自动识别分类。实验结果表明,自动分期准确率可达到87.9%,kappa系数0.77,该方法能提高脑电自动睡眠分期系统的准确率和稳定性。     

多类运动想象脑电信号特征提取与分类

针对多类运动想象情况下存在的脑电信号识别正确率比较低的问题,提出了一种基于小波包特定频段的小波包方差,小波包熵和共同空间模式相结合的脑电信号特征提取的方法,并将特征向量输入到支持向量机中达到分类的目的;首先选择重要导联的脑电信号,进行特定频段的小波包去噪和分解;其次对通道优化的重要导联的每个通道信号计算小波包方差和小波包熵值作为特征向量;然后对所有重要导联的分解系数重构并进行共同空间模式特征提取;最后结合2种不同导联方式所获取的特征向量作为分类器的输入进行分类;采用BCI2005desc_IIIa中l1b数据进行验证,该算法的分类正确率最高达到88.75%,相对2种单一的提取方法分别提高28.27%和6.55%;结果表明该算法能够有效提取特征向量,进而改善多类识别正确率较低的问题。     

Discrete harmony search based expert model for epileptic seizure detection in electroencephalography

Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and highest accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet packet transform (DWT) with energy, entropy, standard deviation, mean, kurtosis, skewness and entropy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN). Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till sixth-level using DWT packet. Discrete harmony search with modified differential operator was used to select the optimal features out of all above mentioned statistical and non-statistical parameters. In order to demonstrate the efficacy of the proposed algorithm for classification purpose using PNN, we have implemented 10-fold cross validation. Clinical EEG data recorded from normal as well as epileptic subjects are used to test the performance of this new scheme. It is found that the detection rate is 100% accurate with same level of sensitivity and specificity.     

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%).     

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。     

Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection

In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic seizure detection is proposed. The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor (k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate using the top-ranked discriminative rules from the HKB. The data set is taken from a publicly available EEG database which aims to differentiate healthy subjects and subjects suffering from epilepsy diseases. Experimental results show the efficiency of our proposed system. The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.     

基于小波包和组合分类器的脑电信号分类

为了提高脑思维任务分类精度,提出了一种基于小波包分解和多分类器投票组合的运动想象任务分类方法。该方法利用小波包分解对经过预处理的脑电信号进行分解,提取所有频带上的相对小波包能量特征;根据不同脑思维任务下左右半脑各通道间的差异性对C3、C4两通道求取特定频带上的小波包系数的L-2范数作为特征;采用基于投票策略的组合分类器对两种联合特征进行分类,得到了92.85%的识别精度。实验结果表明,联合特征向量较好地反映了左右手运动想象脑电信号的事件相关去同步（ERD）和事件相关同步（ERS）的本质特性;组合分类器识别效果优于单一分类器。     

集成胶囊网络的脑电情绪识别

为了充分提取脑电信号多频带的时频信息和保留导联空间分布的位置信息,提出了一种基于集成胶囊网络的情绪识别模型.对预处理过的脑电信号进行小波包特征提取,并将Theta、Alpha、Beta、Gamma四个频带的小波系数能量值填充于根据导联空间分布映射的稀疏矩阵中,拼接构成多频带特征矩阵,通过胶囊网络对特征数据进行训练,对不...     

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.     

多特征融合的脑电情绪分类

为进一步探究不同类型特征互补性对脑电情绪分类的影响,提出一种基于多特征融合的脑电情绪分类新方法。对预处理后的脑电信号进行DE、MST和SampEn特征提取,采用双样本T检验去除冗余筛选出最优特征并融合,采用SVM分类模型来识别不同的情绪状态。在SEED-Ⅳ数据集上的实验结果表明,单一特征中DE的平均分类准确率最高（77.86%）,而融合非线性SampEn特征与功能连接MST属性后平均分类准确率得到进一步提升（84.58%）,不同时间段采集的数据上重测实验则证明了该方法的有效性与稳定性。     

基于图卷积网络的运动想象识别

运动想象识别将大脑的神经活动信号转为编码输出以实现意念控制,是脑机接口的一个重要研究方向.近年来深度学习算法的应用进一步提高了运动想象识别的准确率,但是当前基于深度学习的运动想象分析都将多路脑电信号作为二维矩阵信号,忽视了不同节点的空间关联信息.为了解决这个问题,将图卷积网络算法应用到运动想象分类中,通过多个节点脑电信...