通道选择对诱发脑电单次提取精度影响的研究

采用“模拟自然阅读”诱发电位作为人脑和计算机之间的通信载体,用支持向量机从脑电中提取诱发电位。以被试4个通道记录到的脑电信号分别作为特征,信号时程固定为300ms,时段分别取100ms~400ms、200ms~500ms和300ms~600ms。三个被试的单通道最佳分类结果分别达到95.9%（被试M,通道Cz,300ms~600ms时段）,94.3%（被试H,通道Oz,100ms~400ms时段）和93.8%（被试T,通道Oz,200ms~500ms时段）。这一结果为简化脑—机接口设计打下了良好的基础。     

基于CEEMDAN-ICA的单通道脑电信号眼电伪迹滤除方法

传统盲源分离法不能解决欠定问题,且分离信号与源信号对应关系不确定.提出一种基于自适应噪声完备经验模态分解(CEEMDAN)和独立成分分析(ICA)相结合的脑电信号眼电伪迹自动去除方法.该方法首先将含伪迹脑电信号自适应分解成多维本征模态函数(IMF),以满足盲源分离方法对信号正定或超定要求,再对本征模态函数用ICA方法构建多维源信号,最后利用模糊熵阈值判据判别多维源信号中的伪迹信号,完成滤波并重构脑电信号.该方法相比于其他算法,能更好的去除眼电伪迹并保留原始信息,适合单通道脑电信号预处理.     

单通道脑电信号眼电伪迹去除算法研究

由眨眼和眼动产生的眼电伪迹（Electrooculography,EOG）信号是脑电信号（Electroencephalography,EEG）中的主要噪声信号之一.目前,多通道脑电信号中眼电伪迹的去除算法已经较为成熟.而在单通道脑电信号的眼电伪迹去除中,由于采集通道数量较少且缺乏参考眼电信号,目前尚无十分有效的去除方法.本文提出一种基于小波变换（Wavelet transform,WT）、集合经验模态分解（Ensemble empirical mode decomposition,EEMD）和独立成分分析（Independent component analysis,ICA）的WT-EEMD-ICA单通道脑电信号眼电伪迹去除算法.实验表明:WT-EEMD-ICA算法有效地解决了单通道WT-ICA算法中的超完备问题,能够有效去除单通道脑电信号中的眼电伪迹,并且分离出的眼电伪迹成分与参考通道采集的眼电信号相关性较强.     

结合人脸图像和脑电的情绪识别技术

本文通过人脸图像和脑电两个输入信号对情绪识别技术展开研究. 采用对应不同情绪的电影片段对被实验者进行情绪刺激的方法采集输入信号. 通过表情识别出八种基本表情的分类, 通过脑电识别出情绪的三种强弱波动. 通过决策层面的信息融合, 进行情绪分类. 最终的识别准确率达到89.5%, 高于采用单模态进行识别的准确率, 分别为: 表情识别: 81.35%, 脑电识别: 71.53%.     

脑电信号特征的归一化方式与选择方法研究

针对多被试脑电数据存在被试间特征值差异较大的问题,分析了单次归一化的数据范围对分类准确率的影响。实验在情感数据集上采用6种常用的归一化方法,对所有被试的特征、单个被试的所有特征、单个被试的单个属性特征这三种单次归一化的数据范围进行准确率上的比较,证明了单个被试的单个属性特征更适于作为多被试脑电数据单次归一化数据范围。此外,提出方差贡献率与F-score结合的特征选择方法,在不降低准确率的情况下大量减少了特征数量。小波包树结点能量作为变换最少的特征得到的分类结果最好,小波包熵比脑电节律小波熵的分类准确率高。     

基于CSP和RR的多类运动想象脑电信号的识别分类研究

脑-机接口通过大脑皮层的EEG活动或者大脑里单个神经元活动使得用户可以来控制设备。这方面最具挑战性的问题之一就是如何提高脑电信号的识别精度。本文采用少通道以及共同空间模式-岭回归分析的模式识别方法,并将其应用到四种运动想象脑电的识别分类。首先对原始数据进行有效的预处理,有漂移矫正,滤波,改进的ICA(Independent Component Analysis)去除伪迹;再利用CSP(Common Space Pattern)和HHT(Hibert-Huang Transform)分别对预处理好的数据进行特征提取;最后再将两种算法提取的特征分别进行SVM(Support vector machine),LDA(Linear Discriminant Analysis)和RR(Ridge Regression)进行分类。实验结果证明,共同空间模式-岭回归分析最后的分类效果是最好的,平均分类识别率约为82.93%,数据中9名被试的最高和最低的分类识别率之间的标准差为1.37%。     

基于K近邻互信息估计的EEG伪迹消除方法

针对脑电信号中的眼电和心电串扰伪迹,提出一种基于最小相依成分分析的互信息(MILCA)算法的伪迹消除方法.在提升小波硬阈值法对多路原始脑电信号去噪基础上,运用MILCA算法对各通道信号进行盲源分离,同时采用信号间互相关系数和互信息量作为指标,分析伪迹分离程度.与Extend Infomax、FastICA 2种常见盲源分离算法的对比结果表明,运用MILCA算法对脑电信号中的眼电及心电伪迹的分离结果最理想.     

基于小波变换和FastICA的眼电伪迹去除研究

为了高效去除脑电信号(Electroencephalogram, EEG)中的眼电伪迹,文章提出一种基于小波变换(Wavelet Transform, WT)和快速独立成分分析(Fast Independent Component Analysis, FastICA)相结合的眼电伪迹去除方法。首先,应用小波变换将信号分解成不同频率的小波分量,采用适合的小波基函数和阈值针对高低频噪声做去噪处理;其次,应用FastICA算法分离出各通道的独立成分,获取纯净的脑电信号;最后,对BCI competition IV公共数据集应用融合算法,并输入支持向量机(Support Vector Machine, SVM)进行分类验证。实验结果表明,相较于单一的小波变换和FastICA算法,采用文章提出的融合算法处理后的脑电信号的SVM分类识别率分别提升了18.9%和15.8%,证明该融合算法对去除脑电信号中的眼电伪迹有较好的效果。     

能量熵与峰值窗口结合去除眼电伪迹研究

为改进传统独立分量分析自动去除眼电伪迹算法中存在识别眼电分量速度慢、需采集同步参考眼电信号、丢失脑电信号问题,提出一种不需要参考眼电信号的眼电伪迹自动识别去除方法。利用FastICA分解出独立分量,计算各独立分量频谱能量熵,以频谱能量熵值作为判据识别出眼电分量;然后使用峰值窗口分离出眼电分量中存在的脑电信号,与其他独立分量进行拼接;利用FastICA逆变换重构出去眼电伪迹的脑电信号。实验结果表明：该方法能准确快速自动地去除眼电伪迹,并较好地保留其他的脑电信号成分;频谱能量熵识别眼电伪迹平均用时为0.01?s,准确率为98％,适用于实时EOG去除。     

基于经验模态－小波包分解和卷积神经网络的 Ｐ３００ 脑电信号分类研究

针对P300脑电信号信噪比低、随机性强及个体差异性大等问题,本文提出了一种将经验模态分解（EMD）和小波包分解（WP）相结合的滤波方法,并使用改进的卷积神经网络（CNN）对脑电信号进行分类识别。首先利用经验模态分解算法将原始脑电信号分解成若干个本征模函数（IMF）分量,并对每个分量进行频谱分析以去除主频段在0~30Hz以外的分量;然后,对保留的IMF分量进行小波包分解,根据P300电位的有效时频信息,选择合适的频段进行重构,再将重构后的各个本征模函数叠加,得到经过滤波后的脑电信号;最后,设计合适的卷积神经网络结构,对P300信号进行分类识别。本文使用国际BCI竞赛数据集对提出的方法进行验证。实验结果表明,两名被试的分类准确率分别为97.78%、95.56%,说明该方法能够有效的改善P300信号的识别效果（相比其他方法至少提升了2.78%,1.39%）,为进一步提高基于P300信号的脑机接口系统的性能提供了一种新的有效的途径。     

Spectral entropy feature subset selection using SEPCOR to detect alcoholic impact on gamma sub band visual event related potentials of multichannel electroencephalograms (EEG)

The problem of analyzing and identifying regions of high discrimination between alcoholics and controls in a multichannel electroencephalogram (EEG) signal is modeled as a feature subset selection technique that can improve the recognition rate between both groups. Several studies have reported efficient detection of alcoholics by feature extraction and selection in gamma band visual event related potentials (ERP) of a multichannel EEG signal. However, in these studies the correlation between features and their class information is not considered for feature selection. This may lead to redundancy in the feature set and result in over fitting. Therefore in this study, a statistical feature selection technique based on Separability & Correlation analysis (SEPCOR) is proposed to select an optimal feature subset automatically that possesses minimum correlation between selected channels and maximum class separation. The optimal feature selection consists of a ranking method that assigns ranks to channels based on a variability measure (V-measure). From the ranked feature set of highly discriminative features, different subsets are automatically selected by heuristically applying a correlation threshold in steps from 0.02 to 0.1. These subsets are applied as input features to multilayer perceptron (MLP) neural network and k-nearest neighbor (k-NN) classifiers to discriminate alcoholic and control visual ERP. Prior to feature selection, spectral entropy features are computed in gamma sub band (30–55 Hz) interval of a 61-channel multi-trial EEG signal with multiple object recognition tasks. Independent Component Analysis (ICA) is performed on raw EEG data to remove eye blink, motion and muscle artifacts. Results indicate that both classifiers exhibit excellent classification accuracy of 99.6%, for a feature subset of 22 optimal channels with correlation threshold of 0.1. In terms of computation time, k-NN classifier outperforms multilayer perceptron-back propagation (MLP-BP) network with 7.93 s whereas MLP network takes 55 s to perform the recognition task with the same accuracy. Compared to feature section methods used in previous studies on the same EEG alcoholic database, there is a significant improvement in classification accuracy based on the proposed SEPCOR method.     

Implementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classification

One of the main purposes of brain-computer interfaces (BCI) is to provide persons of an alternative communication channel. This objective was firstly focused on handicapped subjects but nowadays its scope has increased to healthy persons. Usually, BCIs record brain activity using electroencephalograms (EEG), according to four main neuro-paradigms (slow cortical potentials, motor imagery, P300 component and visual evoked potentials). These analytical paradigms are not intuitive and are difficult to implement. Accordingly, this work researches an alternative neuro-paradigm called imagined speech, which refers to the internal pronunciation of words without emitting sounds or doing facial movements. Specifically, the present research is focused on the recognition of five Spanish words corresponding to the English words “up,” “down,” “left,” “right” and “select”, with which a computer cursor could be controlled. We perform an offline computer automatic classification procedure of a dataset of EEG signals from 27 subjects. The method implements a channel selection composed of two stages; the first one obtains a Pareto front and is approached as a multi-objective optimization problem dealing with the error rate and the number of channels; the second stage selects a single solution (channel combination) from the front, applying a fuzzy inference system (FIS). We assess the method’s performance through a channel combination and a test set not used to generate the front. Several FIS configurations were explored to evaluate if a FIS is able to select channel combinations that improve or, at least, keep the obtained accuracies using all channels for each subject’s data. We found that a FIS configuration, FIS3×3 (three membership functions for both input variables: error rate and the number of channels), obtained the best trade-off between the number of fuzzy rules and its accuracy (68.18% using around 7 channels). Also, the FIS3×3 obtained a similar statistically accuracy compared to the use of all channels (70.33%). Results of our method demonstrate the feasibility of using a FIS to automatically select a solution from the Pareto front to select channels applied to a problem of imagined speech classification. The presented method outperforms previous works in accuracy and showed a dependence relationship between EEG data and imagined words.     

最优区域共空间模式的运动想象脑电信号分类方法

共空间模式(Common Spatial Pattern,CSP)是脑机接口(Brain-Computer Interface,BCI)中一种有效的特征提取方法,然而传统CSP算法并未考虑在提取前剔除可能会影响其性能的不相关的嘈杂通道信号。所以针对不同对象的通道选择问题,提出了一种最优区域共空间模式(ORCSP)特征提取方法。首先通过欧式距离得到每个通道的附近区域,再根据方差比选择可分性最高的区域,然后采用5折交叉验证对区域内通道数目进行寻优,进而得到区分度最高的区域特征,最后使用支持向量机(SVM)进行分类。所提方法在BCI竞赛数据上进行了实验测试,并与同类型的正则化CSP和局部区域CSP算法进行了对比,在BCI Competition Ⅲ Dataset Ⅳ a数据集上达到了89.78%的平均准确率。实验结果验证了所提出方法的有效性。     

A portable HCI system‐oriented EEG feature extraction and channel selection for emotion recognition

Emotion recognition has become an important component of human–computer interaction systems. Research on emotion recognition based on electroencephalogram (EEG) signals are mostly conducted by the analysis of all channels' EEG signals. Although some progresses are achieved, there are still several challenges such as high dimensions, correlation between different features and feature redundancy in the realistic experimental process. These challenges have hindered the applications of emotion recognition to portable human–computer interaction systems (or devices). This paper explores how to find out the most effective EEG features and channels for emotion recognition so as to only collect data as less as possible. First, discriminative features of EEG signals from different dimensionalities are extracted for emotion classification, including the first difference, multiscale permutation entropy, Higuchi fractal dimension, and discrete wavelet transform. Second, relief algorithm and floating generalized sequential backward selection algorithm are integrated as a novel channel selection method. Then, support vector machine is employed to classify the emotions for verifying the performance of the channel selection method and extracted features. At last, experimental results demonstrate that the optimal channel set, which are mostly located at the frontal, has extremely high similarity on the self‐collected data set and the public data set and the average classification accuracy is achieved up to 91.31% with the selected 10‐channel EEG signals. The findings are valuable for the practical EEG‐based emotion recognition systems.     

基于事件相关电位的脑－机接口系统微弱信号分析方法研究

脑－机接口(BCI,brain computer interface)技术作为一种新型的人机交互方式,近年来受到越来越多的关注。基于听觉刺激模式诱发的事件相关电位P300建立了脑－机接口系统,主要研究微弱P300信号的单导联小样本数据的特征提取和模式识别方法。首先经过预处理和小波变换对脑电信号中的P300成分进行特征提取,建立特征向量,然后使用支持向量机的方法对特征提取结果进行模式分类。对7名受试者进行事件相关电位P300诱发实验,结果表明,系统的分类正确率可达到85％以上,且支持向量机的训练时间都在0.05 s以内。该方法可为脑－机接口的进一步应用奠定基础。     

运动想象脑电的块选择共空间模式特征提取

共空间模式(CSP)作为一种空间滤波方法已在脑电信号(EEG)的特征提取上得到了广泛应用,而对脑电信号的通道和频带进行合理选择可以有效改善共空间模式特征在运动想象脑机接口(BCI)中的分类性能.针对已有选择方法中未充分考虑通道间差异性的问题,本文提出一种对通道和频带同时进行选择的块选择共空间模式(BS–CSP)特征提取...     

基于虚拟通道的ICA的P-VEP提取方法的研究

本文介绍了独立分量分析算法(ICA)的原理、算法及其特点。采用ICA算法进行P-VEP的提取,考虑到仪器实际应用中单通道VEP信号的提取,本文通过引入虚拟通道,构造虚拟多通道,克服了ICA无法直接对单路信号进行独立分量分析的难题,实现P-VEP单通道单次的有效提取。经与目前临床通用的相干平均法比较,只需单通道、单次,在波形整体和P100潜伏期的提取上,效果显著,获得医师欢迎,很有进一步开发潜力。     

A comparative analysis of principal component and independent component techniques for electrocardiograms

Principal component analysis (PCA) is used for ECG data compression, denoising and decorrelation of noisy and useful ECG components or signals. In this study, a comparative analysis of independent component analysis (ICA) and PCA for correction of ECG signals is carried out by removing noise and artifacts from various raw ECG data sets. PCA and ICA scatter plots of various chest and augmented ECG leads and their combinations are plotted to examine the varying orientations of the heart signal. In order to qualitatively illustrate the recovery of the shape of the ECG signals with high fidelity using ICA, corrected source signals and extracted independent components are plotted. In this analysis, it is also investigated if difference between the two kurtosis coefficients is positive than on each of the respective channels and if we get a super-Gaussian signal, or a sub-Gaussian signal. The efficacy of the combined PCA–ICA algorithm is verified on six channels V1, V3, V6, AF, AR and AL of 12-channel ECG data. ICA has been utilized for identifying and for removing noise and artifacts from the ECG signals. ECG signals are further corrected by using statistical measures after ICA processing. PCA scatter plots of various ECG leads give different orientations of the same heart information when considered for different combinations of leads by quadrant analysis. The PCA results have been also obtained for different combinations of ECG leads to find correlations between them and demonstrate that there is significant improvement in signal quality, i.e., signal-to-noise ratio is improved. In this paper, the noise sensitivity, specificity and accuracy of the PCA method is evaluated by examining the effect of noise, base-line wander and their combinations on the characteristics of ECG for classification of true and false peaks.