多变量自回归和多线性主成分分析结合的多通道信号特征提取研究

脑机接口(BCI)系统通过从脑信号中提取特征对其进行识别。针对自回归模型特征提取方法和传统主成分分析降维方法处理多通道信号的局限性,本文提出了多变量自回归(MVAR)模型和多线性主成分分析(MPCA)结合的多通道特征提取方法,并用于脑磁图/脑电图(MEG/EEG)信号识别。首先计算MEG/EEG信号的MVAR模型的系数矩阵,然后采用MPCA对系数矩阵进行降维,最后使用线性判别分析分类器对脑信号分类。创新在于将传统单通道特征提取方法扩展到多通道。选用BCI竞赛IV数据集3和1数据进行实验验证,两组实验结果表明MVAR和MPCA结合的特征提取方法处理多通道信号是可行的。     

基于时-频-空间域的运动想象脑电信号特征提取方法研究

脑机接口(BCI)是在人或动物脑与外部设备间建立的直接连接通路,信号分析功能模块是其核心部分,其中特征提取算法的效果如何是脑电图(EEG)信号分析算法的关键。EEG信号本身信噪比低,传统的EEG特征提取方法存在着缺少空间信息,需要的特征量个数较多,分类正确率低等不足。针对以上问题,本文提出了一种基于小波和独立分量分析(ICA)的时间-频率-空间EEG特征的提取方法,分别用离散小波变换(DWT)和ICA提取时频域特征和空域特征。并用支持向量机(SVM)和遗传算法(GA)相结合的方法对提取的特征进行分类。实验对比结果表明,所提出的方法有效地克服了传统的时频特征提取方法空间信息描述不足等问题,对于2003年BCI竞赛数据datasetⅢ分析,最高分类正确率为90.71%。     

基于平稳小波变换的EEG视觉的信号修正

眼球运动和眨眼会在眼球周围产生电信号,这种电信号的存在直接影响到对EEG信号的分析特征提取及EEG模式的分类等研究.本文提出了一种基于小波阈值滤噪方法来修正EEG信号中出现的视觉伪信号(OA).这种用于EEG视觉伪信号处理的小波方法的实现过程如下:1)用平稳小波变换(SWT)对原始EEG信号进行处理;2)设置低频带信号的系数阈值;3)对滤噪后的信号进行重构.实验结果表明这种方法同时适用于眨眼和眼球运动产生的伪信号.最后,通过对采集的信号处理前后做了对比,说明其有效性.     

EMD和SVM结合的脑电信号分类方法

脑电(EEG)癫痫波的自动检测与分类在临床医学上具有重要意义。针对EEG信号的非平稳特点,本文提出了一种基于经验模式分解(EMD)和支持向量机(SVM)的EEG分类方法。首先利用EMD将EEG信号分成多个经验模式分量,然后提取有效特征,最后用SVM对EEG信号进行分类。结果表明,该方法对癫痫发作间歇期和发作期EEG的分类效果比较理想,识别率达到99%。     

基于TQWT的癫痫脑电信号的识别

针对癫痫脑电(EEG)信号的识别问题,提出了一种基于可调品质因子小波变换(TQWT)的脑电特征提取方法。首先,利用TQWT将EEG信号进行分解,得到各个小波子波带;然后,根据癫痫异常波对应的频率范围,合理的选择小波子波带进行重构,提取有效值和峰峰值构成特征分量;最后,采用支持向量机进行分类。将所提出方法应用于癫痫脑电信号的识别中,以德国伯恩大学癫痫研究中心采集的典型脑电数据进行验证。实验分析结果表明,所提出的特征提取方法对正常和癫痫发作期EEG信号的分类准确率可达98%。     

基于多通道经验模式分解的脑机接口特征提取

针对脑机接口(BCI)系统中的多通道非平稳脑电(EEG)信号和脑磁(MEG)信号,本文提出一种基于多通道经验模式分解(MEMD)与功率特征结合的信号特征提取算法。首先将多通道脑信号经MEMD算法分解为一系列多尺度多元固有模态函数(IMF)近似平稳分量,然后对每个IMF分量提取功率特征,并利用主成分分析(PCA)降维处理,最后使用线性判别分析分类器对信号特征分类。实验采用第三次和第四次国际BCI竞赛的数据进行验证,对皮层EEG信号和MEG信号运动想象任务的识别正确率分别达到92.0%和46.2%,均位于竞赛第一名水平。实验结果表明本文所提方法有较好有效性和稳定性,为脑信号特征提取提供了新思路。     

脑机接口中多线性主成分分析的张量特征提取

脑机接口(BCI)可以直接通过脑电(EEG)信号控制外部设备。本文针对传统主成分分析(PCA)和二维主成分分析(2DPCA)处理多通道EEG信号的局限性,提出了多线性主成分分析(MPCA)的张量特征提取和分类框架。首先生成张量EEG数据,然后进行张量降维并提取特征,最后用Fisher线性判别分析分类器进行分类。实验中将新方法应用到BCI competitionⅡ数据集4和BCI competitionⅣ数据集3,分别使用了EEG数据的时空二阶张量表示形式和时空频三阶张量表示形式,通过对可调参数多次调试,取得了高于其它同类降维方法的最佳结果。二阶输入最高正确率分别达到81.0%和40.1%,三阶输入分别达到76.0%和43.5%。     

基于不同特征参数的脑电信号分类

分别以自回归(autoregression,AR)模型系数、相关系数和信息熵作为信号特征对不同思维作业脑电(EEG)信号进行分类,其中相关系数和信息熵均是首次用于思维作业EEG信号的特征提取.实验结果显示,采用信息熵作为EEG信号特征的分类准确率总体上明显高于采用另两种特征参数,且受提取特征的数据分段长度的影响最小,有利于提高基于思维作业实时脑- 机接口的通信准确度和速率.同时,研究结果也进一步证实了高频信息可用于EEG的分类.     

基于时频分析的个性化脑-机接口设计

为了提高脑—机接口的分类正确率和实时性,本研究提出基于时频分析的个性化脑—机接口设计方案。通过对多导联EEG数据进行离线谱图分析,量化不同导联EEG信号在想象左右手运动时,其各个频带能量随时间的变化规律。然后将各导联EEG信号不同频带能量变化特征在左右手运动想象时的差异进行脑地形图成像。进而根据特征差异在大脑皮层的分布为不同用户的最佳电极位置选择和特征频带的确定提供直接依据。实验结果证明,该方法有效减少了特征提取的盲目性,用最少的导联,最少的特征向量,较简单的特征提取和分类算法,取得很好的分类效果,具有较高实用价值。     

基于小波变换和Teager能量算子的癫痫脑电自动分类

提出一种利用小波变换和能量算子对EEG进行预处理提取癫痫特征信号,进行近似熵估计,对脑电信号进行分类的新方法。首先利用小波分析将EEG信号进行4层分解分成多个子频带,对频率接近棘波的第1,2层小波系数计算非线性能量算子,再对能量算子进行近似熵估计,最后用SVM对EEG信号进行分类。结果表明,该方法对癫痫发作期EEG和正常的EEG分类效果比较理想。     

高频成分在思维作业脑电信号分类中的应用

采用独立分量分析(ICA)去除脑电伪迹,AR模型提取信号特征、BP神经网络用于模式识别,对2~5种思维作业脑电信号进行了分类研究。研究结果的重要发现是:对于经过ICA去伪迹后的EEG信号,当分类特征取自20~100 H z的高频范围时,分类准确率很高,与特征取自整个信号频段的分类结果大致相等,且大大超过利用2~35 H z的低频EEG节律进行的分类。对于这一现象的解释是,不同思维作业过程中,大脑在工频电场作用下产生了不同的节律同化反应,致使EEG信号的高频部分带有更显著的思维调制信息,从而有利于提高分类准确率。这一现象的发现,为脑电节律同化反应提供了新的证据,也为思维脑电的高准确率分类和高精度脑-机接口的实现提供了新的方法。     

Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines

A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and multiclass SVM. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the eigenvector methods are the features which well represent the EEG signals and the multiclass SVM trained on these features achieved high classification accuracies.     

Detection of artifacts from high energy bursts in neonatal EEG

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.     

基于加权复杂网络度熵和的癫痫发作检测方法

癫痫发作检测一直是一项富有挑战性的工作,随着癫痫发病率的增加,高性能癫痫自动检测算法在临床上可以减轻医务工作者的工作量,具有重要的临床医学研究意义。提出基于加权水平可视图的癫痫检测新方法。首先利用加权水平可视图将单通道脑电信号转化为复杂网络,并提取生成的复杂网络的度的平方和权重度分布熵两个特征;最后将两个特征之和作为单特征输入到线性分类器中,用来识别癫痫间歇期和发作期信号。对波恩大学的癫痫脑电数据集进行实验,评价所提出的检测算法的性能。使用该癫痫脑电数据集间歇期和发作期各100个实验样本,样本长度为1 024。实验结果表明,所提出的方法具有较高的分类精度,可达到98.5%。由于分类的特征为单特征,所以更加简单高效,可用于癫痫发作在线自动检测。     

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 ± 22% at a specificity of 86 ± 7% (mean ± SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.     

Statistics over features: EEG signals analysis

This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.     

基于 ERP 信号的目标检测研究综述

目标检测是一个广泛应用于多个领域的研究课题。传统的计算机视觉方法存在复杂度高、泛化能力差、模型对 象可迁移性差等缺点,而人脑在目标检测方面具有较强的认知能力和感知能力,仅需要很少的训练样本就可以达到较好的检 测效果。基于事件相关电位（Event-Related Potentials,ERP）的脑机接口（ Brain-Computer Interface,BCI）是一种全新的提 升目标检测性能的方法,它能够将脑内信息与外部环境进行交互。在此基础上,Gerson 提出了一种以脑电信号为基础的快速 串行视觉呈现（Rapid Serial Visual Presentation,RSVP）范式,包括显示方式、有效视觉范围、目标复杂度、刺激频率和持 续时长等。该文还对 ERP 信号的预处理、特征提取及分类等方法进行了综述, 并对该领域的局限性和发展趋势进行了探讨。     

利用整体经验模态分解和随机森林的脑电信号分类研究

癫痫脑电信号的自动监测与分类在临床医学上具有重要意义。针对脑电信号的非平稳特点,提出一种基于整体经验模态分解和随机森林相结合的脑电信号分类方法。选取波恩大学脑电信号数据集中癫痫发作间期和发作期的200个单通道信号,共819 400个数据作为样本。首先利用整体模态分解将癫痫脑电信号分解成多个固有模态函数,然后对各阶固有模态函数提取有效特征,最后分别用随机森林和最小二乘支持向量机对脑电信号的特征进行分类。将随机森林与最小二乘支持向量机分类正确识别率对比,结果表明,随机森林分类方法对发作期和发作间期的癫痫脑电信号的分类效果比较理想,识别精度为99.60％,高于最小二乘支持向量机的准确性。该方法的提出能有效提高临床癫痫脑电信号分析的效率。     

A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements

OBJECTIVE: Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease. METHODS AND MATERIAL: The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively). RESULTS: Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage. CONCLUSION: We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease.