利用有限差分法计算真实头模型脑电正问题

脑电研究领域的两个关键问题是脑电正问题和脑电逆问题,脑电正问题是脑电逆问题的基础.由于复杂、非规则真实头模型中的脑电正问题不存在解析解,因此脑电源分析依赖于正问题数值算法的精度和效率.文章首先详细推导了有限差分算法求解三维各向同性脑电正问题的数学模型,然后在三层同心球模型上通过与解析解比较验证了该算法的精度和效率,最后将该算法应用于真实头模型.仿真结果表明,有限差分法可以有效地处理任意形状几何体的电位场分布问题,是模拟计算真实头模型中脑电正问题的有力工具.     

自适应遗传算法在脑电逆问题中的应用

脑电逆问题是指利用脑电图 (EEG)数据去反演可以反映脑电活动等效偶极子源的参数信息。优化方法是解决这一问题的有力工具。自适应遗传算法根据算法的不同情况自动改变遗传算子 ,将这一算法应用于脑电逆问题 ,其运算速度和防止局部最优的性能较基本遗传算法有较大提高     

脑电逆问题方法研究

本文阐述了脑电逆问题的模型和求解方法,并介绍了求解脑电逆问题的发展方向。     

一种求解冗余机械臂逆运动学的优化方法

基于加权最小范数法,推导出一种避免计算雅可比矩阵伪逆的优化方法.首先对加权雅可比矩阵的6维非奇异子矩阵求逆,得到逆运动学的特解和齐次解.然后用特解减去齐次解沿特解方向的分量得到运动学逆解.通过一个7自由度冗余机械臂的算例和仿真证明了方法在保证求解精度、降低求解难度以及避免关节极限方面的有效性     

基于卡尔曼滤波理论的脑电逆问题反演

由头皮上的电压推断出大脑内神经活动源的过程称之为脑电逆问题,这一问题的解决具有重要的研究意义和应用价值。为了有效地进行脑电逆问题的反演计算,提出了一种基于状态空间的新的脑电逆问题求解算法。该方法首先根据神经系统的动力学方程得到状态方程,并由脑电系统的观测方程构成测量方程;然后应用卡尔曼滤波方法来反演大脑内活动源的信息。这种新的求逆算法不仅可以处理脑电系统中的不确定因素,而且还可以将静态和动态脑电逆问题的求解统一到同一框架下,因此具有一定的新颖性;最后分别给出了模拟数据和实际脑电数据的实验结果。实验结果证明,卡尔曼滤波法更具优越性。     

ICA在思维脑电特征提取中的应用

简要介绍了独立分量分析（ICA）的基本思想及算法，并将其应用在基于多导思维脑电（mental EEG)的特征提取方面。实验结果表明：ICA可以将脑电信号中包含的心电（ECG）、眼电（EOG）等多种干扰信号成功地分离出来，较好地完成了脑电消噪预处理工作。同时，通过使用ICA方法对不同心理作业的脑电信号进行分析处理，发现了与心理作业相对应的脑电独立分量特征，这些稳定的独立分量特征为心理作业分类和脑一机接口技术提供了新的实现方法。     

低密度脑电自适应去噪方法

便携式和可穿戴设备的低密度脑电图更便于实际使用,但会受到多种不可预知的噪声影响,给去噪带来极大的困难。脑活动成分较为相似,在特征空间分布较为紧密,而噪声成分与脑电成分不同,差异性大,在特征空间分布较为分散。本文提出了一种低密度脑电自适应去噪方法,采用小波分解和盲源分离方法提取潜在成分,并基于脑电和噪声成分在特征空间的分布特性,采用单类支持向量机识别并去除远离成分分布中心的异常成分。仿真数据的定量分析结果表明,提出的方法在肌电、眼电和工频等噪声抑制方面均优于现有方法;通过对真实脑电数据的成分簇可视化分析,直观展示了低密度脑电噪声有效去除的原因。结合盲源分离和异常检测的思路进行低密度脑电去噪,不需要设定特定噪声相关的特征参数,能够自适应地去除多种类型噪声同时有效保留脑活动成分,具有优良的性能和实用性。     

脑电信号灰建模及特征提取新方法研究

脑电信号包含着大脑皮层活动的丰富信息，但同时也包含了大量的噪声。如何有效地从这些丰富的信息中提取有用特征．一直是该研究领域的热点问题。文中提出利用灰建模的方法进行脑电特征提取，具有一定的创新性。介绍了灰色建模机理及其在脑电特征提取中的应用，利用实测脑电信号建立了脑电GM（1，1）模型，并进行了模型参数估计和特征提取，用K近邻算法对所提取的特征参数进行了分类。分类结果表明，利用灰建模的方法进行脑电特征提取和分类的方法是可行、有效的，为脑电信号的特征提取提供了一种新的思路和方法。     

便携式脑电采集器研究与设计

脑电信号是人脑内部各种活动的外部表征,脑电信号的分析及处理对临床上一些疾病的诊断和治疗十分重要。基于脑机接口技术,提出了便携式脑电采集器的设计,它将脑电信号进行收集,通过蓝牙的上行通道将信号传递到计算机,并进行信号处理分离出5种脑波信号,在编制的显示软件中准确显示出来,为我国脑机接口技术向实用性转化提供了依据。     

基于脑电功能连接拓扑表征的心算任务分类

使用脑网络图的方法分析脑电功能连接存在阈值选择、忽略了脑电图动力学特性的问题。针对这一问题,提出了一种使用拓扑动态建模的方法来分析脑电功能连接矩阵,以提高心算任务分类识别正确率。该方法首先将功能连接矩阵转换为无向加权图,然后使用持续同调工具来构建不同的复形,记录拓扑动态过程中形成的不同阶的同调特征,形成持续图,最后使用持续景观图特征作为分类特征,输入到随机森林分类器进行心算状态识别。在心算状态识别和心算质量分类两个任务中分别获得了最高99.26%、99.20%的识别准确率,97.87%、99.80%的敏感性,以及99.78%、97.64%的特异性,并且在跨个体验证实验中分别获得了66.81%、66.85%的准确率。实验结果表明,该方法能充分考虑所有可能的阈值,有效提取脑电功能连接的分类信息,实现脑电心算状态自动识别。     

基于神经网络集成的睡眠脑电分期研究

用近似熵对睡眠脑电信号进行分期,由于睡眠Ⅲ期和Ⅳ期近似熵值非常接近,靠近似熵值无法区分,提出基于神经网络集成的睡眠脑电信号分期,采用BP神经网络为分类器,对用AR参数提取的睡眠脑电特征对睡眠Ⅲ期和Ⅳ期进行分期。为进一步提高BP神经网络性能,采用Bagging算法对BP神经网络分类器进行加权投票,实验表明,提出的方法具有很好的分期效果。     

Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.     

Detecting rapid eye movement sleep using a single EEG signal channel

Sleep stage scoring is generally determined in a polysomnographic (PSG) study where technologists use electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals to determine the sleep stages. Such a process is time consuming and labor intensive. To reduce the workload and to improve the sleep stage scoring performance of sleep experts, this paper introduces an intelligent rapid eye movement (REM) sleep detection method that requires only a single EEG channel. The proposed approach distinguishes itself from previous automatic sleep staging methods by introducing two sets of auxiliary features to help resolve the difficulties caused by interpersonal EEG signal differences. In addition to adopting conventional time and frequency domain features, two empirical rules are introduced to enhance REM detection performance based on sleep being a continuous process. The approach was tested with 779,661 epochs obtained from 947 overnight PSG studies. The REM sleep detection results show a kappa coefficient at 0.752, an accuracy level of 0.930, a sensitivity score of 0.814, and a positive predictive value of 0.775. The results also show that the performance of the approach varies with the ratio of REM sleep and the severity of sleep apnea of the subjects. The experimental results also show that it is possible to improve the performance of an automatic sleep staging method by tailoring it to subgroups of persons that have similar sleep architecture and clinical characteristics.     

A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted pre-processing and AIRS

This paper presents a hybrid approach based on feature selection, fuzzy weighted pre-processing and artificial immune recognition system (AIRS) to medical decision support systems. We have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively. In the second stage, heart disease and hepatitis disease datasets are normalized in the range of [0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 92.59% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively. With these results, the proposed method can be used in medical decision support systems.     

Estimating vigilance level by using EEG and EMG signals

We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%.     

基于脑电模糊分析的睡眠分期方法研究

利用脑电信号模糊特征分类的方法对睡眠进行分期研究。首先对脑电信号进行预处理,滤除干扰噪声后使用模糊熵算法、多尺度熵算法以及复杂度算法对脑电信号进行特征参数提取,采用最小二乘支持向量机（the Least Squares Support Vector Machine,LS-SVM）对特征参数进行分类,并将睡眠过程分为清醒期、浅睡期、深睡期和快速眼动期（Rapid Eye Movement,REM）,获得分期正确率。最后通过上述方法对2?000组睡眠脑电样本进行睡眠分期测试,与专家人工分期结果进行比对,将复杂度输入到最小二乘支持向量机进行分类的平均正确率是92.65%,高于模糊熵和多尺度熵作为最小二乘向量机的输入时的准确率。基于模糊特征的复杂度提取的特征参数可以作为睡眠分期的有效依据,在保证准确度的前提下,降低人工成本。     

Classification of sleep stages based on LSTAR model

Sleep study is very important in the health since sleep disorders affect the productivity of individuals. One of the important topics in sleep research is the classification of sleep stages using the electroencephalogram (EEG) signal. Electrical activities of brain are measured by EEG signal in the laboratory. In real-world environments, EEG signal is also used in portable monitoring devices to analyze sleep. In this study, we propose an efficient method for classification of sleep stages. EEG signals are examined by a new model from autoregressive (AR) family, namely logistic smooth transition autoregressive (LSTAR) to study sleep process. In contrast to the AR model, LSTAR is a non-linear one; therefore, it is suitable for modeling non-linear signals such as EEG. In the current research, at first, each 30-second epoch of EEG signal is decomposed into the time-frequency sub-bands using the double-density dual-tree discrete wavelet transform (D3TDWT). In the second step, LSTAR model is used for feature extraction from each sub-band. Next, the dimension of feature vector is reduced by tensor locality preserving projection (tensor LPP) method, and then the obtained features are given to classifier to determine the stage of each epoch based on the number of considered classes. After classifying sleep stages, some misclassified epochs can be corrected according to the smoothing rule. We consider different classifiers and evaluate their performance. The results indicate the efficiency of the proposed method in comparison with the recently introduced methods in terms of accuracy and Kappa coefficient.     

Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm

This paper proposes a fast weighted horizontal visibility graph constructing algorithm (FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated by comparing with Fast Fourier Transform (FFT) and sample entropy (SampEn) method. Two noise-robustness graph features based on the FWHVA, mean degree and mean strength, are investigated using two chaos signals and five groups of EEG signals. Experimental results show that feature extraction using the FWHVA is faster than that of SampEn and FFT. And mean strength feature associated with ictal EEG is significant higher than that of healthy and inter-ictal EEGs. In addition, an 100% classification accuracy for identifying seizure from healthy shows that the features based on the FWHVA are more promising than the frequency features based on FFT and entropy indices based on SampEn for time series classification.     

基于改进EMD的脑电信号去噪方法

以经验模态分解(EMD)为理论基础,提出一种相似波形加权匹配的方法,对脑电信号(EEG)端点进行延拓,改善EMD分解过程中存在的端点效应,利用延拓后的EMD方法对EEG进行去噪。基于美国加州理工学院数据库中EEG的仿真结果表明,延拓后的EMD方法可有效去除EEG波形中的噪声部分,使EEG的特征更明显。