小波包熵在脑电信号分析中的应用

为研究不同脑功能状态下脑电动态非线性特征，利用小波包变换的频率划分特性，对非平稳脑电信号进行节律提取，并计算相对小波能量，反映脑电节律间的相对能量关系。结合小波包熵分析脑电在不同大脑功能状态下的脑电复杂程度。实验结果表明，小波包分解能更精确地提取特定的脑电节律，小波包熵可以准确反映大脑活动的复杂程度。本方法也为分析其他非平稳信号提供了一种新的途径。     

小波包能量谱在睡眠分期中的应用

利用小波包技术,根据脑电信号在不同睡眠状态下各脑电节律所占的成分不同,提出一种基于小波包能量谱的睡眠脑电分期方法。首先依据脑电信号各节律的频率特点选择好分解层数对信号进行小波包分解,再重构信号,提取出睡眠脑电信号的各节律;然后运用小波包能量谱计算各节律所占的能量比重;最后用3例脑电数据进行实验。实验结果表明,不同睡眠状态下各脑电节律所占比重不同,随着睡眠的深入,睡眠脑电节律θ和δ所占的能量比重增大,而节律α和β所占的比重在减少。因此,可以运用睡眠脑电信号中各节律所占的成分不同来区分不同的睡眠状态,并可作为睡眠分期的一个特征参数。     

基于脑电节律样本熵的睡眠分期

针对睡眠脑电人工分期的不足,提出了一种基于脑电节律样本熵的睡眠分期方法。首先对睡眠脑电信号进行去噪和基本节律提取,然后计算不同睡眠状态下脑电节律的样本熵值,最后统计其样本熵均值与方差,通过对比发现:不同睡眠状态下脑电节律δ波和θ波的样本熵均值不相等且方差较小,这表明了通过分析睡眠脑电节律样本熵的方法可以用来表征不同睡眠期,为睡眠脑电分期提供了新的途径。     

基于单通道脑电信号的疲劳检测系统

针对目前高强度劳动人群频繁猝死的情况,文中设计了一套基于单通道脑电信号(Electroencephalography,EEG)的疲劳检测系统,以实现对该类人群疲劳程度的准确判定,起到预警效果。系统利用TGAM(ThinkGearm AM)脑电模块采集原始EEG数据,通过蓝牙方式将数据传送至上位机,在上位机中提取EEG的4个基本节律成分(δ,θ,α,β),以节律信号的相对频带能量作为表征疲劳状态的脑电特征,并利用Fisher判别分析(Fisher Discriminant Analysis,FDA)和概率神经网络(Probabilistic Neural Network,PNN)两种方法对脑电特征进行分类,给出评估结果。实验结果表明,所设计的基于单通道EEG的疲劳检测系统能够实现准确率较高的疲劳状态检测。     

PAC耦合的脑电信号中疲劳特征过滤提纯仿真

脑电信号具有高维度和复杂性,如果筛选出的特征不合理,会导致分析结果存在较大的误差。针对这一问题,研究一种脑电信号中疲劳相关特征过滤提纯方法。针对采集到的脑电信号,利用ICA方法去除其中的伪影,降低非脑电活动所引起的信号的干扰,并通过计算信息增益实现脑电信号中疲劳相关特征过滤。针对过滤出来的功率谱密度以及PAC耦合值特征,通过计算熵值进一步筛选出不同波段的功率谱特征,完成特征提纯。测试结果表明：所研究方法应用提纯得到的功率谱特征+PAC特征输入下,准确率相对较高,且反应时间相对较低,由此说明所研究方法过滤提纯得到的功率谱特征和PAC特征较为合理。     

抑郁症患者脑电复杂度的小波熵分析

应用小波熵理论分析抑郁症患者和健康人在安静和心算任务下自发脑电信号的复杂度：分别采集10例抑郁症患者和10例正常人在安静闭目和闭眼心算连减两种状态下的16导联脑电信号;计算这四组脑电数据的小波熵,并进行对比和统计分析。结果表明,抑郁症患者和正常人自发脑电的小波熵有着显著的差异：（1）在相同状态下,抑郁症患者各导联脑电的小波熵大于正常人对应导联的小波熵;（2）对同一个人,安静闭目状态下各导联脑电的小波熵大于心算连减状态下对应导联的小波熵。结论可为抑郁症的诊断提供参考。     

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。     

基于多特征融合的瞌睡检测与预警系统

针对驾驶疲劳瞌睡检测识别效果不佳及被试差异性所致的性能不稳问题，该文提出了一种使用单通道脑电信号进行瞌睡检测与预警的模型架构。首次利用注意力熵刻画清醒与瞌睡状态对应脑电动态演化的复杂度。使用小波包分解法提取相对能量特征，采用Relief算法筛选出适合不同个体的显著时频特征。最后将融合特征矢量送入支持向量机完成分类判别。将所提方法在脑电公开数据集上进行验证，得到95.9%的准确率及96.8%的敏感度。在LabVIEW平台上进行了虚拟仪器的设计，结果表明，所提方法可为驾驶疲劳的检测与预警设备开发提供有效模型。     

基于小波变换的动态脑电节律提取

针对脑电信号和其他医学信号的非平稳性,引入小波变换处理临床脑电信号的动态特性。根据脑电信号的不同节律特性,提出应用小波包变换构造不同频率特性的滤波器,提取脑电信号的４种节律,并由各种节律对应的小波系数构造动态脑电地形图。为了研究不同脑功能状态下脑电信号４种节律的动态特性,文中对两组不同临床脑电数据进行分析与比较,给出了有关的实际分析结果。实验结果表明,利用小波包分析的滤波特性,能够有效地反映临床脑电不同节律的动态特性,也为分析其他生物医学信号提供了一条新的途径。     

基于小波包分解的时变脑电节律提取

研究从时变非平稳脑电信号中提取脑电动态节律的新方法。首先用小波包分解构造不同频率特性的时变滤波器以提取各种时变的脑电节律，研究临床脑电信号瞬时变化。在此基础上测试并分析两种不同功能状态下的脑电信号，并由此构造各种节律的时变脑电地形图。实验结果表明，小波包分解可以有效提取脑电不同节律的动态特性，此方法也适用于分析其他生物医学信号。     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5–30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8–12 Hz) frequency band is weakened, and the beta (13–30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake–fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

频带能量与样本熵在注意力脑电信号中的对比研究

脑电波是一种复杂的生物电信号,可反应出大脑内部的活动及注意力等精神状态。基于此,论文设计了注意力相关的脑电实验,并完成了受试者脑电数据的采集,对所采集的脑电数据分别从以下两种角度进行研究:从时频分析的角度,采用db4小波基对原始脑电信号进行7层小波包分解,提取了β波/θ波能量占比作为特征量;从非线性动力学的角度,提取脑电信号的样本熵作为特征,并分别对各受试者进行注意力的分级研究。通过对比分析,结果表明两者都能从一定程度上表征注意力水平的状况,但样本熵对于多级注意力的区分度更好。     

EEG index for control operators’ mental fatigue monitoring using interactions between brain regions

Mental fatigue is a gradual and cumulative phenomenon induced by the time spent on a tedious but mentally demanding task, which is associated with a decrease in vigilance. It may be dangerous for operators controlling air traffic or monitoring plants. An index that estimates this state on-line from EEG signals recorded in 6 brain regions is proposed. It makes use of the Frobenius distance between the EEG spatial covariance matrices of each of the 6 regions calculated on 20 s epochs to a mean covariance matrix learned during an initial reference state. The index is automatically tuned from the learning set for each subject. Its performance is analyzed on data from a group of 15 subjects who performed for 90 min an experiment that modulates mental workload. It is shown that the index based on the alpha band is well correlated with an ocular index that measures external signs of mental fatigue and can accurately assess mental fatigue over long periods of time.     

基于单导EEG的高空缺氧所致疲劳的实时检测技术研究

研究了一种基于单导EEG的高空缺氧所致疲劳的实时检测技术.采用小波包分解对所采集的EEG进行预处理,然后选取EEG数据的近似熵和Welch谱分析中30-60Hz频段的能量作为模式识别的特征向量,用Bayes概率统计识别方法进行模式识别以检测高空缺氧所致疲劳.实验结果表明,通过提取的特征量可以很好的区分正常情况和缺氧疲劳情况下的脑电模式(检测正确率高于93%).这种方法为客观、实时检测缺氧所致疲劳提供了可能.     

Quantitative methods for detecting cerebral infarction from multiple channel EEG recordings

EEG has been known to be non-stationary and time varying. Time–frequency representation (TFR) is a proper tool for such non-stationary signals. In the present paper, TFR-based quantitative methods that can translate complicated and subjective waveform-based EEG analysis into objective measures are introduced to characterize EEG recorded from normal subjects and cerebral infarction (CI) patients. Relative frequency band energy (RFBE) is computed from time–frequency plane for the five subbands: delta, theta, alpha, beta and gamma. Moreover, we propose the Shannon entropy (SE) of TFR to detect the difference in EEG for the two kinds of subjects. Finally, the temporal evolutions of these quantitative parameters are presented to trace EEG changes. The experiment results show that CI results in the RFBE changes of the five rhythms; however, the RFBEs of some rhythms have stronger association with CI. Increase in EEG SE of CI patients is obvious. The time evolutions of RFBE and SE as valuable objective measures can be displayed in real time and be used as helpful references in detection and monitoring of CI.     

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5-30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8-12 Hz) frequency band is weakened, and the beta (13-30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake-fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.     

Assessment visual fatigue of watching 3DTV using EEG power spectral parameters

Although the three-dimensional television is popular for its stereoscopy, the fatigue caused by the prolonged watching of 3DTV should not be underestimated. Electroencephalogram (EEG) has been widely used for monitoring the brain’s functional activities. Based on our previous research of 3DTV fatigue, one more objective and effective 3DTV fatigue evaluation model is proposed on gravity frequency of power spectrum and power spectral entropy. As the fatigue changes, the gravity frequency reflects the transition of EEG power spectrum and the power spectral entropy describes the level of chaos of EEG. 16 channels of EEG data of twenty-five subjects watching 2DTV and 3DTV were collected, and gravity frequency of power spectrum and power spectral entropy were then calculated and analyzed. These two parameters of the 3D group changed more significantly comparing with that of the 2D group on several electrodes. There are significant decreases in gravity frequency and power spectral entropy in several brain regions after long time of watching 3DTV, which indicates the decline of subjects’ alertness level. Based on the subjective evaluation and two significant parameters, gravity frequency and power spectral entropy, an accurate evaluation model for 3DTV fatigue was established using the regression equation.