虚拟现实视觉体验对事件相关电位影响的研究

虚拟现实技术具有广阔应用前景,但它对大脑信息处理及认知影响尚不清楚。本文结合头皮脑电,设计了平面和虚拟现实两种模式的视觉任务,对比分析虚拟现实对事件相关电位（ERP）的影响,探索沉浸式视觉体验过程中大脑的认知加工过程。参考电极的选择是研究事件相关电位的关键,为获得更客观的脑电信息,本文首先使用参考电极标准化技术将记录的脑电信号的参考近似转换为理想的零点,经过必要的预处理,再采用叠加平均的方法从中提取ERP。结果发现,与平面模式相比,在虚拟现实模式下ERP成分中没有出现显著的P300成分,这可能与大脑产生疲劳感相关;虚拟现实模式下N100成分的潜伏期提前、幅值增大,反映了虚拟现实环境更容易引起注意,并使人产生沉浸感。      

EEG-based drowsiness estimation for safety driving using independent component analysis

Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.     

基于相位延迟指数的脑功能网络及测谎研究

在脑认知科学领域,越来越多的研究开始专注于利用不同导联脑电信号之间的相互依赖关系来研究大脑整体认知功能.相位延迟指数可有效减少由容积导体引起的误差,该方法已被广泛应用.而基于图论的脑网络研究方法在测谎方面还少见报道.本文通过对30名（诚实和说谎）受试者的脑电信号进行网络拓扑分析,将网络参数作为判别指标,使用支持向量机对实验数据进行分类.研究发现,两组受试者的小世界指标表现出显著的统计学差异,且得到较高的测谎准确率,结果证明了利用相位延迟指数方法进行图论分析的测谎有效性.     

基于听觉E RP功能脑网络特征和SVM的测谎方法研究

基于图论理论的脑网络分析方法近年来在认知脑科学研究中起到了非常重要的作用,而基于事件相关电位（Event-Related Potentials,ERP）的传统测谎方法一直都专注于对某一特定通道上的脑电信号进行分析,针对传统方法中使用少数通道并不能够全面的反映人在说谎状态下大脑整体认知功能特征的缺点,本文提出了基于脑网络特征的测谎方法,通过听觉刺激诱发事件相关电位ERP,记录脑区多通道脑电信号,通过讨论各导联之间的相位延迟指数来构建脑功能网络,计算7类脑网络特征参数作为判别指标。分析被试在说谎和无辜状态下的网络特征参数,使用支持向量机对实验数据进行分类判断,结果表明：本文提出的方法有较高的判别准确率,优于目前判别方法的平均值,证明了本方法的测谎有效性。     

基于持续同调性的人脑认知过程格式塔模式识别拓扑分析

信息通信技术和神经科学的融合发展预示了脑对脑无线通信的可能性与巨大潜力。将持续同调分析方法与脑电图（EEG）结合，提取了在格式塔完形（Gestalt）认知测试中大脑对不同轮廓和形状的神经反应的生理学特征。实验结果表明，当被试者观察随机序列图像（random sequence diagram，RSD）时，其大脑额叶涉及的活动区域多于其观察有序格式塔图像（Gestalt image，GST）。同时，RSD诱发的EEG信号在几个频带上与GST的持续同调熵（persistent entropy，PE）有着显著不同，这表明人类对形状和轮廓的认知过程，可以通过拓扑分析在一定程度上实现分类区分。该方法可以在保留原生信号的整体和局部特征的前提下实现神经信号的数字化。总的来说，通过对EEG信号的持续同源性特征评估量化了认知过程神经信号的相关性，提供了实现B2BC中神经信号数字化的可行方法。     

基于PCANet和SVM的谎言测试研究

主成分分析网络（Principal Component Analysis Network,PCANet）是基于深度学习理论的一种非监督式的特征提取方法,它克服了手工提取特征的缺点,目前其有效性仅仅在图像处理领域中得到了验证。本文针对当前谎言测试方法中脑电信号特征提取困难的缺点,首次将PCANet方法应用到一维信号的特征提取领域,并对测谎实验的原始脑电信号提取特征,然后使用支持向量机（Support Vector Machine,SVM）将说谎者和诚实者的两类信号进行分类识别,将实验结果和其它分类器及未使用特征提取的分类效果进行了比较。实验结果显示相对未抽取任何特征的方法,提出的方法PCANet－SVM可以获得更高的训练和测试准确率,表明了PCANet方法对于脑电信号特征提取的有效性,也为基于脑电信号的测谎提供了一种新的途径。     

基于互信息的脑网络及测谎研究

互信息分析方法是基于信息论提出的一种描述两信号间信息交互情况的算法,其在脑电信号领域的有效性已得到了充分证实.针对当前测谎方法中脑电信号特征提取困难以及大脑整体认知功能分析在脑认知科学研究中越来越被重视的情况,本文首次将互信息分析方法应用到脑电测谎领域中,使用互信息量化大脑各节点之间的相关性,对计算结果进行统计分析,选取出在两类人群中具有显著性差异的电极对的互信息作为分类特征,进行模式识别,得到了99.67%的准确率.这一结果表明,互信息分析方法是一种有效的脑功能连接分析方法,为基于脑电信号连接分析的测谎研究提供了一种新的途径.另外,对说谎与诚实两类受试者的大脑功能网络的分析结果表明：处于说谎状态时,大脑的额叶、顶叶、颞叶及枕叶之间协同实现谎言功能,并在躯体行为所对应的脑区与其他脑区的连接上也表现出相对诚实组的显著性差异,以上结果均有助于进一步揭示谎言的神经活动机制.     

基于C-LSTM模型的端到端多粒度运动想象脑电信号分析方法

脑电信号（Electroencephalography, EEG）是人的大脑在不同状态下产生的生物电信号。运动想象脑电信号是其中较为典型的一类信号，广泛应用于脑机接口技术中。对运动想象脑电信号分析的研究由来已久，目前主要采用公共空间模式等特征提取方法，对于如何提取更加有效的脑电信号特征以及如何对时序信息进行建模仍然是需要解决的问题。因此，本文设计了基于C-LSTM（Convolutional-Long Short Term Memory）模型的端到端多粒度脑电分析方法。并利用空间信息以及小波脑网络方法进行了改进，在BCI2008数据集上，相较传统方法提高了近10%，到达了93.6%的识别率。      

使用EEG分析水平视差对3D图像舒适性的影响

水平视差是影响3D 图像舒适性的重要因素之一。采用不同等级水平视差的3D 图像作为刺激信号,通过记录被试者的EEG 反应信号,提取出被试者观看不同等级水平视差3D 图像的ERP 波形;同时记录被试观看这一系列3D 图像舒适性体验的行为数据,并计算其舒适性检测率。通过EEG信号分析可见,无论在交叉或者非交叉水平视差情况下,3D 图像水平视差舒适范围在45'以内;水平视差在45'~75'时,交叉视差3D 图像ERP 波形280 ms 附近的幅值与不舒适的程度相关,非交叉视差3D 图像ERP 波形250 ms 附近的幅值与不舒适程度相关;检测率曲线结果和主观评价结果显示:3D图像水平视差的舒适性范围在45'以内,水平视差在45'~75'时,3D 图像不舒适程度随着视差的增加而增大。结果表明:EEG 分析、检测率曲线和主观评价结论一致,因此,可以使用EEG 分析水平视差对3D 图像舒适性的影响。     

A matrix-based algorithm for estimating multiple coherence of a periodic signal and its application to the multichannel EEG during sensory stimulation

The coherence between the stimulation signal and the electroencephalogram (EEG) has been used in the detection of evoked responses. The detector's performance, however, depends on both the signal-to-noise ratio (SNR) of the responses and the number of data segments (M) used in coherence estimation. In practical situations, when a given SNR occurs, detection can only be improved by increasing M and hence the total data length. This is particularly relevant when monitoring is the objective. In the present study, we propose a matrix-based algorithm for estimating the multiple coherence of the stimulation signal taking into account a set of N EEG channels as a way of increasing the detection rate for a fixed value of M. Monte Carlo simulations suggest that thresholds for such multivariate detector are the same as those for multiple coherence of Gaussian signals and that using more than six signals is not advisable for improving the detection rate with M = 10. The results with EEG from 12 normal subjects during photic stimulation at 10 Hz showed a maximum detection for N greater than 2 in 58% of the subjects with M = 10, and hence suggest that the proposed multivariate detector is valuable in evoked responses applications.     

面向事件相关电位成分的稀疏字典构建方法

利用稀疏分解来研究EEG信号是一种新兴的可靠的方法。常见的稀疏字典学习算法中,K-SVD算法得到了比较广泛的应用。但是利用K-SVD算法获得的字典原子很难完整的包含EEG信号中的事件相关电位（ERP）成分,通常样本中的ERP成分都会分布在大量原子中。本文提出把常用的K-SVD算法基础结合稀疏性能指标作为约束条件,解决稀疏字典难以与ERP成分对应的问题,并给出利用该算法进行ERP分析的步骤。通过在一个基于听觉刺激的公开数据集上使用本文的方法,成功地获得了包含完整目标ERP成分的字典原子,证明了方法的可行性。      

Estimating alertness from the EEG power spectrum

In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings     

NeuroGlasses: a neural sensing healthcare system for 3-D vision technology

3-D vision technologies are extensively used in a wide variety of applications. Particularly glasses-based 3-D technology facilities are increasingly becoming affordable to our daily lives. Considering health issues raised by 3-D video technologies, to the best of our knowledge, most of the pilot studies are practiced in a highly-controlled laboratory environment only. In this paper, we present NeuroGlasses, a nonintrusive wearable physiological signal monitoring system to facilitate health analysis and diagnosis of 3-D video watchers. The NeuroGlasses system acquires health-related signals by physiological sensors and provides feedbacks of health-related features. Moreover, the NeuroGlasses system employs signal-specific reconstruction and feature extraction to compensate the distortion of signals caused by variation of the placement of the sensors. We also propose a server-based NeuroGlasses infrastructure where physiological features can be extracted for real-time response or collected on the server side for long term analysis and diagnosis. Through an on-campus pilot study, the experimental results show that NeuroGlasses system can effectively provide physiological information for healthcare purpose. Furthermore, it approves that 3-D vision technology has a significant impact on the physiological signals, such as EEG, which potentially leads to neural diseases.     

一种基于FPGA的脑电分类算法实现

通过对脑电信号特征的分析,利用小波变换的多尺度分析技术对脑电信号进行特征提取,进而使用主成分分析算法对特征进行降维,并对降维后的信号使用Fisher线性判别方法进行分类。最后,利用VerilogHDL硬件编程语言设计实现了Mallat分解算法、PCA算法和LDA算法模块,并在FPGA应用板上实现了脑电分类功能。系统对2008年BCI大赛的数据进行了测试,分类准确率达到92.31%,表明该方法对开发便携式脑机接口系统具有良好的应用价值。     

色彩感知中的脑电信号多域特征选择算法研究

针对目前在不同色彩感知中的脑电信号识别方面的研究还不多见,本文提出采用随机森林算法对信号的时域特征和频域特征进行最优组合的方法对不同色彩感知中的脑电信号进行识别。首先采用小波变换,对脑电信号进行7层分解,提取脑电信号在delta、theta、alpha和beta节律频带上的小波能量,并结合脑电信号在时域上的统计量偏度和峰度组成特征向量。然后通过基于随机森林的特征选择算法提取最优的特征组合方案,删除冗余的特征量。使用自适应增强算法进行分类识别,识别的平均正确率可达到85.07%。该结果表明使用本文所提出的特征提取与选择方法用于不同色彩感知中的脑电信号识别上是可行的,并且能够取得较好的识别率。      

Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach

Traditional methods for removing ocular artifacts (OAs) from electroencephalography (EEG) signals often involve a large number of EEG electrodes or require electrooculogram (EOG) as the reference, these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces (BCI). To address these limitations, a method combining a convolutional autoencoder (CAE) and a recursive least squares (RLS) adaptive filter is proposed. The proposed method consists of offline and online stages. In the offline stage, the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model. Once the model is trained, the EOG channels are no longer needed. In the online stage, by using the CAE model to identify the OAs from a single-channel raw EEG signal, the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter. Experiments show that the root mean square error (RMSE) of the CAE-RLS algorithm and independent component analysis (ICA) are 1.253 3 and 1.254 6 respectively, and the power spectral density (PSD) curve for the CAE-RLS is similar to the original EEG signal. These experimental results indicate that by using only a couple of EEG channels, the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal. In addition, the processing time of the CAE-RLS is shorter than that of ICA, so the CAE-RLS algorithm is very suitable for BCI system.     

基于ITU-T G.8032的以太环网保护方案

Ethernet Ring Protection (ERP) introduced by ITU-T G.8032 Recommendation is a new type of packet-switched network protection technology, which can provide ring automatic protection switch. However, the Filtering Database (FDB) flush meth-od adopted in the current ERP standard has the drawback of introducing a large amount of transient traffic overshoot caused by flooded Ethernet frames right after protection switching. In this article, we propose a G.8032 ERP scheme that uses FDB flush based on area to resolve this issue and investigate how the proposed scheme deals with the traffic flooding problem. The theoretical analysis and sim-ulation show that the proposed scheme can effec-tively improve the performance of the Ethernet ring by decreasing the unnecessary FDB flush in com-parison with the existing approaches.     

New Approach to Estimation of Interchannel Phase Coupling of Electroencephalograms

A new method to estimate the phase coupling of electroencephalogram (EEG) signals in different channels is based on the calculation of difference of phase characteristics for channel signals at the ridge points (maximum magnitudes) of wavelet spectra. It is shown that the method can be used to determine phase-coupled pairs of EEG leads and distinguish such pairs from uncoupled pairs. The interchannel phase coupling of EEG is estimated for cognitive and motor tests of a normal patient and a patient with craniocerebral injury     

基于脑电信号深度迁移学习的驾驶疲劳检测

脑电信号一直被誉为疲劳检测的“金标准”,驾驶者的精神状态可通过对脑电信号的分析得到。但由于脑电信号具有非线性、非平稳性和空间分辨率低等特点,传统的机器学习方法在运用脑电信号进行疲劳检测时还存在识别率低,特征提取操作繁琐等不足。为此,该文基于脑电信号的电极-频率分布图,提出运用深度迁移学习实现的驾驶疲劳检测方法,即搭建深度卷积神经网络,并利用SEED脑电情绪数据集对其进行预训练,然后通过迁移学习方法将其用于驾驶疲劳检测。实验结果表明,卷积神经网络模型能够很好地从电极-频率分布图中获得与疲劳状态相关的特征信息,达到较好的识别效果。此外,基于迁移学习策略可以将训练好的深度网络模型迁移到其他识别任务上,有助于推动脑电信号在驾驶疲劳检测系统中的应用。