基于平均能量差的运动想象EEG通道选择和特征提取

共空间模式(CSP)广泛应用于脑电信号(EEG)的特征提取,合适的通道选择可以有效地提高CSP的分类性能,增加信噪比。根据运动想象信号的平均能量差来进行通道选择和特征提取。首先取两类运动想象信号的通道均值能量作为投票的阈值,根据投票差值统计各通道上有明显能量差值试次的数量,基于此来选择出合适的通道,然后对这些通道取能量特征进行归一化,再结合CSP空域特征利用SVM进行分类。在BCI CompetitionⅢData SetsⅣa和BCI Competition IV Dataset SetsⅠ两个数据集上进行的分类实验中,所提出的方法相比于全通道CSP,平均精度分别提高了5.7%和10.9%,通道数分别减少了74.3%和51.7%,验证了所提出的通道选择和特征提取方法的有效性。

EEG信号结合特征融合技术诊断精神分裂症和抑郁症

目的探索通过机器学习算法结合脑电信号实现对精神分裂症和抑郁症的诊断。方法分别采集33例精神分裂症患者和28例抑郁症患者的脑电信号,并将采集到的脑电图信号格式由EDF格式转化为ASCII格式,提取脑电信号的Lempel-Ziv复杂度、最大李雅普诺夫指数、Higuchi分形维数等特征。应用特征融合策略对特征进行融合,形成新的特征向量,然后利用机器学习分类算法进行分类研究。结果最终基于高斯核函数的支持向量机(SVM)的分类准确率为84.85%,其中灵敏度为89.47%,特异性为78.57%。结论通过提取EEG脑电信号特征结合机器学习算法对精神分裂症和抑郁症进行识别,对开发新型的精神分裂症和抑郁症的诊断技术具有一定的研究意义。

基于EEG的警觉度客观检测与通道筛选技术研究

在航天领域,警觉度的降低会影响航天员的工效,还可能引发重大安全事故。针对航天领域的警觉度客观检测技术进行研究,设计了多轮次PVT和3⁃back任务结合的警觉度建模实验,并全程采集EEG信号和行为学数据,该实验范式成功诱导了37名志愿者警觉度下降,并伴随着大脑快波成分减少,慢波成分增加,复杂度降低等现象,在额叶、颞叶和枕叶区域变化明显。使用方差分析(ANOVA)结合支持向量机(SVM)对单个特征建模的方法筛选出了6个警觉度敏感特征,在此基础上使用SVM⁃RFECV方法筛选出了12个警觉度敏感导联,最后使用SVM进行警觉度客观检测模型的构建。结果显示:特征筛选前的平均分类正确率为83.10%,在导联筛选后为87.16%,提升了约4%。特征筛选和导联筛选工作有效地提高了模型的综合性能,同时简化了脑电采集流程,减少了后续数据处理的工作量和时间成本。

Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

The term Epilepsy refers to a most commonly occurring brain disorder after a migraine.Early identification of incoming seizures significantly impacts the lives of people with Epilepsy.Automated detection of epileptic seizures(ES)has dramatically improved the life quality of the patients.Recent Electroencephalogram(EEG)related seizure detection mechanisms encountered several difficulties in real-time.The EEGs are the non-stationary signal,and seizure patternswould changewith patients and recording sessions.Further,EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs.Artificial intelligence(AI)methods in the domain of ES analysis use traditional deep learning(DL),and machine learning(ML)approaches.This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection(OAOFS-DBNECD)technique using EEG signals.The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs.The suggested OAOFS-DBNECD technique transforms the EEG signals into.csv format at the initial stage.Next,the OAOFS technique selects an optimal subset of features using the preprocessed data.For seizure classification,the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer(AEO)with a deep belief network(DBN)model.An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm.The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies.In addition,the result of the suggested approach has been evaluated using the CHB-MIT database,and the findings demonstrate accuracy of 97.81%.These findings confirmed the best seizure categorization accuracy on the EEG data considered.

基于深度学习的EEG数据分析技术综述

对近年来的相关工作进行全面分析、横向比较,梳理出基于深度学习的EEG数据分析闭环流程.对EEG数据进行介绍,从深度学习在EEG数据预处理、特征提取以及模型泛化3个关键阶段的应用进行展开,梳理深度学习算法在相应阶段提供的研究思路和解决方案,包括各阶段所存在的难点与问题.全方位总结出不同算法的主要贡献和局限性,讨论深度学习技术在各个阶段处理EEG数据时所面临的挑战及未来的发展方向.

Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN

This paper proposes the use of time-frequency and wavelet transform features for emotion recognition via EEG signals. The proposed experiment has been carefully designed with EEG electrodes placed at FP1 and FP2 and using images provided by the Affective Picture System (IAP), which was developed by the University of Florida. A total of two time-domain features, two frequen-cy-domain features, as well as discrete wavelet transform coefficients have been studied using Artificial Neural Network (ANN) as the classifier, and the best combination of these features has been determined. Using the data collected, the best detection accuracy achievable by the proposed schemed is about 81.8%.

抑郁症EEG诊断的类脑学习模型

抑郁症是一种全球性精神疾病,传统诊断方法主要依靠量表与医生的主观评估,无法有效识别症状,甚至存在误诊的风险。基于生理信号的深度学习辅助诊断有望改善传统缺乏生理学依据的方法。然而,传统深度学习方法依赖巨大算力,且大多是端到端的网络学习。这些学习方法也缺乏生理可解释性,限制了辅助诊断临床应用。提出一种用于抑郁症脑电图(electroencephalogram,EEG)诊断的类脑学习模型,在功能层面,构建脉冲神经网络对抑郁症与健康个体进行分类,精度超过97.5%,相比深度卷积方法,脉冲方法降低了能耗;在结构层面,利用复杂网络建立脑连接的空间拓扑并分析其图特征,找出了抑郁症个体潜在的脑功能连接异常机制。

基于3D特征融合与轻量化CNN的情绪EEG识别

情绪变化可引起头皮脑电信号的改变,基于脑电信号的情绪识别是近年来情绪研究的一个重要方向。为此,提出一种基于3D特征融合与轻量化卷积神经网络的情绪EEG识别方法,使用2 s窗口的3D特征图作为输入,并根据效价和唤醒提供情绪状态作为输出。在DEAP公开数据集上对所提方法进行受试者依赖实验,结果表明情绪识别性能评估效价和唤醒识别准确率分别为(97.08±0.32)%和(96.78±0.34)%。所提方法具有较高的情绪识别准确度和较低的计算复杂度,适用于实际场景中的情绪识别。

基于时空和频域特征的EEG帕金森疾病识别

脑电图(EEG)中蕴含着有关脑功能的丰富信息,这些信息对不同类型神经系统疾病的检测和诊断非常重要。针对单一特征无法充分表达脑电信号的问题,本文融合了频域特征和时空信息来更好的对信号进行表征,并提出一种基于时空和频域特征的注意力网络(STFACN)用于帕金森疾病(PD)的自动检测。在频域角度,利用快速傅里叶变换法从多通道脑电图中求取Delta、Theta、Alpha频段的平均功率特征。同时构建基于时空特征的紧凑型卷积神经网络,并将通道注意力机制嵌入到网络中,自适应提取表征PD的时空特征。最后将基于频域特征的模型与基于时空特征的紧凑型卷积神经网络模型进行融合,在新墨西哥州大学(UNM)数据集上进行实验,特异性、敏感性、准确率分别达到87.97%、84.39%、86.89%。在爱荷华大学(UI)数据集上进行跨数据集实验,准确率达到77.33%。实验结果表明:与现有的方法相比,本文提出的方法能够从原始脑电图中挖掘出有效特征,在基于EEG的帕金森疾病识别问题上准确率高,泛化能力强。

EEG Signal Denoising and Feature Extraction Using Wavelet Transform in Brain Computer Interface

Electroencephalogram(EEG) signal preprocessing is one of the most important techniques in brain computer interface(BCI).The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition.Wavelet transform is a method of multi-resolution time-frequency analysis,it can decompose the mixed signals which consist of different frequencies into different frequency band.EEG signal is analyzed and denoised using wavelet transform.Moreover,wavelet transform can be used for EEG feature extraction.The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features.The eigenvector for classification is obtained by combining the effective features from different channels.The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition,the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.

基于EEG的船舶驾驶员疲劳程度识别

大约80%的水上交通事故涉及人为因素,驾驶员疲劳是船舶交通事故发生的关键原因之一。近年来,基于脑电图(Electroencephalogram,EEG)的驾驶员疲劳检测技术的发展,有助于快速准确地识别驾驶员的疲劳程度。然而,由于EEG信号的敏感性和个体差异,影响驾驶员疲劳检测的准确性。该试验在船舶模拟器中进行,收集多个受试者的脑电信号。选取与疲劳相关的脑前额叶的3个通道脑电信号进行预处理,并提取基于EEG的多种特征,例如平均绝对值(Mean Absolute Value,MAV)、标准差(Standard Deviation,SD)、均方根(Root Mean Square,RMS)和香农熵(Shannon Entropy,SE)。基于卡罗林斯卡嗜睡量(Karolinska Sleepiness Scale,KSS)表将驾驶员的疲劳分为清醒、中等和疲劳等3个程度。将多种分类算法的分类准确率进行比较,双向长短期记忆网络(Bi-Long Short Term Memory,Bi-LSTM)分类器效果最佳,分类准确率达到88.63%。结果表明:该方法在研究船舶驾驶员跨个体的三分类问题中能获得显著的效果。

3DMKDR:3D Multiscale Kernels CNN Model for Depression Recognition Based on EEG

Depression has become a major health threat around the world,especially for older people,so the effective detection method for depression is a great public health challenge.Electroencephalogram(EEG)can be used as a biomarker to effectively explore depression recognition.Motivated by the studies that multiple smaller scale kernels could increase nonlinear expression compared to a larger kernel,this article proposes a model named the three-dimensional multiscale kernels convolutional neural network model for the depression disorder recognition(3DMKDR),which is a three-dimensional convolutional neural network model with multiscale convolutional kernels for depression recognition based on EEG signals.A three-dimensional structure of the EEG is built by extending one-dimensional feature sequences into a two-dimensional electrode matrix to excavate the related spatiotemporal information among electrodes and the collected electrode matrix.By the major depressive disorder(MDD)and the multi-modal open dataset for mental-disorder analysis(MODMA)datasets,the experiment shows that the accuracies of depression recognition are up to99.86%and 98.01%in the subject-dependent experiment,and 95.80%and 82.27%in the subjectindependent experiment,which are higher than alternative competitive methods.The experimental results demonstrate that the proposed 3DMKDR is potentially useful for depression recognition in older persons in the future.

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

Human Stress Recognition by Correlating Vision and EEG Data

Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.

基于脑电图的帕金森轻度认知障碍功能网络特征分析

帕金森氏轻度认知障碍(PDMCI)是帕金森氏症患者痴呆的先兆,这对使用神经评分量表和医生经验等传统方法进行准确诊断提出了挑战。利用26名PDMCI患者和23名正常人的脑电信号,基于定向传递函数构建了Delta、Theta、Alpha、Beta和Gamma频段的脑功能网络。引入了一种新颖的图论特征——效率密度来捕获网络密度和传输效率。研究结果揭示了独特的连接模式,Delta和Theta波段的连接更紧密,而Alpha、Beta和Gamma波段的连接更稀疏。帕金森病(PD)患者与对照组之间的Theta、Alpha、Beta和Gamma频带存在显著差异(p<0.05)。因此,脑功能网络可以有效反映PD脑功能异常状态,效率密度特征可以反映PD脑功能异常活动的特征量。

多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别

在结合脑电(EEG)信号与人脸图像的双模态情感识别领域中,通常存在两个挑战性问题:(1)如何从EEG信号中以端到端方式学习到更具显著性的情感语义特征;(2)如何充分利用双模态信息,捕捉双模态特征中情感语义的一致性与互补性。为此,提出了多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别模型。一方面,为从EEG信号中获得更具显著性的情感语义特征,设计了多层次时空特征自适应集成模块。该模块首先通过双流结构捕捉EEG信号的时空特征,再通过特征相似度加权并集成各层次的特征,最后利用门控机制自适应地学习各层次相对重要的情感特征。另一方面,为挖掘EEG信号与人脸图像之间的情感语义一致性与互补性,设计了特有-共享特征融合模块,通过特有特征的学习和共享特征的学习来联合学习情感语义特征,并结合损失函数实现各模态特有语义信息和模态间共享语义信息的自动提取。在DEAP和MAHNOB-HCI两种数据集上,采用跨实验验证和5折交叉验证两种实验手段验证了提出模型的性能。实验结果表明,该模型取得了具有竞争力的结果,为基于EEG信号与人脸图像的双模态情感识别提供了一种有效的解决方案。

高血压性颅内出血患者预后不良的定量脑电图特征

目的:探讨高血压性颅内出血患者预后不良的定量脑电图特征。方法:前瞻性连续收集2017年1月至2023年6月在开滦总医院神经内科重症加强治疗病房诊治的高血压性颅内出血患者69例为研究对象。根据出院30d后的改良Rankin评分(mRS评分)随访结果将患者分为两组:预后良好组(mRS≤2分)、预后不良组(mRS3~6分)。收集患者一般资料、qEEG监测指标〔左、右侧前额区(Fp1、Fp2)、额区(F3、F4)、中央区(C3、C4)、顶区(P3、P4)、枕区(O1、O2)、前颞区(F7、F8)、中颞区(T3、T4)、后颞区(T5、T6)的α、θ、δ、β脑波功率值。结果:高血压性颅内出血预后不良组年龄大于预后良好组,P<0.05。高血压性颅内出血预后不良组10-20导联中Fp2、F4、C4、P4、F7-8、T4导联δ脑波功率值高于预后良好组,P<0.05。预后不良组10-20导联中Fp1-2、F3、C3、P4、O1、F7、T3导联θ脑波功率值高于预后良好组,P<0.05。预后不良组10-20导联中Fp1-2、F3-4、C3-4、P3-4、O1、F7-8、T4、全脑平均慢波指数功率值高于预后良好组,P<0.05。结论:高血压性颅内出血预后不良患者,定量电脑图δ、θ、全脑平均慢波指数功率值呈明显增高改变。

基于微分熵及卷积神经网络的脑电运动想象分类识别

针对基于运动想象的脑电信号多分类识别准确率不高的问题,提出一种基于微分熵及卷积神经网络对运动想象四分类的识别方法。首先,将脑电信号通过滤波器提取为Alpha、Beta、Theta、Gamma 4个频段,分别计算各个频段的微分熵特征,并按照脑电极空间特征对数据结构进行重构为三维脑电信号特征立方体。最后,将其输入卷积神经网络进行四分类,该方法基于BCI Competition IV-2a公开数据集,准确率达到95.88%,并在试验室建立四分类运动想象数据集进行相同的处理,准确率达到94.50%。测试结果表明本文所提方法具有更好的识别效果。

基于眼动和脑电特征的高速铁路行车调度员疲劳状态判别研究

针对当前高速铁路行车调度员疲劳状态判别方法尚未结合多源信息的优势,提出了一种基于多源信息的高速铁路行车调度员疲劳状态判别方法,在利用支持向量机对眼动和脑电特征进行软分类作为决策层输入的基础上,结合上一时间点疲劳状态判别结果,利用改进的D-S证据理论进行决策层融合得到最终的判别结果。通过采集20名行车调度员的模拟实验数据对模型进行检测,结果表明:改进后的研究方法在疲劳状态判别方面准确率达到93.75%。可以看出,信息融合判别的准确率高于单纯依靠眼动和脑电特征的方法,增加上一时间点的疲劳判别结果有助于提高模型的鲁棒性和可靠性,并且改进的模型对高速铁路行车调度员疲劳状态判别具有有效性。

基于改进局部图结构的再认记忆脑电特征提取

为了研究再认记忆脑电的纹理特征,以及解决垂直对称局部图结构和对称局部图结构在提取脑电纹理特征时结构不稳定的问题。基于新旧范式设计了再认记忆实验,采集医学生和非医学生(均为35名)相关脑电,并且将这些脑电分为学习医学图片阶段、学习非医学图片阶段、再认旧医学图片阶段、再认旧非医学图片阶段、再认新医学图片阶段和再认新非医学图片阶段。首先,利用二维小波变换得到每位被试脑电的三个子频带,并提出改进集成局部图结构方法对原数据和3个子频带进行特征提取,改进算法纳入了具有稳定结构的扩展对称局部图结构和复合局部图结构;然后对特征进行归一化,避免结果过拟合,使用皮尔逊相关系数筛选出相关系数在0.8~1之间的特征矩阵列。在支持向量机等分类器上验证改进前后的算法,并使用正确率、精确率、召回率和F1评分这四个指标对模型进行评估。与改进前算法相比,改进后算法在支持向量机上的分类正确率分别提升3.8%,0.4%,0.3%,1.6%,5.1%和4.2%。分类结果说明医学生和非医学生在医学图片学习再认阶段存在明显差异,新加入扩展对称局部图结构和复合局部图结构比原算法中垂直对称局部图结构和对称局部图结构具有更好的分类性能。