基于有效注意力和GAN结合的脑卒中EEG增强算法

在基于脑电的卒中分类诊断任务中,以卷积神经网络为基础的深度模型得到广泛应用,但由于卒中类别病患样本数量少,导致数据集类别不平衡,降低了分类精度。现有的少数类数据增强方法大多采用生成对抗网络(GAN),生成效果一般,虽然可通过引入缩放点乘注意力改善样本生成质量,但存储及运算代价往往较大。针对此问题,构建一种基于线性有效注意力的渐进式数据增强算法LESA-CGAN。首先,算法采用双层自编码条件生成对抗网络架构,分别进行脑电标签特征提取及脑电样本生成,并使生成过程逐层精细化;其次,通过在编码部分引入线性有效自注意力(LESA)模块,加强脑电的标签隐层特征提取,并降低网络整体的运算复杂度。消融与对比实验结果表明,在合理的编码层数与生成数据比例下,LESA-CGAN与其他基准方法相比计算资源占用较少,且在样本生成质量指标上实现了10%的性能提升,各频段生成的脑电特征样本均更加自然,同时将病患分类的准确率和敏感度提高到了98.85%和98.79%。

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。

EEG光子处理器——基于衍射光子计算单元的癫痫发作检测

Electroencephalography(EEG)analysis extracts critical information from brain signals,enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces.However,performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices,especially with large neural network models.Herein,we propose an EEG opto-processor based on diffractive photonic computing units(DPUs)to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures.The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification,which monitors the brain state to identify symptoms of an epileptic seizure.We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets,that is,the Children’s Hospital Boston(CHB)–Massachusetts Institute of Technology(MIT)extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets,with excellent computing performance results.Along with the channel selection mechanism,both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis.Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.

Approach for epileptic EEG detection based on gradient boosting

The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.

Epileptic seizure detection using EEG signals and extreme gradient boosting

The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex data set are still not encountering expectations.The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances.Two methods are explored:a standard partitioning on a recent and larger version of the TUSZ,and a leave-one-out approach used to increase the amount of data for the training set.XGBoost,a fast implementation of the gradient boosting classifier,is the ideal algorithm for these tasks.The performances obtained are in the range of what is reported until now in the literature with deep learning models.We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types.We show that generalized seizures tend to be far better predicted than focal ones.We also notice that some EEG channels and features are more important than others to distinguish seizure from background.

An Efficient Method for Epileptic Seizure Detection in Long-Term EEG Recordings

Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, and neither national nor geographic boundaries. There are 50 million sufferers in the world today with 2.4 million new cases occur each year. Electroencephalogram (EEG) has become a traditional procedure to investigate abnormal functioning of brain activity. Epileptic EEG is usually characterized by short transients and sharp waves as spikes. Identification of such event splays a crucial role in epilepsy diagnosis and treatment. The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches. The method was applied to real clinical EEG data of epileptic patients and evaluated according to sensitivity, specificity, selectivity and average detection rate. The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

We are here to present a new method for the classification of epileptic seizures from electroencephalogram(EEG) signals.It consists of applying empirical mode decomposition(EMD) to extract the most relevant intrinsic mode functions(IMFs) and subsequent computation of the Teager and instantaneous energy,Higuchi and Petrosian fractal dimension,and detrended fluctuation analysis(DFA) for each IMF.We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures,even with segments of six seconds and a smaller number of channels(e.g.,an accuracy of0.93 using five channels).We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects,after reducing the number of instances,based on the k-means algorithm.

THE EFFECT OF ACUPUNCTURING ACUPOINTS ON THE CHANGE OF ELECTROENCEPHALOGRAM (EEG) IN ENDOTOXIC SHOCKED RATS

In present work,EEG and BP were used as the indexes to observe the relationbetween the change of EEG and the change of BP in the endotoxic shocked rats。At maintainingshock for 1 hr,dysrhythmia of EEG appeared in 38/46 cases.Simultaneously,there was a markeddrop in Bp,P【0.05.Following the shocked time prolonged,dysrhythmia was getting severe。AfterEA”Rengzhong"(n=14)or“Zusanli”(n=12),BP was significantly increased(P【0.05),anddysrhythmia of EEG showed clear improvement in most of the rats。There was a close relation be-tween the changes of EEG and BP,the change of EEG had a direct bearing on the change of BP.

基于改进Renyi熵算法的EEG心算任务识别

结构熵是度量网络复杂度的重要手段,为了弥补传统结构熵仅仅关注网络单一特性的问题,提出了一种改进Renyi熵算法来研究心算任务下的EEG脑网络,引入了两个重要网络属性——分形维数和介数中心性来提高网络复杂性的度量能力。之后,基于心算EEG数据计算两两电极间的相位锁定值(PLV),构建了复杂脑网络,并进行复杂度分析。结果表明,在α频段,心算状态下额叶与顶枕叶的脑同步性低于休息状态,心算状态的脑网络复杂性高于休息状态。利用支持向量机(SVM)实现了休息、心算状态的识别,算法识别准确率达到了88.42%。

Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction

This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms.The articles selected present important findings including new experimental results and theoretical studies.

Correlation between level of serum prolactin and epileptic discharges of electroencephalogram from 24 to 36 hours after epileptic onset

BACKGROUND： Researchers discovered that serum prolactin could rise following an epileptic seizure. The prolactin level might reach three times more than basic level within 30 minutes and decrease to the normal value 2 hours after the seizure occurred. The mechanism might result in an increase of serum prolactin concentrations with the activation of the hypothalamic-pituitary axis. OBJECTIVE：To probe into the correlation between changes of serum prolactin and incidence of epileptic discharges of electroencephalogram （EEG） at 24-36 hours after epileptic onset of patients with secondary epilepsy. DESIGN ： Clinical observational study SEI-FING： Department of Neurology, First Hospital affiliated to Soochow University PARTICIPANTS： A total of 21 patients with secondary epilepsy were selected from the Department of Neurological Emergency or Hospital Room of the First Hospital affiliated to Soochow University from November 2005 to April 2006. There were 14 males and 7 females aged from 25 to 72 years. All patients met International League Anti-epileptic （ILAE） criteria in 1981 for secondary generalized tonic clonic seizure through CT or MRI and previous EEG. All patients were consent. Primary diseases included cerebral trauma （3 cases）, tumor （2 cases）, stroke （7 cases） and intracranial infeion （9 cases）. METHODS ： Venous blood of all patients was collected at 24-36 hours after epileptic onset. Serum prolactin kit （Beckman Coulter, Inc in USA） was used to measure value of serum prolactin according to kit instruction. Then, value of serum prolactin was compared with the normal value （male： 2.64-13.13 mg/L; female： 3.34- 26.72 mg/L）; meanwhile, EEG equipment （American Nicolet Incorporation） was used in this study. MAIN OUTCOME MEASURES ： ① Abnormal rate of serum prolactin of patients with secondary epilepsy; ②Comparison between normal and abnormal level of serum prolactin and incidence of EEG epileptic discharge of patients with secondary epilepsy. RESULTS：All 21 patients with secondary epilepsy were involved in the final analysis. ① Results of serum prolactin level： Among 21 patients with of secondary epilepsy, 10 of them had normal serum prolactin and 11 had abnormal one, and the abnormal rate was 52% （11/21）. ② Detecting results of EEG： EEG results showed that 6 cases were normal and 15 were abnormal, and the abnormal rate was 71% （15/21）. The symptoms were sharp wave, spike wave or sharp slow wave, spike slow wave of epileptic discharges in 8 cases, which was accounted for 38%. ③ Correlation between abnormality of serum prolactin and EEG epileptic wave： Eleven cases had abnormal serum prolactin, and the incidence was 64% （7/11）, which was higher of epileptic wave than that of non-epileptic wave [36% （4/11）, P 〈 0.05]; however, 10 cases had normal serum prolactin, and the incidence was 10% （1/10）. Epileptic wave was lower than non-epileptic wave [90% （9/10）, P 〈 0.01]. CONCLUSION ： The level of serum prolactin of patients with secondary epilepsy is abnormally increased at 24- 36 hours after epileptic onset; in addition, incidence of epileptic discharge is also increased remarkably.

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.

Synthesized Multi-Method to Detect and Classify Epileptic Waves in EEG

In order to sufficiently exploit the advantages of different signal processing methods, such as wavelet transformation (WT), artificial neural networks (ANN) and expert rules (ER),a synthesized multi-method was introduced to detect and classify the epileptic waves in the EEG data. Using this method, at first, the epileptic waves were detected from pre-processed EEG data at different scales by WT, then the characteristic parameters of the chosen candidates of epileptic waves were extracted and sent into the well-trained ANN to identify and classify the true epileptic waves,and at last, the detected epileptic waves were certificated by ER. The statistic results of detection and classification show that, the synthesized multi-method has a good capacity to extract signal features and to shield the signals from the random noise. This method is especially fit for the analysis of the biomedical signals in biomedical engineering which are usually non-placid and nonlinear.

基于EEG和DE-CNN-GRU的情绪识别

近年,情绪识别研究已经不再局限于面部和语音识别,基于脑电等生理信号的情绪识别日趋火热.但由于特征信息提取不完整或者分类模型不适应等问题,使得情绪识别分类效果不佳.基于此,本文提出一种微分熵(DE)、卷积神经网络(CNN)和门控循环单元(GRU)结合的混合模型(DE-CNN-GRU)进行基于脑电的情绪识别研究.将预处理后的脑电信号分成5个频带,分别提取它们的DE特征作为初步特征,输入到CNN-GRU模型中进行深度特征提取,并结合Softmax进行分类.在SEED数据集上进行验证,该混合模型得到的平均准确率比单独使用CNN或GRU算法的平均准确率分别高出5.57%与13.82%.

基于多尺度卷积和自注意力特征融合的多模态情感识别方法

基于生理信号的情感识别受噪声等因素影响,存在准确率低和跨个体泛化能力弱的问题。对此,提出一种基于脑电(EEG)、心电(ECG)和眼动信号的多模态情感识别方法。首先,对生理信号进行多尺度卷积,获取更高维度的信号特征并减少参数量;其次,在多模态信号特征的融合中使用自注意力机制,以提升关键特征的权重并减少模态之间的特征干扰;最后,使用双向长短期记忆(Bi-LSTM)网络提取融合特征的时序信息并进行分类。实验结果表明,所提方法在效价、唤醒度和效价/唤醒度四分类任务上分别取得90.29%、91.38%和83.53%的识别准确率,相较于脑电单模态和脑电/心电双模态方法,准确率上提升了3.46~7.11和0.92~3.15个百分点。所提方法能够准确识别情感,在个体间的识别稳定性更好。

Double Deep Q-Network Decoder Based on EEG Brain-Computer Interface

Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.

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。

基于拓扑数据分析的驾驶疲劳EEG数据处理与优化分析研究

为提高驾驶疲劳脑电(EEG)数据处理与分析的准确性和鲁棒性,提出一种基于拓扑数据分析(TDA)的驾驶人疲劳脑电分析方法。首先利用汽车性能虚拟仿真平台开展驾驶实验,通过驾驶人状态反馈和面部特征视频,标记脑电数据,形成清醒和疲劳二分数据集。之后利用EEGLAB预处理数据,剔除噪声并保留0.3~30 Hz频带,直接从时域EEG数据中提取拓扑特征。此外还提取了经典频域特征α波能量和α/β用于对比分析。最后使用支持向量机进行分类。结果表明,基于持久同源(PH)的拓扑特征取得了高达88.7%的准确率和91.4%的召回率,与经典频域特征性能相当,且对脑电伪影的鲁棒性明显更好,在未剔除EEG伪影的情况下仍取得了87.4%的准确率和89.7%的召回率。综上所述,本文提出的用于驾驶疲劳脑电信号处理与分析的TDA方法抗干扰特性好、处理成本低、经济性高,有助于稳定、高效地处理驾驶人脑电数据并检测驾驶疲劳状态,具有较大的科学实际应用价值。

基于多频带路径签名特征的癫痫脑电图信号分类方法

基于脑电图(EEG)信号的癫痫自动检测对癫痫的临床诊断和治疗有很大的帮助。由于大部分癫痫识别算法忽略了EEG信号的时序关系,为此,文中提出了一种基于多频带路径签名特征的癫痫EEG信号分类方法。此方法首先将EEG信号分解成5个不同频段的频带信号,再通过路径签名算法进行特征提取,然后采用局部主成分分析去除特征相关性并进行特征融合,最后将融合特征送入集成分类器中进行预测分类。由于路径签名可以更深入地挖掘EEG信号的相关关系,结合局部主成分分析后,文中方法可以获取更有鉴别性的癫痫分类特征。分别在时长超过2 000 s癫痫发作片段的本地医院私有数据集和开源的CHB-MIT癫痫数据集上,选用10折交叉进行实验验证,结果表明:在私有数据集上,文中方法的平均分类准确率达到97.25%,比经典的基于经验模态分解(EMD)的方法提高了3.44个百分点,比最新的基于长短期记忆网络(LSTM)+卷积神经网络(CNN)的方法提高了1.35个百分点;在CHB-MIT数据集上,文中方法的平均分类准确率达到98.11%,比经典的基于EMD的方法提高了5.20个百分点,比最新的基于LSTM+CNN的方法提高了2.64个百分点;在两个数据集上文中方法的分类准确率均优于其他对比方法。