单通道脑电信号眼电伪迹去除算法研究

由眨眼和眼动产生的眼电伪迹（Electrooculography,EOG）信号是脑电信号（Electroencephalography,EEG）中的主要噪声信号之一.目前,多通道脑电信号中眼电伪迹的去除算法已经较为成熟.而在单通道脑电信号的眼电伪迹去除中,由于采集通道数量较少且缺乏参考眼电信号,目前尚无十分有效的去除方法.本文提出一种基于小波变换（Wavelet transform,WT）、集合经验模态分解（Ensemble empirical mode decomposition,EEMD）和独立成分分析（Independent component analysis,ICA）的WT-EEMD-ICA单通道脑电信号眼电伪迹去除算法.实验表明：WT-EEMD-ICA算法有效地解决了单通道WT-ICA算法中的超完备问题,能够有效去除单通道脑电信号中的眼电伪迹,并且分离出的眼电伪迹成分与参考通道采集的眼电信号相关性较强.     

表面肌电信号脉搏伪迹的消除方法研究

许多生物电信号的获取过程中都夹杂了脉搏伪迹,表面肌电信号(SEMG)也是一样。本文通过相邻部位同时采集两路SEMG,一路为待处理信号,另一路作为参考信号。采用小波变换提取参考SEMG中的脉搏波,与待处理的SEMG构建独立分量分析的输入,最后用FastICA算法分离出待处理SEMG中的脉搏波,得到去除脉搏伪迹的SEMG。实验结果表明,该方法用于SEMG中的脉搏伪迹的消除是非常有效的。     

Communication system using EOG for persons with disabilities and its judgment by EEG

The aim of this study was to present electrooculogram (EOG) signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from amyotrophic lateral sclerosis (ALS) or other diseases that prevent correct limb and facial muscular responses. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes. Investigating the possible usage of the EOG for human–computer interface, a relation between sight angle and EOG is determined. In other methodology, most famous approaches involve the use of a camera to visually track the eye. However, this method has problems that the eyes of user must always be open. In this paper, we propose the mouse cursor control system for ALS patients using EOG and electroencephalograph (EEG) signals. We introduced the algorithm using alternating current and direct current of EOG corresponding to the drift. Therefore, EOG measurement system we proposed improved the problems of artifacts caused by eye blinking which was not accepted for other systems, the displacement of electrode positions and the drift. In addition, we introduced the EEG measurement to examine whether the subject could control their eye movement consciously. The EEG signals were not used to control the mouse movement, but to determine the subject’s control state. In order to test whether our system works well, we prepared a questionnaire and asked the subjects to operate our system, and answer with YES or NO by moving the mouse cursor. During the task, we also recorded the subjects’ EEG by MYNDPLAY [7] and checked their conscious level. Three subjects participated in this experiment, and they had never operated this system before. In this experiment, we measured 30 states of EEG signals while EOG was also measuring for one eye movement and analyzed the EEG signals. The results of analysis of the EEG signal changes and the answers to questions indicated that at 26 of 30 states, the subjects’ conscious level while they were moving the cursor by EOG signals was correctly determined from the EEG signals. From these results, we could know that the EEG signals can be used to adjust the EOG system whether it works according to patients’ mind or just a misjudgment.     

用 EEGLAB 分析脑电信号磁

EEGLAB 是一种基于 Matlab 的工具箱。它主要用于处理连续记录的脑电信号（EEG）脑磁信号（MEG）和其它电生理数据。它运用的方法主要有独立分量分析（ICA ）、时间-频率分析[1]、绘制 ERP 图、排除伪迹和几种有用的可视化模式（对于求平均和单次提取数据）等。 EEGLAB 还为从事神经信号处理方法研究的开发人员提供了一个可扩展的开源的平台,他们利用邮件列表和世界各地的研究人员一起讨论新方法,研究出更多的 EEGLAB 的新插件。 EEGLAB 的插件可以通过下载设置后,直接融入并出现在用户菜单。 EEGLAB 可用于研究各种脑电信号,这些研究有助于对人类情绪探知和生理病理情况下的脑机制做研究,有助于了解人脑的工作原理,找到更有效的治疗精神疾病的方法。     

基于小波变换的动态心电信号伪差识别

由突变干扰引起的伪差会严重阻碍动态心电信号的自动分析和正确诊断,常规的小波消噪方法无法消除这类伪差干扰。提出一种新的识别心电信号突变干扰的方法,即利用小波多尺度分解并结合阈值判定算法对突变干扰这类伪差进行自动识别。采用实测的30组动态心电信号对该算法进行测试,实验结果表明,该算法能快速、有效地识别动态心电信号中的突变干扰,正确检出率达到96．4％,为正确诊断动态心电信号提供保障。     

A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

Neural Computing and Applications - Interference between EEG and EOG signals has been studied heavily in clinical EEG signal processing applications. But, in automatic sleep stage classification...     

基于脑电信号的思维任务分类

脑电信号(EEG)是研究脑活动的一种重要的信息来源,基于脑电信号的人与计算机的通信已成为一种新的人机接口方式。文中主要对不同心理作业的思维脑电信号运用独立分量分析进行预处理,然后采用AR模型提取特征,最后应用BP神经网络对AR系数特征进行训练和分类。实验表明,此方法可以达到很好的分类效果。     

基于脑电信号的思维任务分类

脑电信号（EEG）是研究脑活动的一种重要的信息来源，基于脑电信号的人与计算机的通信已成为一种新的人机接口方式。文中主要对不同心理作业的思维脑电信号运用独立分量分析进行预处理，然后采用AR模型提取特征，最后应用BP神经网络对AR系数特征进行训练和分类。实验表明，此方法可以达到很好的分类效果。     

基于Infomax算法的脑电信号中心电伪迹的消除

介绍了一种基于信息最大化(Infomax)算法的盲源分离(BlindSourceSeparation,简称BSS)方法,并将其应用于脑电信号的预处理中,有效地去除了脑电信号中的心电伪迹。     

Numerical Schemes of Shock Filter Models for Image Enhancement and Restoration

Considerable interest has recently been given to signal processing models based on partial differential equations. Successively improved models based on hyperbolic partial differential equation types are proposed in the literature. These models yield interesting results; however, it would be of great interest to generalize them in order to increase their efficiency. In this paper, we propose a generalized shock filter model for one-dimensional signal restoration. After justifying the existence and uniqueness of the solutions in an adequate vector space, we propose an effective numerical scheme to discretize the proposed model, and derive a two-dimensional numerical scheme directly from the one-dimensional model following a space-split strategy. We then prove a stability result for both schemes. We conclude our study by providing high-quality experimental results for one- and two-dimensional signal enhancement and restoration, and showing the influence the shock speed control has on processing time.     

基于脑电信号的青少年注意力检测和训练系统

青少年注意力不集中的现象在生活中十分普遍,而现有的青少年注意力检测和训练系统功能单一.因此,本文研发了一个基于脑电信号的青少年注意力检测和训练系统.针对注意力检测分类少、准确率低的问题,本文将注意力分为5类,并提出基于随机森林模型的注意力检测方法以改进检测的准确率,达76.17％;针对注意力效果不佳的问题,本文基于闭环脑电生物反馈感知技术,首次根据持续型注意力、选择型注意力和集中型注意力,分别设计3个面向青少年的严肃游戏训练模式,并提出4个评估指标进行实验,排除游戏熟悉程度对受试者影响的同时,结合自身对照法验证了注意力训练的有效性.     

基于局部模式的癫痫脑电信号自动分类方法

为有效地检测脑电图(EEG)中的癫痫信号,设计一维局部三值模式(1D-LTP)算子提取信号特征,并结合主成分分析(PCA)和极限学习机(ELM)对特征进行分类。通过1D-LTP算子计算信号点的顶层模式和底层模式下的特征变换码以准确滤除干扰信号,并对变换码直方图PCA降维后采用ELM进行分类,以10折交叉验证评估分类性能。实验结果表明,该方法能有效识别在癫痫发作期的EEG信号,其准确率可达99.79%。     

Application of Quantile Graphs to the Automated Analysis of EEG Signals

Neural Processing Letters - Epilepsy is classified as a chronic neurological disorder of the brain and affects approximately 2% of the world population. This disorder leads to a reduction in...     

脑电信号采集与管理系统的设计与实现

为了研究针灸情况下的脑电信号的特点,设计了一个基于Windows平台的脑电信号采集与管理系统.该系统可以进行脑电信号的实时采集显示、存储和处理,可以对采集的信号进行数据库管理,并将采集的数据进行回放.系统具有实时性好、界面友好、操作简单和信息管理便捷等优点.为中医针灸激励研究打下良好的基础.     

互耦效应下多组相干源的波达方位估计算法

对于实际环境中存在的多径现象和阵元间的互耦效应,提出一种互耦效应下针对相干源的波达方向估计算法。首先,通过波达方向矩阵法利用二阶矩求出互耦效应下的广义导向矢量;然后对广义导向矢量进行 子空间平滑,通过矩阵变换得到一个线性约束下的规划问题,实现相干源方位和互耦系数的级联估计。该算法只需利用二阶矩求得广义导向矢量,相比常规的四阶累积量方法,减少了计算量;本文算法在解互耦和解相干过程中都没有损失阵列孔径,极大提高了阵元利用效率。计算机仿真结果验证了该算法的有效性。     

面向特征融合的脑卒中脑电信号分类方法

为了实现脑卒中患者中脑梗死、脑出血两类疾病脑电信号的高效分类与检测,提出了一种基于小波包能量与近似熵特征提取结合的脑电自动分类预测方法。将输入的脑卒中病人的脑电信号进行小波包分解,提取各个频段的能量并降维,而后与近似熵融合作为特征向量,并用支持向量机算法对其进行分类。研究结果表明该方法有较强的脑电特征分类识别能力,进一步单独提取原始脑电信号α波段的信号进行分类,得到了更优的分类效果,脑卒中脑电信号的分类准确率可以达到98.36%。这对临床上实现脑卒中疾病的智能预测具有辅助决策作用。     

An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals

Machine Intelligence Research - This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram (EEG) data in an automatic way. This...     

面向癫痫EEG自适应识别的迁移径向基神经网络

在癫痫脑电图(electroencephalogram,EEG)信号识别中,传统的智能建模方法要求训练数据集和测试数据集均服从相同的分布。但在实际应用中,某些情况并不能满足此条件,进而导致传统方法性能急剧下降。针对上述情况,引入迁移学习策略,提出了适用于数据分布迁移环境的直推式径向基神经网络(transductive radial basis function neural network,TRBFNN)。该方法在癫痫EEG信号识别中的实验结果表明:直推式径向基神经网络具有较好的场景迁移适应性,对训练数据和测试数据存在差异时,识别性能不会出现急剧恶化的现象。     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

基于SAE和GNDO-SVM的脑电信号情绪识别

情感计算是现代人机交互中的关键问题, 随着人工智能的发展, 基于脑电信号(electroencephalogram, EEG)的情绪识别已经成为重要的研究方向. 为了提高情绪识别的分类精度, 本研究引入堆叠自动编码器(stacked auto-encoder, SAE)对EEG多通道信号进行深度特征提取, 并提出一种基于广义正态分布优化的支持向量机(generalized normal distribution optimization based support vector machine, GNDO-SVM)情绪识别模型. 实验结果表明, 与基于遗传算法、粒子群算法和麻雀搜索算法优化的支持向量机模型相比, 所提出的GNDO-SVM模型具有更优的分类性能, 基于SAE深度特征的情感识别准确率达到了90.94%, 表明SAE能够有效地挖掘EEG信号不同通道间的深度相关性信息. 因此, 利用SAE深度特征结合GNDO-SVM模型可以有效地实现EEG信号的情绪识别.