Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data

In this study, we propose an analysis system for single-trial classification of electroencephalogram (EEG) data. Combined with automatic EOG artifact removal and wavelet-based amplitude modulation (AM) features, the support vector machine (SVM) classifier is applied to the classification of left finger lifting and resting. Automatic EOG artifact removal is proposed to eliminate the EOG artifacts automatically by means of independent component analysis (ICA) and correlation coefficient. The features are then extracted from the discrete wavelet transform (DWT) data by the AM method. Finally, the SVM is used for the discriminant of wavelet-based AM features. Compared with EEG data without EOG artifact removal, band power features and LDA classifier, the proposed system achieves promising results in classification accuracy.     

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.     

典型相关分析去除脑电信号中眼电伪迹的研究

给出了一种基于典型相关分析（Canonical Correlation Analysis,CCA）的盲源分离技术来去除脑电信号中的眼电伪迹。通过实验验证了基于CCA的盲源分离方法去除眼电伪迹的有效性,并将该方法与广泛使用的独立分量分析（Independent Component Analysis,ICA）进行了比较。实验结果表明,基于CCA的盲源分离方法可以对眼电伪迹进行成功地分离和消除,该方法相较于ICA方法而言,算法更为简单,计算速度更快。     

Reliable EOG signal-based control approach with EEG signal judgment

This article proposes a reliable EOG signal-based control approach with EEG signal judgment. In this method, raw bio-neurological signals (including EOG and EEG) are first extracted and segmented in the pre-processing stage. The processed bio-neurological signals will then be evaluated by calculating the feature parameters of these signals. Since the feature parameters in bio-neurological signals may be contaminated by various kinds of artifacts, some artifacts of bio-neurological signals can be indicated by means of the feature parameters of bio-neurological signals. Therefore, the bio-neurological signals contaminated with artifacts cannot be adopted to generate control signals or to judge the correctness of control signals. In the proposed method, in order to generate a reliable control signal based on the EOG signal, the EEG signal is adopted to assist in making a judgment about the validity of the EOG signal. With the proposed method, an EOG signal-based control software platform has been implemented. By using this platform, simulation work has been carried out to control the behavior of a robot. The simulation results verified the effectiveness of the proposed method.     

基于独立分量分析的脑电信号的眼电伪迹消除

介绍了独立分量分析技术的基本概念和原理,及其具有代表性的基于负熵最大的快算独立分量分析算法和基于核空间的独立分量分析算法,并分别对脑电中的眼电伪迹进行去除。通过仿真实验表明了独立分量分析算法较快速独立分量分析算法能更好去除眼电伪迹,具有较好准确性和鲁棒性。     

Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

Frequent occurrence of ocular artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In the present paper, a novel and robust technique is proposed to eliminate ocular artifacts from EEG signals in real time. Independent Component Analysis (ICA) is used to decompose EEG signals. The features of topography and power spectral density of those components are extracted. Moreover, we introduce manifold learning algorithm, a recently popular dimensionality reduction technique, to reduce the dimensionality of initial features, and then those new features are fed to a classifier to identify ocular artifacts components. A k-nearest neighbor classifier is adopted to classify components because classification results show that manifold learning with the nearest neighbor algorithm works best. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove ocular artifacts effectively from EEG signals with little distortion of the underlying brain signals and be satisfied the real-time application.     

脑电信号中眼电伪迹自动识别与去除方法研究

传统盲源分离算法消除眼电伪迹须用到两个眼电信号作为参考,但在采集眼电信号时易给被试带来不适产生噪声,且识别时需要人为辨别,为了解决这些问题,提出一种基于FastICA的眼电伪迹自动去除方法。该方法先计算出FastICA提取出的各独立成分与GFP（Global Field Power）值的相关系数,再比较相关系数,将其绝对值最大所对应的独立成分识别为眼电伪迹独立成分,最后把该独立成分置零重构干净的脑电信号,实现眼电伪迹的自动去除。通过自采的30例脑电数据实验结果表明：该方法能完全自动地去除眼电伪迹成分并有效保留其他脑电成分,且快速准确,适用于实时场合。     

Improvements of Adaptive Filtering by Optimal Projection to filter different artifact types on long duration EEG recordings

Adaptive Filtering by Optimal Projection (AFOP) is an automatic method for reducing ocular and muscular artifacts on electro-encephalographic (EEG) recordings. This paper presents two additions to this method: an improvement of the stability of ocular artifact filtering and an adaptation of the method for filtering electrode artifacts. With these improvements, it is possible to reduce almost all the current types of artifacts, while preserving brain signals, particularly those characterising epilepsy. This generalised method consists of dividing the signal into several time-frequency windows, and in applying different spatial filters to each. Two steps are required to define one of these spatial filters: the first step consists of defining artifact spatial projection using the Common Spatial Pattern (CSP) method and the second consists of defining EEG spatial projection via regression. For this second step, a progressive orthogonalisation process is proposed to improve stability. This method has been tested on long-duration EEG recordings of epileptic patients. A neurologist quantified the ratio of removed artifacts and the ratio of preserved EEG. Among the 330 artifacted pages used for evaluation, readability was judged better for 78% of pages, equal for 20% of pages, and worse for 2%. Artifact amplitudes were reduced by 80% on average. At the same time, brain sources were preserved in amplitude from 70% to 95% depending on the type of waves (alpha, theta, delta, spikes, etc.). A blind comparison with manual Independent Component Analysis (ICA) was also realised. The results show that this method is competitive and useful for routine clinical practice.     

A deep wavelet sparse autoencoder method for online and automatic electrooculographical artifact removal

Electrooculographical (EOG) artifacts are problematic to electroencephalographical (EEG) signal analysis and degrade performance of brain–computer interfaces. A novel, robust deep wavelet sparse autoencoder (DWSAE) method is presented and validated for fully automated EOG artifact removal. DWSAE takes advantage of wavelet transform and sparse autoencoder to become a universal EOG artifact corrector. After being trained without supervision, the sparse autoencoder performs EOG correction on time–frequency coefficients collected after brain wave signal wavelet decomposition. Corrected coefficients are then used for wavelet reconstruction of uncontaminated EEG signals. DWSAE is compared with five other methods: second-order blind identification, information maximization, joint approximation diagonalization of eigen-matrices, wavelet neural network (WNN) and wavelet thresholding (WT). Experimental results on a visual attention task dataset, a mental state recognition dataset and a semi-simulated contaminated EEG dataset show that DWSAE is capable of suppressing EOG artifacts effectively, while preserving the nature of background EEG signals. The mean square error of signals before and after correction by DWSAE on a semi-simulated contaminated EEG segment of 30 s is the lowest (65.62) when compared to the results produced by WNN and WT. DWSAE addresses limitations posed by these methods in three ways. First, DWSAE can be performed automatically and online in a single channel of EEG data; this has advantages over independent component analysis-based methods. Second, its results are robust and stable in comparison with those of other wavelet-based methods. Third, as an unsupervised learning scheme, DWSAE does not require the off-line training that is necessary for WNN and other supervised learning machine learning-based methods.

     

基于典型相关分析与低通滤波的肌电伪迹去除

提出了一种基于典型相关分析(CCA)和低通滤波的盲源分离方法去除脑电信号(EEG)中的肌电伪迹.该方法首先将混入了肌电伪迹的EEG信号分解为不相关的CCA分量,然后对与伪迹源相关的分量进行低通滤波处理,去除这些分量中的高频伪迹成分,最后利用与EEG相关的CCA分量和滤波处理后的新分量重构信号,消除肌电伪迹的影响.实验结果表明,采用CCA能够有效地分离出肌电伪迹,而结合低通滤波技术能够更有效地保留EEG信息.该方法取得了较好的去除肌电伪迹的效果.     

Clustering technique-based least square support vector machine for EEG signal classification

This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.     

一种基于脑电信号的眼动方向分类方法

为了提高基于眼电的眼动方向的识别准确性,文中利用包含眼电伪迹的脑电信号,提出了一种新的眼动方向分类方法。首先,在10-20国际标准导联配置下,通过脑电仪采集靠近人脑额叶处的AF7,F7,FT7,T7,AF8,F8,FT8,T8这8个通道的脑电信号;然后,通过基线移除、归一化、最小二乘法降噪等进行数据预处理;最后,采用支持向量机的方法进行眼动方向的多次二分类,并使用投票策略实现眼动方向的四分类识别。实验结果表明,所提方法进行眼动方向分类时,在上、下、左、右4个方向上的分类率分别达到了78.47%,72.22%,84.03%,79.86%,平均分类率达到了78.65%。与已有的分类方法相比,所提方法的分类准确率更高,分类算法的实现过程更简单,这进一步验证了利用脑电信号识别眼动方向的可行性和有效性。     

基于CEEMDAN-ICA的单通道脑电信号眼电伪迹滤除方法

传统盲源分离法不能解决欠定问题,且分离信号与源信号对应关系不确定.提出一种基于自适应噪声完备经验模态分解(CEEMDAN)和独立成分分析(ICA)相结合的脑电信号眼电伪迹自动去除方法.该方法首先将含伪迹脑电信号自适应分解成多维本征模态函数(IMF),以满足盲源分离方法对信号正定或超定要求,再对本征模态函数用ICA方法构建多维源信号,最后利用模糊熵阈值判据判别多维源信号中的伪迹信号,完成滤波并重构脑电信号.该方法相比于其他算法,能更好的去除眼电伪迹并保留原始信息,适合单通道脑电信号预处理.     

基于CuBICA算法的EEG伪迹去除方法

在高阶累积量和独立分量分析的基础上,提出一种基于CuBICA算法的脑电信号伪迹去除方法。针对脑电信号中常含有的眼电、心电等伪迹问题,利用小波包方法对原始脑电信号去噪,并进行中心化和白化处理,运用CuBICA算法对消噪后的脑电信号进行盲源分 离。分析分离后各信号间相关性,结果表明,CuBICA算法能成功分离脑电、眼电与心电信号,有效去除纯脑电信号中的各种伪迹。     

A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis

ElectroEncephaloGram (EEG) gives information about the electrical characteristics of the brain. EEG can be used for various applications, such as diagnosis of diseases, neuroscience and Brain Computer Interface (BCI). Several artefacts sources can disturb the brain signals in EEG measurements. The signals caused by eye movements are the most important sources of artefacts that must be removed in order to obtain a clean EEG signal. During the removal of Ocular Artefacts (OAs), the preserve of the original EEG signal is one of the most important points to be taken into account. An ElectroOculoGram (EOG) reference signal is needed in order to remove OAs in some methods. However, long-term EOG measurements can disturb a subject. In this paper, a novel robust method is proposed in order to remove OAs automatically from EEG without EOG reference signal by combining Outlier Detection and Independent Component Analysis (OD-ICA). The OD-ICA method searches OA patterns in all components instead of a single component. Moreover, OD-ICA removes only OA patterns and preserves meaningful EEG signal. In this method, user intervention is not needed. These advantages make the method robust. The OD-ICA is tested on two real datasets. Relative Error (RE), Correlation Coefficient (CorrCoeff) and percentage of finding OA pattern are used for the performance test. Furthermore, three different methods are used as Outlier Detection (OD) methods. These are the Chauvenet Criterion, the Peirce's Criterion and the Adjusted Box Plot. The performance analysis is made between our proposed method and the method of zeroing the component with artefact. The experiment results show that the proposed OD-ICA method effectively removes OAs from EEG signals and is also successful in preserving the meaningful EEG signals during the removal of OAs.     

A Machine Learning Approach for Artifact Removal from Brain Signal

Electroencephalography (EEG), helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond range. To extract clean clinical information from EEG signals, it is essential to remove unwanted artifacts that are due to different causes including at the time of acquisition. In this piece of work, the authors considered the EEG signal contaminated with Electrocardiogram (ECG) artifacts that occurs mostly in cardiac patients. The clean EEG is taken from the openly available Mendeley database whereas the ECG signal is collected from the Physionet database to create artifacts in the EEG signal and verify the proposed algorithm. Being the artifactual signal is non-linear and non-stationary the Random Vector Functional Link Network (RVFLN) model is used in this case. The Machine Learning approach has taken a leading role in every field of current research and RVFLN is one of them. For the proof of adaptive nature, the model is designed with EEG as a reference and artifactual EEG as input. The peaks of ECG signals are evaluated for artifact estimation as the amplitude is higher than the EEG signal. To vary the weight and reduce the error, an exponentially weighted Recursive Least Square(RLS) algorithm is used to design the adaptive filter with the novel RVFLN model. The random vectors are considered in this model with a radial basis function to satisfy the required signal experimentation. It is found that the result is excellent in terms of Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Relative Error (RE), Gain in Signal to Artifact Ratio (GSAR), Signal Noise Ratio(SNR), Information Quantity (IQ), and Improvement in Normalized Power Spectrum (INPS). Also, the proposed method is compared with the earlier methods to show its efficacy.     

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.     

EEG signal classification using wavelet feature extraction and a mixture of expert model

Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the “gating function”. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.     

Removal of the ocular artifacts from EEG data using a cascaded spatio-temporal processing

Eye movements and blinks may produce unusual voltage changes in human electroencephalogram (EEG). These effects may spread across scalp and mask brain signals. In this paper, a cascaded spatio-temporal processing procedure (CAST) is presented to remove artifact electrooculogram (EOG). Firstly a discrete equivalent distributed source on the cortical surface is reconstructed from the contaminated scalp recordings by a linear minimum norm estimation (i.e. a spatial analysis step). Then, the equivalent sources of EOG are identified by principal component analysis (PCA) of the equivalent distributed source time series (i.e. a temporal analysis step). Finally, the EOG-corrected scalp EEG is reconstructed from the equivalent distributed source where EOG components have been removed. The effectiveness of CAST is confirmed by the application to actual scalp data and a detailed comparative study.     

EEG signal preprocessing for biometric recognition

Electroencephalography (EEG) has been recently investigated as a biometric modality for automatic people recognition purposes. Several studies have shown that brain signals possess subject-specific traits that allow distinguishing people. Nonetheless, extracting discriminative characteristics from EEG recordings may be a challenging task, due to the significant presence of artifacts in the acquired data. In order to cope with such issue, in this paper we evaluate the effectiveness of some preprocessing techniques in automatically removing undesired EEG contributions, with the aim of improving the achievable recognition rates. Specifically, methods based on blind source separation and sample entropy estimation are here investigated. An extensive set of experimental tests, performed over a large database comprising recordings taken from 50 healthy subjects during three distinct sessions spanning a period of about one month, in both eyes-closed and eyes-open conditions, is carried out to analyze the performance of the proposed approaches.