Differential Contributions of Blinks and Vertical Eye Movements as Artifacts in EEG Recording

Blinks and vertical eye movements were studied as artifacts of EEG recording. The electro-oculogram (EOG) and vertex vs joined mastoids EEG were recorded in 13 college-aged subjects. Subjects were asked to blink “normally, without excessive effort,” and move their eyes through vertical visual arcs of 5°, 10°, 20°, 30°, and 60°. The ratio EEG/EOG, the fraction of the EOG potential transmitting to the scalp EEG electrode as artifact, was calculated for potentials generated during both blinks and eye movement. Vertical eye movement scalp EEG artifact was a constant percentage of the vertical eye movement EOG across visual arcs of 10° to 60°. Mean percentage eye blink EEG artifact (9.3%) was significantly (p < .001) less than the mean percentage vertical eye movement artifact (13.9%). Thus, blink and vertical eye movement artifact fields are quantitatively different in terms of their transmission to the scalp (C_z) EEG electrode. Subtraction of a single subject specific percentage of the EOG from the EEG would correct for either artifact source, but different subtraction percentages must be used for each.     

New spectral detection and elimination test algorithms of ECG and EOG artefacts in neonatal EEG recordings

New methods for the detection of ECG and EOG artefacts in the EEG are introduced, which can also be used for the evaluation of the quality of the elimination procedure. These algorithms are based on the estimation of the power or coherence spectrum by means of FFT. The advantage of this method is that the EEG spectrum is monitored by the test algorithm. The spectrum will be plotted if no influence of the ECG and EOG can be found. On the other hand, if artefacts are detected the correction of the EEG time series will be carried out and the spectrum of the corrected EEG time series will be plotted after repeated monitoring by the test algorithm.     

The Correction of EOG Artifacts by Frequency Dependent and Frequency Independent Methods

Methods for correcting EEG which is contaminated by EOG artifacts, which are frequency dependent or frequency independent, were compared. EEG activity contaminated mainly by eye movements was treated separately from activity contaminated mainly by blinks. The statistical comparison was based on representative, real data incorporating differential effects over frequency. The influence of EOG activity on different derivations and bands, and the resulting need for correction of EOG artifacts were of interest too. Correction using a constant gain function proved to be consistently inferior to using a gain depending on frequency. For eye movements, differences were, however, not very large in practical terms, and this was true for all frequency bands. The correction of blinks by the time domain method (i.e., constant gain) may become misleading in the sub-delta band, which might have implications for very slow activity such as slow cortical potentials. Transfer functions not corrected for coherent EEG activity at the EOG electrodes overcompensated in the alpha and beta bands, in line with earlier results showing the need to incorporate coherent EEG activity into the determination of EOG-EEG transfer. It was found to be preferable to use sample average coefficients and gain functions, rather than individual ones, leading also to simpler computing. EOG correction is indispensable at frontal derivations, in particular for the delta and theta bands. It is advisable to correct at central and even at parietal derivations.     

基于变分模态分解的眼电伪迹去除

脑电信号可以反映人体大脑活动状态,精确地将脑内信息传递向外界,对脑科学研究具有重要的意义。在实际情况中,脑电信号采集的同时会带有一些噪声,而眼电伪迹的存在会严重干扰脑电信号。本研究尝试了一种基于变分模态分解的眼电伪迹去除方法。通过变分模态分解将采集到的脑电信号分解成K组模态分量;根据眼电伪迹的频率特点,选择出眼电伪迹所对应的模态分量,并将其去除后重新构建剩余的模态分量。结果表明通过对实验数据的处理,变分模态分解可以有效地将眼电伪迹去除,并维持脑电信号的特征。     

Temporal stability of regression‐based electrooculographic correction coefficients

A means of accounting for ocular artifact in the electroencephalograph (EEG) is to subtract portions (Bs) of ocular voltage measured by the electrooculograph (EOG) from the EEG. Some such EOG correction methods calculate Bs at one time and use these to correct data recorded at a different time; these require information about the temporal stability of the Bs. This study investigated the stability of Bs over a 2‐hr EEG recording session. Participants performed 5 eye movement tasks, each separated by 30 min. Four EOG correction methods were then used to calculate Bs from each of the 5 data sets, resulting in VEOG, HEOG, and REOG (where appropriate) Bs for each methods at each of the 5 time points. We did not find evidence that Bs changed over the 2‐hr period, nor of any difference in temporal stability between the methods. This study suggests that it is appropriate to employ Bs calculated from calibration trials to correct data recorded within at least a 2‐hr time window.     

EOG correction: Which regression should we use?

Electrooculogram (EOG) correction is used to remove eye-movement-related contamination from electroencephalograms (EEG). Correction is reliant on the regression procedure, although when multiple EOG channels are used in the correction, the appropriate type of regression to use is not known. In the present study, we aimed to resolve this matter. Computer simulations were used to compare the simultaneous, multiple-stage, and single-channel regression methods of correction. EOG propagation was modeled on prior findings, under conditions of varying vertical and horizontal EOG (VEOG/HEOG) correlation. The dependent variable was the correlation between the uncontaminated and the corrected EEG. The simultaneous regression procedure gave the best correction, with its advantage increasing as a function of VEOG/HEOG correlation. It is recommended that the simultaneous regression procedure be used for EOG correction of the EEG.     

The Removal of Ocular Artifacts from EEG Signals Using Adaptive Filters Based on Ocular Source Components

Ocular artifacts are the most important form of interference in electroencephalogram (EEG) signals. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the patient. In contrast, blind source separation (BSS) is a method of decomposing multiple EEG channels into an equal number of source components (SCs) by independent component analysis. The ocular artifacts significantly contribute to some SCs but not others, so uncontaminated EEG signals can be obtained by discarding some or all of the affected SCs and re-mixing the remaining components. BSS can be performed without EOG data. This study presents a novel ocular-artifact removal method based on adaptive filtering using reference signals from the ocular SCs, which avoids the need for parallel EOG recordings. Based on the simulated EEG data derived from eight subjects, the new method achieved lower spectral errors and higher correlations between original uncorrupted samples and corrected samples than the adaptive filter using EOG signals and the standard BSS method, which demonstrated a better ocular-artifact reduction by the proposed method.     

Two-Year Retest Stability of Eye Tracking Performance and a Comparison of Electro-oculographic and Infrared Recording Techniques: Evidence of EEG in the Electro-oculogram

This study assessed the quantitative and qualitative comparability of simultaneously recorded electrooculographic (EOG) and infrared (IR) measures of eye movement and the long-term retest stability of the EOG. Fifty-two twins, all of whom performed the same tasks while the EOG was recorded two years ago, engaged in smooth-pursuit tracking of sinusoidally driven targets ranging in frequency from. 4 to 1.2 Hz and in saccadic tracking of a modified square wave target. In addition to computer-derived estimates of smooth-pursuit and saccadic eye tracking proficiency, a measure of saccadic interference during smooth-pursuit tracking and ratings of the amount of small-amplitude spiky oscillation in the EOG were made. Two-year retest stability of these various measures varied from .54 to .83 and were similar to within-session and one-week retest reliabilities computed for these same subjects. Correlations between computer-derived EOG and IR scores ranged from.89 to.99 indicating good correspondence between the two methods. Qualitative EOG-IR comparability was not high for subjects who showed a pronounced spiky oscillation in the EOG. Spectral analyses of the EOG and concurrently recorded EEG suggest that this spiky rhythm represents EEG (perhaps kappa or alpha), the presence of which is correlated with poor eye tracking.     

Removal of ocular artifacts from electro-encephalogram by adaptive filtering

The electro-encephalogram (EEG) is useful for clinical diagnosts and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and timeconsuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M=3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive leastsquares algorithm that includes a forgetting factor (λ=0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M=3) were significantly larger than any remaining coefficients.     

One-channel EOG artifact correction: an analytic approach

This article approaches the problem of EOG artifact correction using one EOG channel from a biophysical point of view. It shows that recordings from one EOG channel are sufficient to correct artifacts from one-dimensional eye movements not exceeding 30 degrees . We prove that the subtraction method "corrected EEG=measured EEG-backward propagation * measured EOG" yields the uncorrupted EEG trace up to scaling despite possible influences of forward propagation. Further, a special calibration paradigm (aligned artifact average, AAA) is investigated, and algorithms are presented to calculate the exact backward propagation. Experimental results from 13 subjects are shown, supporting the theoretical prediction of optimal correction.     

Evaluation of fatigue monitoring technologies

The purpose of this study was to evaluate commercially available devices for driver fatigue monitoring with particular focus on the needs of the mining industry. We present an overview of fatigue monitoring technologies (FMTs) and propose means to evaluate the devices. Three video-based devices were selected and used in an overnight driving simulation study to test their accuracy. In total 14 healthy volunteers performed the driving simulation tasks in eight test runs separated by breaks of approximately 10 min. EEG and EOG were recorded during the driving periods. The output variable of the FMT devices (percentage of eye closures, PERCLOS), subjectively rated fatigue on the Karolinska sleepiness scale (KSS), and driving performance in terms of standard deviation of lateral position in lane (SDL) were also recorded throughout testing sessions. Regression analysis revealed that PERCLOS is significantly related to higher KSS scores and to SDL. Calculations at a finer temporal resolution as well as on an intra-subject level showed decreased correlation coefficients. Discriminant analysis of PERCLOS and EEG/EOG suggested that PERCLOS does not differentiate well between mild and strong fatigue. The results suggest that under laboratory conditions current FMT devices are reliable when temporal resolution is not too fine (>30 min) and data averaged across several subjects is utilized, but fail to give a valid prediction of subjective fatigue as well as of driving performance on an individual level.     

Minimization of EOG artefacts from corrupted eeg signals using a neural network approach

In this paper, we propose a neural network (NN) approach to the enhancement of EEG signals in the presence of EOG artefacts. We recast the EEG enhancement problem into the optimization framework by developing an appropriate cost function. The cost function is nothing but the energy in the enhanced EEG signal obtained through a nonlinear filter formulation, unlike the conventionally-used linear filter formulation. The minimization property of feedback-type neural networks is exploited to solve this problem. An analysis has been performed to characterize the stationary points of the suggested energy function. The hardware set-up of the developed neural network has also been derived. The optimum nonlinear filter coefficients obtained from this minimization algorithm are used to estimate the EOG artefact which is then subtracted from the corrupted EEG signal, sample by sample, to get the artefact minimized signal. The time plots as the LP spectrum show that the proposed method is very effective. Thus the power and efficacy of the NN approach have been exploited for the purpose of minimizing EOG artefacts from corrupted EEG signals.     

Removal of the Ocular Artifact from the EEG: A Comparison of Time and Frequency Domain Methods with Simulated and Real Data

Frequency-dependent transfer from EOG to EEG may be insufficiently accounted for by simple time domain regression methods (Gasser, Sroka, & Möcks, 1986; Woestenburg, Verbaten, & Slangen, 1983). In contrast, a multiple-lag time domain regression analysis, using lagged regression of EEG on EOG, must theoretically account for both frequency dependence and independence. Two data sets were constructed, in which the transfer from EOG to EEG was either frequency-independent (constant gain) or frequency-dependent. Subsequently, three different correction methods were applied: 1) a simple regression analysis in the time domain; 2) a multiple-lag regression analysis in the time domain; and 3) a regression analysis in the frequency domain. The major results were that, for data set 1, the three methods constructed the original EEG equally well. With data set 2, reconstruction of the original EEG was achieved reasonably well with the frequency domain method and the time domain multiple-lag method, but not with simple time domain regression. These three correction procedures were also applied to real data, consisting of concomitantly recorded EEG and high-variance EOG series. No appreciable differences in outcome of the three methods were observed, and estimated transfer parameters suggested that these data were marked by weak frequency dependence only, which can be accounted for by simple time domain regression (and also by the other two methods).     

Automatic detection and operant reinforcement of slow potential shifts

A technique for the automatic detection and operant reinforcement of slow potential (SP) changes is described. The SP shift detection device contains 3 inhibit channels to control sources of potential artifact including: vertical EOG, horizontal EOG and high voltage EEG transients. Two EEG SP shift detection circuits allow the simultaneous analysis of positive and negative shifts. The operation and potential uses of the device are discussed.     

Computerized processing of EEG-EOG-EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials.

The aim of this study is to present a package including standard software for the electroencephalographic (EEG), electro-oculographic (EOG) and electromyographic (EMG) preliminary data analysis, which may be suitable to standardize the results of many EEG research centers studies (i.e. multi-centric studies) especially focused on event-related potentials. In particular, our software package includes (semi)automatic procedures for (i) EOG artifact detection and correction, (ii) EMG analysis, (iii) EEG artifact analysis, (iv) optimization of the ratio between artifact-free EEG channels and trials to be rejected. The performances of the software package on EOG-EEG-EMG data related to cognitive-motor tasks were evaluated with respect to the preliminary data analysis performed by two expert electroencephalographists (gold standard). Due to its extreme importance for multi-centric EEG studies, we compared the performances of two representative "regression" methods for the EOG correction in time and frequency domains. The aim was the selection of the most suitable method in the perspective of a multi-centric EEG study. The results showed an acceptable agreement of approximately 95% between the human and software behaviors, for the detection of vertical and horizontal EOG artifacts, the measurement of hand EMG responses for a cognitive-motor paradigm, the detection of involuntary mirror movements, and the detection of EEG artifacts. Furthermore, our results indicated a particular reliability of a 'regression' EOG correction method operating in time domain (i.e. ordinary least squares). These results suggest that such a software package could be used for multi-centric EEG studies.     

家庭远程医疗监护数据的传输及处理

介绍了家庭远程医疗监护系统，利用VB编程通过Intemet实现了监护数据的远程传输，医疗中心对接收到的监护数据可以进行去噪、去干扰、特征提取等预处理。在此基础上，实现脑电信号的远程传输，并对医疗中心接收到的脑电信号进行独立成分分析(ICA)分离眼动干扰的处理，取得了较好的效果。     

Removal of ocular artifacts from the REM sleep EEG.

The present report concerns the first study in which electrooculographic (EOG) contamination of electroencephalographic (EEG) recordings in rapid eye movement (REM) sleep is systematically investigated. Contamination of REM sleep EEG recordings in six subjects was evaluated in the frequency domain. REM-active and REM-quiet series were obtained for each subject. Transfer coefficients and power spectra of EOG and EEG indicated that (a) increases in transfer coefficients beyond 4.5 Hz are brought about by residual EEG in the EOG, and (b) EOG-EEG contamination in the delta band is most pronounced in frontal, intermediate in central and negligible in occipital leads. It was found that correction of the REM-active series resulted in significant (c) reductions in power, (d) increases in interhemispheric coherences and (e) reductions in degree of lateral asymmetry. These effects were largest for frontal leads, but still marked for central ones. The results are discussed in the light of previous findings concerning models of hemispheric functioning during REM sleep.     

A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case

Eye movement artifacts represent a critical issue for quantitative electroencephalography (EEG) analysis and a number of mathematical approaches have been proposed to reduce their contribution in EEG recordings. The aim of this paper was to objectively and quantitatively evaluate the performance of ocular filtering methods with respect to spectral target variables widely used in clinical and functional EEG studies. In particular the following methods were applied: regression analysis and some blind source separation (BSS) techniques based on second-order statistics (PCA, AMUSE and SOBI) and on higher-order statistics (JADE, INFOMAX and FASTICA). Considering blind source decomposition methods, a completely automatic procedure of BSS based on logical rules related to spectral and topographical information was proposed in order to identify the components related to ocular interference. The automatic procedure was applied in different montages of simulated EEG and electrooculography (EOG) recordings: a full montage with 19 EEG and 2 EOG channels, a reduced one with only 6 EEG leads and a third one where EOG channels were not available. Time and frequency results in all of them indicated that AMUSE and SOBI algorithms preserved and recovered more brain activity than the other methods mainly at anterior regions. In the case of full montage: (i) errors were lower than 5% for all spectral variables at anterior sites; and (ii) the highest improvement in the signal-to-artifact (SAR) ratio was obtained up to 40dB at these anterior sites. Finally, we concluded that second-order BSS-based algorithms (AMUSE and SOBI) provided an effective technique for eye movement removal even when EOG recordings were not available or when data length was short.     

Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. 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 EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.     

基于眼电和稳态视觉诱发电位分析的目光跟踪方法

眼动跟踪技术作为人机交互手段和行为检测方法已广泛应用于心理学和认知科学领域的研究,基于稳态视觉诱发电位的脑-机接口也是一种备受关注的人机交互方法。本研究提出一种结合眼电和稳态视觉诱发电位同步分析的眼睛注视点位置跟踪方法,通过同步检测两种电生理信号:眼电信号(EOG)和脑电信号(EEG)来实现。主要的处理算法有:基于EOG的人机交互算法,包括基线去除、去噪声、角度变换、基准校正等;基于SSVEP的脑-机接口算法,通过典型相关分析法实现。由SSVEP判断出的目标对应的屏幕坐标可以作为眼动分析中基准校正的输入参数。实验结果表明:每0.5 sEOG-HCI可以对注视点位置进行一次识别;每2 sSSVEP-BCI可以对注视目标做一次判别;两者既可以独立运行,也可以协同工作,相比单一信号的人机交互方法,可以缩短判断时间和提高准确率。