Eye Blink Artifacts Research Articles

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

Objective. Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main results. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

Mental Tasking and Caloric-Induced Vestibular Nystagmus Utilizing Videonystagmography.

The purpose of this study was to quantify the effects of mental tasking on measures of the caloric vestibulo-ocular reflex utilizing videonystagmography as the measurement technique. A within-subjects repeated-measures design was utilized. Sixteen healthy adults were evaluated (13 women, 3 men; ages 19-31 years). Each participant underwent bithermal caloric irrigation at 2 separate counterbalanced visits. At 1 visit mental tasking was utilized, whereas the other visit did not utilize mental tasking. The following outcomes were measured for each visit: peak slow-phase velocity (SPV), response duration, peak SPV latency, and eye blink artifact. No significant difference was seen for tasking versus no tasking with peak SPV, peak latency, or response duration. A significant difference was seen for the amount of eye blink artifact, with significantly more eye blinks present for the tasking condition. Results could indicate mental tasking does not affect the important measure of SPV. Moreover, increased eye blink artifact with tasking could obscure the clinician's ability to read the nystagmograph. However, this investigation is limited to the healthy young adult population, and more studies should be performed to corroborate the presented evidence.

Spectral entropy feature subset selection using SEPCOR to detect alcoholic impact on gamma sub band visual event related potentials of multichannel electroencephalograms (EEG)

The problem of analyzing and identifying regions of high discrimination between alcoholics and controls in a multichannel electroencephalogram (EEG) signal is modeled as a feature subset selection technique that can improve the recognition rate between both groups. Several studies have reported efficient detection of alcoholics by feature extraction and selection in gamma band visual event related potentials (ERP) of a multichannel EEG signal. However, in these studies the correlation between features and their class information is not considered for feature selection. This may lead to redundancy in the feature set and result in over fitting. Therefore in this study, a statistical feature selection technique based on Separability & Correlation analysis (SEPCOR) is proposed to select an optimal feature subset automatically that possesses minimum correlation between selected channels and maximum class separation. The optimal feature selection consists of a ranking method that assigns ranks to channels based on a variability measure (V-measure). From the ranked feature set of highly discriminative features, different subsets are automatically selected by heuristically applying a correlation threshold in steps from 0.02 to 0.1. These subsets are applied as input features to multilayer perceptron (MLP) neural network and k-nearest neighbor (k-NN) classifiers to discriminate alcoholic and control visual ERP. Prior to feature selection, spectral entropy features are computed in gamma sub band (30–55Hz) interval of a 61-channel multi-trial EEG signal with multiple object recognition tasks. Independent Component Analysis (ICA) is performed on raw EEG data to remove eye blink, motion and muscle artifacts. Results indicate that both classifiers exhibit excellent classification accuracy of 99.6%, for a feature subset of 22 optimal channels with correlation threshold of 0.1. In terms of computation time, k-NN classifier outperforms multilayer perceptron-back propagation (MLP-BP) network with 7.93s whereas MLP network takes 55s to perform the recognition task with the same accuracy. Compared to feature section methods used in previous studies on the same EEG alcoholic database, there is a significant improvement in classification accuracy based on the proposed SEPCOR method.

An improved artifacts removal method for high dimensional EEG

BackgroundMultiple noncephalic electrical sources superpose with brain signals in the recorded EEG. Blind source separation (BSS) methods such as independent component analysis (ICA) have been shown to separate noncephalic artifacts as unique components. However, robust and objective identification of artifact components remains a challenge in practice. In addition, with high dimensional data, ICA requires a large number of observations for stable solutions. Moreover, using signals from long recordings to provide the large observation set might violate the stationarity assumption of ICA due to signal changes over time. New methodInstead of decomposing all channels simultaneously, subsets of channels are randomly selected and decomposed with ICA. With reduced dimensionality of the subsets, much less amount of data is required to derive stable components. To characterize each independent component, an artifact relevance index (ARI) is calculated by template matching each component with a model of the artifact. Automatic artifact identification is then implemented based on the statistical distribution of ARI of the numerous components generated. ResultsThe proposed permutation resampling for identification matching (PRIM) method effectively removed eye blink artifacts from both simulated and real EEG. Comparison with existing methodThe average topomap correlation coefficient between the cleaned EEG and the ground truth is 0.89±0.01 for PRIM, compared with 0.64±0.05 for conventional ICA based method. The average relative root-mean-square error is 0.40±0.01 for PRIM, compared with 0.66±0.10 for conventional method. ConclusionsThe proposed method overcame limitations of conventional ICA based method and succeeded in removing eye blink artifacts automatically.

Gamma band dysfunction in patients with schizophrenia during a Sternberg Task: A wavelet analysis

BackgroundIncreasing body of evidence suggest that patients with schizophrenia (SCZ) present dysfunction of the gamma band oscillations (GBO) during cognitive tasks. The current study aimed to explore the GBO activity in SCZ during a Sternberg task.Materials and methodsTwenty-eight chronic stabilized SCZ and 18 healthy controls (HC), were recruited. Ongoing EEG was recorded during the execution of the Sternberg task. Continuous EEG data were band-pass filtered (1–100 Hz) and corrected for eye blink and muscle artefacts by ICA. For each subject, the event-related-spectral-perturbation (ERSP) and the inter-trial-coherence (ITC) were computed at the Pz channel only for those stimulus-locked segments containing correct responses. GBO wavelet analysis was performed with two different increasing cycle ranges (3 to 5.8 and 12 to 22.6; frequency range: 30–90 Hz), to obtain the best information about temporal and frequency dynamics. Student's t test (with multiple comparisons FDR correction) was used to compare the groups.ResultsDuring the manteinance phase (4000 to 4600 ms after the stimulus onset), SCZ presented a significant increase, respect to HC, in low GBO activity (range: 30-50 Hz;). In the other phases of the Sternberg task (encoding, probe presentation and response periods), no significant difference in GBO was observed between SCZ and HC.ConclusionsThese findings are in line with the evidence that GBO dysfunction in SCZ is present during selective phases of the working memory task. Future studies have to clarify the role of GBO dysfunction on the cognitive performance and the clinical utility of selective GBO modulation during cognitive rehabilitation.Disclosure of interestThe authors have not supplied their declaration of competing interest.

An unsupervised eye blink artifact detection method for real-time electroencephalogram processing

Electroencephalogram (EEG) is easily contaminated by unwanted physiological artifacts, among which electrooculogram (EOG) artifacts due to eye blinking are known to be most dominant. The eye blink artifacts are reported to affect theta and alpha rhythms of frontal EEG signals, and hard to be accurately detected in an unsupervised way due to large individual variability. In this study, we propose a new method for detecting eye blink artifacts automatically in real time without using any labeled training data. The proposed method combined our previous method for detecting eye blink artifacts based on digital filters with an automatic thresholding algorithm. The proposed method was evaluated using EEG data acquired from 24 participants. Two conventional algorithms were implemented and their performances were compared with that of the proposed method. The main contributions of this study are (1) confirming that individual thresholding is necessary for artifact detection, (2) proposing a novel algorithm structure to detect blink artifacts in a real-time environment without any a priori knowledge, and (3) demonstrating that the length of training data can be minimized through the use of a real-time adaption procedure.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram

The effect of voluntary and involuntary eyeblinks in independent components (ICs) contributing to electroencephalographic (EEG) signals was assessed to create templates for eyeblink artifact rejection from EEG signals with small number of electrodes. Fourteen EEG and one vertical electrooculographic signals were recorded for twenty subjects during experiments that prompted subjects to blink voluntarily and involuntarily. Wavelet-enhanced independent component analysis with two markers was employed as a feature extraction scheme to investigate the effects of eyeblinks in ICs of EEG signals. Extracted features were separated into epochs and analyzed. This paper presents following characteristics: (i) voluntary and involuntary eyeblink features obtained from all channels present significant differences in the delta band; (ii) distorting effects have continued influence for 3.0–4.0s (in the occipital region, 2.0s); and (iii) eyeblink effects cease to exist after the zero-crossing four (in the occipital region, two) times, regardless of the type. Several characteristics are different between voluntary and involuntary eyeblinks in EEG signals. Therefore, any templates need both types of data for eyeblink artifact rejection if the EEG signals were obtained from small number of electrodes.

Driving drowsiness detection using fusion of electroencephalography, electrooculography, and driving quality signals

This study investigates the detection of the drowsiness state (DS) for future application such as in the reduction of the road traffic accidents. The electroencephalography, electrooculography, driving quality, and Karolinska sleepiness scale data of 7 males during approximately 20 h of sleep deprivation were recorded. To reduce the eye blink artifact, an automatic mechanism based on the independent component analysis method and Higuchi's fractal dimension has been applied. After recordings, for selecting the best subset of features, a new combined method, called class separability feature selection-sequential feature selection, has been developed. This method reduces the time of calculations from 6807 to 2096 s (by 69.21%) while the classification accuracy remains relatively unchanged. For diagnosis of the DS and classification of the state, a new approach based on a self-organized map network is used. First, using the data obtained from two classes of awareness state (AS) and DS, the network achieved an accuracy of 76.51 ± 3.43%. Using data from three classes of AS, AS/DS (passing from awareness to drowsiness), and DS to the network, an accuracy of 62.70 ± 3.65% was achieved. It is suggested that the DS during driving is detectable with an unsupervised network.

Beta activity in the premotor cortex is increased during stabilized as compared to normal walking.

Walking on two legs is inherently unstable. Still, we humans perform remarkable well at it, mostly without falling. To gain more understanding of the role of the brain in controlling gait stability we measured brain activity using electro-encephalography (EEG) during stabilized and normal walking. Subjects walked on a treadmill in two conditions, each lasting 10 min; normal, and while being laterally stabilized by elastic cords. Kinematics of trunk and feet, electro-myography (EMG) of neck muscles, as well as 64-channel EEG were recorded. To assess gait stability the local divergence exponent, step width, and trunk range of motion were calculated from the kinematic data. We used independent component (IC) analysis to remove movement, EMG, and eyeblink artifacts from the EEG, after which dynamic imaging of coherent sources beamformers were determined to identify cortical sources that showed a significant difference between conditions. Stabilized walking led to a significant increase in gait stability, i.e., lower local divergence exponents. Beamforming analysis of the beta band activity revealed significant sources in bilateral pre-motor cortices. Projection of sensor data on these sources showed a significant difference only in the left premotor area, with higher beta power during stabilized walking, specifically around push-off, although only significant around contralateral push-off. It appears that even during steady gait the cortex is involved in the control of stability.

Detection of eye blink artifacts from single prefrontal channel electroencephalogram

Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain–computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.

Eye blink artifact rejection in single-channel electroencephalographic signals by complete ensemble empirical mode decomposition and independent component analysis.

To study an eye blink artifact rejection scheme from single-channel electroencephalographic (EEG) signals has been now a major challenge in the field of EEG signal processing. High removal performance is still needed to more strictly investigate pattern of EEG features. This paper proposes a new eye blink artifact rejection scheme from single-channel EEG signals by combining complete ensemble empirical mode decomposition (CEEMD) and independent component analysis (ICA). We compare the separation performance of our proposed scheme with existing schemes (wavelet-ICA, EMD-ICA, and EEMD-ICA) though real-life data by using signal-to-noise ratio. As a result, CEEMD-ICA showed high performance (11.86 dB) than all other schemes (10.78, 10.59, and 11.30 dB) in the ability of eye blink artifact removal.

Comparison of three ICA algorithms for ocular artifact removal from TMS-EEG recordings.

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a powerful tool to investigate brain excitability and information processing in brain networks. However, EEG-TMS recordings are challenging because EEG is contaminated by powerful TMS-related artifacts. Because of these artifacts, different EEG-driven analyses (for instance, source analysis and analysis of information flow on the sensors and source level) reveal incorrect results. The aim of this study was to remove ocular artifacts from TMS-EEG recordings following stimulation of motor cortex using three independent component analysis (ICA) algorithms and to evaluate the effectiveness of these algorithms. We showed that the temporal ICA algorithm better separates those components that contain time-locked eye blink artifacts.

Novel Multipin Electrode Cap System for Dry Electroencephalography.

Current usage of electroencephalography (EEG) is limited to laboratory environments. Self-application of a multichannel wet EEG caps is practically impossible, since the application of state-of-the-art wet EEG sensors requires trained laboratory staff. We propose a novel EEG cap system with multipin dry electrodes overcoming this problem. We describe the design of a novel 24-pin dry electrode made from polyurethane and coated with Ag/AgCl. A textile cap system holds 97 of these dry electrodes. An EEG study with 20 volunteers compares the 97-channel dry EEG cap with a conventional 128-channel wet EEG cap for resting state EEG, alpha activity, eye blink artifacts and checkerboard pattern reversal visual evoked potentials. All volunteers report a good cap fit and good wearing comfort. Average impedances are below 150 kΩ for 92 out of 97 dry electrodes, enabling recording with standard EEG amplifiers. No significant differences are observed between wet and dry power spectral densities for all EEG bands. No significant differences are observed between the wet and dry global field power time courses of visual evoked potentials. The 2D interpolated topographic maps show significant differences of 3.52 and 0.44% of the map areas for the N75 and N145 VEP components, respectively. For the P100 component, no significant differences are observed. Dry multipin electrodes integrated in a textile EEG cap overcome the principle limitations of wet electrodes, allow rapid application of EEG multichannel caps by non-trained persons, and thus enable new fields of application for multichannel EEG acquisition.

Distributed Signal Processing for Wireless EEG Sensor Networks.

Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

半正定値テンソル分解による瞬きアーチファクト除去手法の提案

In this paper, we aim to propose an eye blink artifact rejection technique for single-channel electroencephalographic (EEG) devices. Independent component analysis (ICA) is usually employed for eye blink artifact rejection. However, ICA cannot be used for the artifact rejection when we recorded EEG signals by single-channel EEG devices, since this method requires multichannel signals for source estimation. On the other hand, single-channel EEG devices have been attracted attention since 2000 because of its usability for measurement and portability. In this paper, we propose positive semi-definite tensor factorization (PSDTF) with 2 step learning method as a new eye blink artifact rejection technique for single-channel EEG analysis. We investigate its validity using signal-to-noise ratio (SNR) between eye blink artifact signals estimated by our proposed method or ICA. Average value of SNR across subjects and trials is 16.13dB. For the value, the validity of our proposed method for eye blink artifact rejection of single-channel EEG signals was confirmed.

Single-channel Electroencephalographic Recording in Children with Developmental Coordination Disorder: Validity and influence of Eye Blink Artifacts

Background: The NeuroSky single-channel, dry-electrode, and wireless electroencephalographic (EEG) recording system is a fairly new measure of mental status, the validity of which had not yet been tested in children with developmental coordination disorder (DCD). Purpose: To examine the validity of the NeuroSky single-channel EEG recording device (Mindwave Mobile EEG headset and NeuroView data acquisition software) and investigate the influence of eye blink artifacts on EEG attention-meditation measurements in children with DCD. Methods: Thirty-seven children with DCD (with or without attention deficits) participated in the study. Validity was assessed primarily by correlating the EEG-derived attention and meditation indices with scores on other mental status measures (duration of gaze fixation and Movement Assessment Battery for Children bicycle/flower trial item score) in the DCD-attentive group and then comparing the EEG-derived attention and meditation indices of the DCD-attentive group (n = 20) with those of the DCD-inattentive group (n = 17) and among the frequent-blinking (7-8 eye blinks/trial), moderate-blinking (5-6 eye blinks/trial), and rare-blinking (3-4 eye blinks/trial) groups. Results: The EEG-derived attention index was correlated with the duration of gaze fixation (r = 0.648, p = 0.002) and the Movement Assessment Battery for Children bicycle/flower trial item score (r = -0.688, p = 0.001). A significant difference in the attention index was found between the DCD-attentive group and DCD-inattentive group (p = 0.003), but no significant results were found for the EEG-derived meditation index. With regard to eye blinks, no significant differences in the EEG-derived attention or meditation indices were noted between the three blinking groups (p = 0.887). Conclusion: The single-channel EEG device accurately measured the overall level of mental attention in children with DCD clinically and was not significant influenced by eye blinking. This portable device has potential utility in such children for whom ease of use is the first priority

Quantifying the Vestibulo-Ocular Reflex with Video-Oculography: Nature and Frequency of Artifacts

Video-oculography devices are now used to quantify the vestibulo-ocular reflex (VOR) at the bedside using the head impulse test (HIT). Little is known about the impact of disruptive phenomena (e.g. corrective saccades, nystagmus, fixation losses, eye-blink artifacts) on quantitative VOR assessment in acute vertigo. This study systematically characterized the frequency, nature, and impact of artifacts on HIT VOR measures. From a prospective study of 26 patients with acute vestibular syndrome (16 vestibular neuritis, 10 stroke), we classified findings using a structured coding manual. Of 1,358 individual HIT traces, 72% had abnormal disruptive saccades, 44% had at least one artifact, and 42% were uninterpretable. Physicians using quantitative recording devices to measure head impulse VOR responses for clinical diagnosis should be aware of the potential impact of disruptive eye movements and measurement artifacts. i 2014 S. Karger AG, Basel

Automated detection and correction of eye blink and muscular artefacts in EEG signal for analysis of Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is a neural development disorder affecting the information processing capability of the brain by altering how nerve cells and their synapses interconnect and organize. Electroencephalograph or EEG signals records the electrical activity of the brain from the scalp which can be utilized to identify and investigate the brain wave pattern which are specific to individuals with ASD. Therefore, the analysis of ASD can be done by scrutinizing the specific bands (Theta, Mu and Beta) of the EEG signal. However, EEG signals are mainly contaminated by Ocular (Eye-blink) and Myogenic artefacts which pose problems in EEG interpretation. In this paper an automated real-time method for detection and removal of Ocular and Myogenic artefacts for multichannel EEG signal is proposed which would enhance the diagnostic accuracy. The proposed methodology has been validated against 20 subjects from Caltech, Physionet, Swartz Center for Computational Neuroscience and the computed average correlation and regression are 0.7574 and 0.6992 respectively.

Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA

Brain activities commonly recorded using the electroencephalogram (EEG) are contaminated with ocular artifacts. These activities can be suppressed using a robust independent component analysis (ICA) tool, but its efficiency relies on manual intervention to accurately identify the independent artifactual components. In this paper, we present a new unsupervised, robust, and computationally fast statistical algorithm that uses modified multiscale sample entropy (mMSE) and Kurtosis to automatically identify the independent eye blink artifactual components, and subsequently denoise these components using biorthogonal wavelet decomposition. A 95% two-sided confidence interval of the mean is used to determine the threshold for Kurtosis and mMSE to identify the blink related components in the ICA decomposed data. The algorithm preserves the persistent neural activity in the independent components and removes only the artifactual activity. Results have shown improved performance in the reconstructed EEG signals using the proposed unsupervised algorithm in terms of mutual information, correlation coefficient, and spectral coherence in comparison with conventional zeroing-ICA and wavelet enhanced ICA artifact removal techniques. The algorithm achieves an average sensitivity of 90% and an average specificity of 98%, with average execution time for the datasets ( N = 7) of 0.06 s ( SD = 0.021) compared to the conventional wICA requiring 0.1078 s ( SD = 0.004). The proposed algorithm neither requires manual identification for artifactual components nor additional electrooculographic channel. The algorithm was tested for 12 channels, but might be useful for dense EEG systems.