Ocular Artifacts Research Articles

Effects of signal artefacts on electroencephalography spectral power during sleep: quantifying the effectiveness of automated artefact-rejection algorithms.

Electroencephalography (EEG) recordings during sleep are often contaminated by muscle and ocular artefacts, which can affect the results of spectral power analyses significantly. However, the extent to which these artefacts affect EEG spectral power across different sleep states has not been quantified explicitly. Consequently, the effectiveness of automated artefact-rejection algorithms in minimizing these effects has not been characterized fully. To address these issues, we analysed standard 10-channel EEG recordings from 20 subjects during one night of sleep. We compared their spectral power when the recordings were contaminated by artefacts and after we removed them by visual inspection or by using automated artefact-rejection algorithms. During both rapid eye movement (REM) and non-REM (NREM) sleep, muscle artefacts contaminated no more than 5% of the EEG data across all channels. However, they corrupted delta, beta and gamma power levels substantially by up to 126, 171 and 938%, respectively, relative to the power level computed from artefact-free data. Although ocular artefacts were infrequent during NREM sleep, they affected up to 16% of the frontal and temporal EEG channels during REM sleep, primarily corrupting delta power by up to 33%. For both REM and NREM sleep, the automated artefact-rejection algorithms matched power levels to within ~10% of the artefact-free power level for each EEG channel and frequency band. In summary, although muscle and ocular artefacts affect only a small fraction of EEG data, they affect EEG spectral power significantly. This suggests the importance of using artefact-rejection algorithms before analysing EEG data.

Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics

Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step.

Robust Granger Analysis in Lp Norm Space for Directed EEG Network Analysis.

Granger analysis (GA) is widely used to construct directed brain networks based on various physiological recordings, such as functional magnetic resonance imaging, and electroencephalogram (EEG). However, in real applications, EEGs are inevitably contaminated by unexpected artifacts that may distort the networks because of the L2 norm structure utilized in GAs when estimating directed links. Compared with the L2 norm, the Lp ( ) norm can compress outlier effects. In this paper, an extended GA is constructed by applying the Lp ( ) norm strategy to estimate robust causalities under outlier conditions, and a feasible iteration procedure is utilized to solve the new GA model. A quantitative evaluation using a predefined simulation network demonstrates smaller bias errors and higher linkage consistence for the Lp ( , 0.8, 0.6, 0.4, 0.2) -GAs compared with both the Lasso- and L2-GAs under various simulated outlier conditions. Applications in resting-state EEGs that contain ocular artifacts also show that the proposed GA can effectively compress the ocular outlier influence and recover the reliable networks. The proposed Lp-GA may be helpful in capturing the reliable network structure when EEGs are contaminated with artifacts in related studies.

Surrogate-Based Artifact Removal From Single-Channel EEG.

the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this paper, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR). by means of the time-frequency analysis of surrogate data, our approach is able to identify and filter automatically ocular and muscular artifacts embedded in single-channel EEG. in a comparative study using artificially contaminated EEG signals, the efficacy of the algorithm in terms of noise removal and signal distortion was superior to other traditionally-employed single-channel EEG denoizing techniques: wavelet thresholding and the canonical correlation analysis combined with an advanced version of the empirical mode decomposition. Even in the presence of mild and severe artifacts, our artifact removal method provides a relative error 4 to 5 times lower than traditional techniques. in view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring, or anesthesia monitoring, where very few EEG channels or even a single channel is available.

Automatic minimization of ocular artifacts from electroencephalogram: A novel approach by combining Complete EEMD with Adaptive Noise and Renyi's Entropy

Ocular artifacts (OAs) are one of the major interferences that obscure electroencephalogram (EEG) signals. In this paper, a novel, completely automatic, adaptive and fast method that combines the Complete Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Renyi's Entropy (RE) is proposed for minimizing the OAs from corrupted EEG signals. The RE criterion is suggested to automatically select the Intrinsic Mode Functions (IMFs) to reconstruct the artifact minimized EEG signals. The scheme requires only a single channel OAs corrupted EEG recording and a reasonable computation time. The method is first evaluated on simulated OAs (one, two, and several blinks as well as saccadic eye movements) corrupted EEG signals and then extended to real EEG signals. The signal-to-noise ratio improvement (SNRimp) along with time and power spectral density (PSD) plots are used for evaluating the performance of the scheme. The method is compared to the one based on the CEEMDAN and manual choice of IMFs for OAs minimization from EEG. Results from extensive simulation studies clearly indicate the efficacy of the proposed scheme in automatically minimizing the OAs from the corrupted EEG signals.

Retinal tractography: Influence of diffusion acquisition, ocular laterality and ocular dominance in healthy subjects

Purpose Exploration of the human retina has never been described with MRI tractography, supposedly because of ocular movement-related artifacts and imaging distortions with EPI diffusion acquisition. In this work, we used track-weighting imaging to assess retinal fascicles. Diffusion acquisition parameters influence was studied and the relationship between variability and ocular laterality and ocular dominance was explored. Materials and Methods Twenty healthy subjects underwent MRI, with retinal tractography calculated with the constrained spherical deconvolution model. A DWI protocol with b value of 1000 s/mm 2 and 60 diffusion weighting directions was structured in 4 scans of 15 directions, and b = 0 s/mm 2 value repeated at each scan to ease motion estimation and correction. At post-processing, we merged subsets of 15 directions according to varied combinations. We generated whole-brain tractography with 10 million streamlines based on 30, 45 and 60 diffusion weighting directions. Ocular dominance was determined using a hole-in-a-card test. Results Retinal nerve fiber layer appeared to have a heterogeneous distribution, with a fascicle-like display on tractography. Diffusion acquisition required at least 45 directions to display retinal fascicles. There was no significant difference on the number of fascicles displayed between 45 and 60 direction acquisitions, whereas there were significantly more fascicles using 60 and 45 directions than with 30 directions ( P ≤0.001). Regarding ocular dominance (OD), we observed there were more superior fascicles on the right OD group than on the left one ( P ≤0.05), whereas there were more inferior fascicles on the left OD group ( P ≤0.05). Conclusion Although the distribution of reconstructed retinal fascicles depends on the number of diffusion weighting directions used, assessment of ocular laterality and dominance–related variability seems possible.

Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach

Electroencephalography (EEG) is a non-invasive way of recording brain activities, making it useful for diagnosing various neurological disorders. However, artifact signals associated with eye blinks or the heart spread across the scalp, contaminating EEG recordings and making EEG data analysis difficult. To solve this problem, we implement a common methodology to suppress both cardiac and ocular artifact signal, by correlating the measured contaminated EEG signals with the clean reference electro-oculography (EOG) and electrocardiography (EKG) data and subtracting the scaled EOG and EKG from the contaminated EEG recording. In the proposed methodology, the clean EOG and EKG signals are extracted by subjecting the raw reference time-series data to ensemble empirical mode decomposition to obtain the intrinsic mode functions. Then, an unsupervised technique is used to capture the artifact components. We compare the distortion introduced into the brain signal after artifact suppression using the proposed method with those obtained using conventional regression alone and with a wavelet-based approach. The results show that the proposed method outperforms the other techniques, with an additional advantage of being a common methodology for the suppression of two types of artifact.

Fast and Efficient Four‑class Motor Imagery Electroencephalography Signal Analysis Using Common Spatial Pattern– Ridge Regression Algorithm for the Purpose of Brain– Computer Interface

Brain-computer interfaces enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. One of the most challenging issues in this regard is the balance between the accuracy of brain signals from patients and the speed of interpreting them into machine language. The main objective of this paper is to analyze different approaches to achieve the balance more quickly and in a better way. To reduce the ocular artifacts, the symmetric prewhitening independent component analysis (ICA) algorithm has been evaluated, which has the lowest runtime and lowest signal-to-interference (SIR) index, without destroying the original signal. After quick elimination of all undesirable signals, two successful feature extractors - the log-band power algorithm and common spatial patterns (CSPs) - are used to extract features. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during the imagination of the tongue, feet, and left-right-hand movement. Finally, three well-known classifiers are evaluated, where the ridge regression classifier and CSPs as feature extractor have the highest accuracy classification rate about 83.06% with a standard deviation of 1.22%, counterposing the recent studies.

33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface

A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an auto-matic method “Kmeans-ICA” which does not require a reference channel. This method starts by decomposing EEG signals into Independent Components; artefactual ones are then identified using Kmeans clustering, a non-supervised machine learning technique. After signal preprocessing, a Brain computer interface system is implemented; physiologically interpretable features extracting the wavelet-coherence, the wavelet-phase locking value and band power are computed and introduced into a statistical test to check for a significant difference between relaxed and motor imagery states. Features which pass the test are conserved and used for classification. Leave One Out Cross Validation is performed to evaluate the performance of the classifier. Two types of classifiers are compared: a Linear Discriminant Analysis and a Support Vector Machine. Using a Linear Discriminant Analysis, classification accuracy improved from 66% to 88.10% after ocular artifacts removal using Kmeans-ICA. The proposed methodology outperformed state of art feature extraction methods, namely, the mu rhythm band power.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis.

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

Dealing with ocular artifacts on lateralized ERPs in studies of visual-spatial attention and memory: ICA correction versus epoch rejection.

The objective of the present study was to assess the robustness and reliability of independent component analysis (ICA) as a method for ocular artifact correction in electrophysiological studies of visual-spatial attention and memory. The N2pc and sustained posterior contralateral negativity (SPCN), electrophysiological markers of visual-spatial attention and memory, respectively, are lateralized posterior ERPs typically observed following the presentation of lateral stimuli (targets and distractors) along with instructions to maintain fixation on the center of the visual search for the entire trial. Traditionally, trials in which subjects may have displaced their gaze are rejected based on a cutoff threshold, minimizing electrophysiological contamination by saccades. Given the loss of data resulting from rejection, we examined ocular correction by comparing results using standard fixation instructions against a condition where subjects were instructed to shift their gaze toward possible targets. Both conditions were analyzed using a rejection threshold and ICA correction for saccade activity management. Results demonstrate that ICA conserves data that would have otherwise been removed and leaves the underlying neural activity intact, as demonstrated by experimental manipulations previously shown to modulate the N2pc and the SPCN. Not only does ICA salvage and not distort data, but also large eye movements had only subtle effects. Overall, the findings provide convincing evidence for ICA correction for not only special cases (e.g., subjects did not follow fixation instruction) but also as a candidate for standard ocular artifact management in electrophysiological studies interested in visual-spatial attention and memory.

Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm

Electroencephalography (EEG) is a non-invasive technique to record brain activities in natural settings. Ocular Artifacts (OA) usually contaminates EEG signals, removal of which is critical for accurate feature extraction and classification. With the increasing adoption of wearable technologies, single-channel real-time EEG systems that often require real-time signal processing for immediate real-time feedback are becoming more prevalent. However, traditional OA removal algorithms usually require multiple channels of EEG data, are computationally expensive, and do not perform well in real-time. In this article, a new hybrid algorithm is proposed that autonomously detects OA and subsequently removes OA from a single-channel steaming EEG data in real-time. The proposed single EEG channel algorithm also does not require additional reference electrooculography (EOG) channel. The algorithm has also been implemented on an embedded hardware platform of single channel wearable EEG system (NeuroMonitor). The algorithm first detects the OA zones using an Algebraic approach and then removes these artifacts from the detected OA zones using the Discrete Wavelet Transform (DWT) decomposition method. The de-noising technique is applied only to the OA zone, which minimizes loss of neural information outside the OA zone. A qualitative and quantitative performance evaluation was carried out with a 0.5s epoch in overlapping sliding window technique using time-frequency analysis, mean square coherence, and correlation coefficient statistics. The hybrid OA removal algorithm demonstrated real-time operation with 3s latency on the PSoC-3-microcontroller-based EEG system. Successful implementation of OA removal from single-channel real-time EEG data using the proposed algorithm shows promise for real-time feedback applications of wearable EEG devices.

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.

Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach

Removal of ocular artifacts (OA) in real-time is an essential component in electroencephalography (EEG) based brain computer interface (BCI) applications. However, many proposed artifact removal methods are not applicable in real-time applications due to their time-consuming process. In this paper we propose a hybrid approach based on a new combination of independent component analysis (ICA) and adaptive noise cancellation (ANC). A particularly new feature of the proposed approach is the utilization of the ICA decomposition to extract the artifact source signal to be used in ANC based on neural networks. The method performs using a few EEG signals without requiring any additional electrodes (e.g. electrooculography). We show that the proposed approach is capable of effectively reducing the ocular artifacts in a negligible time delay well applicable in real-time BCI. In order to achieve reliable results, the proposed approach is evaluated using data recorded during cue-based BCI. The efficacy of the proposed approach in both offline and online performances is compared to several state of the art methods. The results demonstrate that the proposed approach outperforms the compared methods in terms of removal of OA and recovery of the underlying EEG.

Functional Cortical networks associated with personality, emotional intelligence and decision making

Background: Many studies have investigated the neural correlates associated with personality by using a variety of neuroimaging techniques and psychometrics (Kennis et al., 2013). Previous electroencephalogram (EEG) studies have examined the relationship between emotional traits (Stenberg 1995) and personality traits (Takahashi et al 2005). In this EEG based pilot study, a multidisciplinary strategy of combining brain imaging techniques (LORETA) and EEG coherence analysis with psychological constructs was applied to explore the relationships between personality traits. In particular, the relationship between emotional intelligence with participants’ reactions and decision making to rich complex stimuli such as advertising videos was examined. Methods: A preliminary study group of 45 participants, (Mean age 30.8 years SD 11.9 years, Education = 14.8 years SD 1.5 years ) had their EEG recorded with a Quikcap electrode system while watching a series of videos which were selected based on various marketing categories such as, food & drink, community interests, celebrities and social issues. Using the Swinburne University Emotional Intelligence Test or SUEIT (Stough, Palmer 2001), individuals were categorized into two groupings; those who scored high and low in emotional intelligence (EI). EEG was analyzed offline to remove muscle and ocular artifact. Results: Selected epochs of EEG were then analyzed to produce LORETA and EEG coherence data during decision making. Preliminary findings suggest that participants with high or low emotional intelligence (EI) were receptive to certain types of social issues or visual imagery. This was validated by the associated brain functional connectivity networks for each group. Also, correlations with other personality scales (NEO-PI Costa & McCrae 1992) demonstrated personality types react differently to imagery, content and humor. Discussion/Conclusion: Both LORETA and EEG coherence data demonstrated distinct functional connectivity associated with each group and may help to explain their associated perceptions and behaviors. More specifically, the high EI group demonstrated clear preferences in specific video material when contrasted with the low EI group. The NEO-PI has been used in previous psychophysiology studies of emotional arousability but not with complex social stimuli. The use of a multidisciplinary approach can assist in the identification of the neural networks associated with personality and provide insights into individual differences in perception.

Unscented Kalman filter for neural decoding of human treadmill walking from non-invasive electroencephalography.

The feasibility of decoding lower limb kinematics in human treadmill walking from noninvasive electroencephalography (EEG) has been demonstrated with linear Wiener filter. However, nonlinear relationship between neural activities and limb movements may challenge the linear decoders in real-time brain computer interface (BCI) applications. In this study, we propose a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower limb joint angles from noninvasive scalp EEG signals during human treadmill walking. Our results demonstrate that lower limb joint angles during treadmill walking can be decoded from the fluctuations in the amplitude of slow cortical potentials in the delta band (0.1-3Hz). Overall, the average decoding accuracy were 0.43 ± 0.18 for Pearson's r value and 1.82 ± 3.07 for signal to noise ratio (SNR), and robust to ocular, muscle, or movement artifacts. Moreover, the signal preprocessing scheme and the design of UKF allow the implementation of the proposed EEG-based BCI for real-time applications. This has implications for the development of closed-loop EEG-based BCI systems for gait rehabilitation after stroke.

Classification of artifactual EEG signal and detection of multiple eye movement artifact zones using novel Time-amplitude algorithm

Electroencephalogram (EEG) signal has numerous applications in the field of medical science. It is used to diagnose many of the abnormalities, disorders, and diseases related to the human brain. The EEG signal contaminated with ocular artifacts makes it very difficult for analysis and diagnosis. This paper includes work on classification of artifactual/non-artifactual EEG time series and perfect detection of eye movement (EM) artifact contaminated EEG signal along with multiple EM artifactual zones in the same time series. Artificial Neural Network classifier in a simple perceptron model without hidden layer is used for the identification. This study presents a newly developed, simple, robust, and computationally fast statistical Time-Amplitude algorithm. By the application of novel Time-Amplitude algorithm on identified signal, the EM artifactual EEG signal along with multiple zones is automatically detected and marked accurately. Such robust, efficient, real-time and simple algorithm is not ever designed and used for ocular artifact detection by any author. The ROC analysis gives accuracy of the ANN model for classifying the presence of artifacts in the EEG data, which is 97.50 %. The time elapsed for executing the Time-Amplitude algorithm for automatic detection of EM artifact is very less (4.30 msec.) compared to DWT with Haar. It has the capability to detect multiple EM artifactual zones, in the same time, for the montage of 8-second EEG.

On potential ocular artefacts in infant electroencephalogram: a reply to comments by Köster.

Koster's comment [[1][1]] on Kampis et al. [[2][2]] adopts an objection that was put forward previously regarding the interpretation of scalp-recorded gamma-band electroencephalogram (EEG) activity in adults as a correlate of object processing. Gamma-band (over 25 Hz) oscillatory activity has been

Study on the Impact of Light on Human Physiology and Electroencephalogram

The influence of light on Electroencephalogram seems to be more critical, when physician depends on its readings to diagnose subject brain disorder according to its level. Although, bright light brings various changes in human physiological variables. But, Neurophysiological changes for specific light level still remain unclear. Thereby, in the present study, the response of brain electrical activity towards various light levels has been classified and verified. The reasons for alteration in different physiological variables due to bright light which results change in the state of electroencephalographic recordings has been reviewed. Additionally, the effects of bright light towards Ocular artifacts are also put forward. A novel hypothesis has been made using subjective analysis and experimental analysis in low luminance or darkness. It is observed that a poor lighting condition affects both behavioral task and brain activity.

ERP correlates of error processing during performance on the Halstead Category Test

The Halstead Category Test (HCT) is a neuropsychological test that measures a person's ability to formulate and apply abstract principles. Performance must be adjusted based on feedback after each trial and errors are common until the underlying rules are discovered. Event-related potential (ERP) studies associated with the HCT are lacking. This paper demonstrates the use of a methodology inspired on Singular Spectrum Analysis (SSA) applied to EEG signals, to remove high amplitude ocular and movement artifacts during performance on the test. This filtering technique introduces no phase or latency distortions, with minimum loss of relevant EEG information. Importantly, the test was applied in its original clinical format, without introducing adaptations to ERP recordings. After signal treatment, the feedback-related negativity (FRN) wave, which is related to error-processing, was identified. This component peaked around 250ms, after feedback, in fronto-central electrodes. As expected, errors elicited more negative amplitudes than correct responses. Results are discussed in terms of the increased clinical potential that coupling ERP information with behavioral performance data can bring to the specificity of the HCT in diagnosing different types of impairment in frontal brain function.