Reduction Of Ocular Artifacts Research Articles

Optimized deformable convolution network for detection and mitigation of ocular artifacts from EEG signal.

Electroencephalogram (EEG) is the key component in the field of analyzing brain activity and behavior. EEG signals are affected by artifacts in the recorded electrical activity; thereby it affects the analysis of EGG. To extract the clean data from EEG signals and to improve the efficiency of detection during encephalogram recordings, a developed model is required. Although various methods have been proposed for the artifacts removal process, sill the research on this process continues. Even if, several types of artifacts from both the subject and equipment interferences are highly contaminated the EEG signals, the most common and important type of interferences is known as Ocular artifacts. Many applications like Brain-Computer Interface (BCI) need online and real-time processing of EEG signals. Hence, it is best if the removal of artifacts is performed in an online fashion. The main intention of this proposal is to accomplish the new deep learning-based ocular artifacts detection and prevention model. In the detection phase, the 5-level Discrete Wavelet Transform (DWT), and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the Principle Component Analysis (PCA) and Independent Component Analysis (ICA) are adopted as the techniques for extracting the features. With the collected features, the development of optimized Deformable Convolutional Networks (DCN) is used for the detection of ocular artifacts from the input EEG signal. Here, the optimized DCN is developed by optimizing or tuning some significant parameters by Distance Sorted-Electric Fish Optimization (DS-EFO). If the artifacts are detected, the mitigation process is performed by applying the Empirical Mean Curve Decomposition (EMCD), and then, the optimized DCN is used for denoising the signals. Finally, the clean signal is generated by applying inverse EMCD. Based on the EEG data collected from diverse subjects, the proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular-artifact reduction by the proposed method.

Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme.

Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.

Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms

This paper addresses two issues toward practical use of wearable electroencephalogram (EEG) measurement devices. Ocular (eye movement and blink) artifacts often contaminate EEGs and deteriorate the performance of EEG-based brain–computer interfaces (BCIs). Although wearable consumer-grade EEG devices with single electrode allow users to operate BCIs conveniently in daily lives, it remains a challenging issue to attenuate ocular artifacts from single-channel measurements without spatial information. Existing ocular artifact reduction methods are, however, not simple enough for single-channel EEG data in the sense that they require an additional reference channel and/or pre-processing for artifact segment detection. Another issue is how to assess the performance of artifact reduction; the existing studies have used their own datasets that are not accessible from other researchers. Then, this paper makes two major contributions. (1) This paper proposes a novel ocular artifact reduction method, multi-scale dictionary learning (MSDL), which operates under single-channel measurements and without artifact segment detection. (2) We also develop a semi-simulation setting for quantitative evaluation with a publicly available EEG dataset. In particular, we employed BCI Competition IV Dataset 2a, on which the proposed method was compared with state-of-art methods. The proposed technique showed the best performance for recovering artifact-reduced waveforms from single-channel data compared to the other artifact reduction methods. The Matlab scripts for semi-simulation data generation and single-channel artifact reduction are available on GitHub.

Empirically Validating Fully Automated EOG Artifact Correction using Independent Components Analysis

Empirically Validating Fully Automated EOG Artifact Correction using Independent Components Analysis

High-throughput ocular artifact reduction in multichannel electroencephalography (EEG) using component subspace projection

After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion.

Fully Automated Reduction of Ocular Artifacts in High-Dimensional Neural Data

The reduction of artifacts in neural data is a key element in improving analysis of brain recordings and the development of effective brain-computer interfaces. This complex problem becomes even more difficult as the number of channels in the neural recording is increased. Here, new techniques based on wavelet thresholding and independent component analysis (ICA) are developed for use in high-dimensional neural data. The wavelet technique uses a discrete wavelet transform with a Haar basis function to localize artifacts in both time and frequency before removing them with thresholding. Wavelet decomposition level is automatically selected based on the smoothness of artifactual wavelet approximation coefficients. The ICA method separates the signal into independent components, detects artifactual components by measuring the offset between the mean and median of each component, and then removing the correct number of components based on the aforementioned offset and the power of the reconstructed signal. A quantitative method for evaluating these techniques is also presented. Through this evaluation, the novel adaptation of wavelet thresholding is shown to produce superior reduction of ocular artifacts when compared to regression, principal component analysis, and ICA.

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.

Automated ocular artifact removal: comparing regression and component-based methods

Abstract Objective: The aim is to compare various fully automated methods for reducing ocular artifacts from EEG recordings. Methods: Seven automated methods including regression, six component-based methods for reducing ocular artifacts have been applied to 36 data sets from two different labs. The influence of various noise sources is analyzed and the ratio between corrected and uncorrected EEG spectra, has been used to quantify the distortion. Results: The results show that not only regression but also component-based methods are vulnerable to over- or under-compensation and can cause significant distortion of EEG. Despite common belief, component-based methods did not demonstrate an advantage over the simple regression method. Conclusion: The newly proposed evaluation criterion showed to be an effective approach to evaluate 252 results from 36 data sets and 7 different methods. Significance: Currently, the regression method provides the most robust and stable results and is therefore the state-of-the-art-method for fully automated reduction of ocular artifacts.

A Clinical Electrophysiological Study of Emotional Lability in Patients With Systemic Lupus Erythematosus

A Clinical Electrophysiological Study of Emotional Lability in Patients With Systemic Lupus Erythematosus

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 40 dB 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.

Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients

Electroencephalogram (EEG) gives researchers a non-invasive way to record cerebral activity. It is a valuable tool that helps clinicians to diagnose various neurological disorders and brain diseases. Blinking or moving the eyes produces large electrical potential around the eyes known as electrooculogram. It is a non-cortical activity which spreads across the scalp and contaminates the EEG recordings. These contaminating potentials are called ocular artifacts (OAs). Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the possible experimental designs and may affect the cognitive processes under investigation. In this paper, a nonlinear time-scale adaptive denoising system based on a wavelet shrinkage scheme has been used for removing OAs from EEG. The time-scale adaptive algorithm is based on Stein's unbiased risk estimate (SURE) and a soft-like thresholding function which searches for optimal thresholds using a gradient based adaptive algorithm is used. Denoising EEG with the proposed algorithm yields better results in terms of ocular artifact reduction and retention of background EEG activity compared to non-adaptive thresholding methods and the JADE algorithm.

Should We Really Use Different Estimates for Correcting EEG Artefacts Produced by Blinks and by Saccades?

ArticlesShould We Really Use Different Estimates for Correcting EEG Artefacts Produced by Blinks and by Saccades?Rolf VerlegerRolf Verleger Lübeck Medical University, Germany Search for more papers by this authorPublished Online:September 01, 2006https://doi.org/10.1027//0269-8803.14.4.204PDFView Full Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreReferences Berg, P. (1986). The residual after correcting event-related potentials for blink artifacts. Psychophysiology, 23, 354– 364 . First citation in articleCrossref, Google Scholar Bour, L.J. , Aramideh, M. , Ongerboer de Visser, B.W. (2000). Neurophysiological aspects of eye and eyelid movements during blinking in humans. Journal of Neurophysiology, 83, 166– 176 . First citation in articleGoogle Scholar Colewijn, H. , van der Steen, J. , Steinman, R. (1985). Human eye movements associated with blinks and prolonged eye closure. Journal of Neurophysiology, 54, 11– 27 . First citation in articleGoogle Scholar Croft, R.J. , Barry, R.J. (2000). EOG correction of blinks with saccade coefficients: a test and revision of the aligned-artefact average solution. Clinical Neurophysiology, 111, 444– 451 . First citation in articleCrossref, Google Scholar Gratton, G. , Coles, M.G.H. , Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55, 468– 484 . First citation in articleCrossref, Google Scholar Picton, T.W. , van Roon, P. , Armilio, M.L. , Berg, P. , Ille, N. , Scherg, M. (2000). The correction of ocular artifacts: A topographic perspective. Clinical Neurophysiology, 111, 53– 65 . First citation in articleCrossref, Google Scholar Semlitsch, H. , Anderer, P. , Schuster, P. , Presslich, O. (1986). A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology, 23, 695– 703 . First citation in articleCrossref, Google Scholar Verleger, R. , Gasser, T. , Möcks, J. (1982). Correction of EOG artifacts in event related potentials of the EEG: Aspects of reliability and validity. Psychophysiology, 19, 472– 480 . First citation in articleCrossref, Google ScholarFiguresReferencesRelatedDetailsCited byEnsemble learning based classification for BCI applicationsDetection of Artefacts from the Motion of the Eyelids Created During EEG Research Using Artificial Neural Network4 February 2016Early and Recent Trends in PsychophysiologyMichael Falkenstein23 November 2011 | Journal of Psychophysiology, Vol. 25, No. 4Using an Eye Tracker for Accurate Eye Movement Artifact CorrectionIEEE Transactions on Biomedical Engineering, Vol. 54, No. 7A Model-Based Objective Evaluation of Eye Movement Correction in EEG RecordingsIEEE Transactions on Biomedical Engineering, Vol. 53, No. 2EOG correction: A comparison of four methodsPsychophysiology, Vol. 42, No. 1Principal component analysis for reduction of ocular artefacts in event-related potentials of normal and dyslexic childrenClinical Neurophysiology, Vol. 115, No. 3Correcting Ocular Artefact in Electrophysiological Data: New Approaches to Old Problems Geoff Barrett 1 September 2006 | Journal of Psychophysiology, Vol. 14, No. 4 Volume 14Issue 4October 2000ISSN: 0269-8803eISSN: 2151-2124 Accepted2 November 2000 InformationJournal of Psychophysiology (2000), 14, pp. 204-206 https://doi.org/10.1027//0269-8803.14.4.204.© 2000The Federation of European Psychophysiology SocietiesPDF download

Analysis of single trial movement-related brain macropotential

A parametric method of identification of movement-related brain macropotentials on a single trial basis through an ARX (autoregressive with exogenous inputs) algorithm is presented. The basic estimation of the information contained in the single trial is taken from an average carried out on a sufficient number of trials, while the noise sources, EEG and EOG are characterized as exogenous inputs in the model. The simulations as well as the experimental results confirm the capability of the model to drastically improve the signal/noise ratio in each single trial and to satisfactorily identify the contributions of signal and noise in the overall recording. This way, using the same algorithm, a particularly efficient reduction of ocular artifacts is also achieved. The movement-related brain macropotentials recorded in three subjects show a high degree of variability from trial to trial and this effect seems to be related to programming processes and evaluation of errors.

A parametric method of identification of single-trial event-related potentials in the brain.

A parametric method of identification of event-related (or evoked) potentials on a single-trial basis through an ARX (autoregressive with exogenous input) algorithm is discussed. The basic estimation of the information contained in the single trial is taken from an average carried out on a sufficient number of trials, while the noise sources, EEG and EOG, are characterized as exogenous inputs in the model. The simulations as well as the experimental results confirm the capability of the model of drastically improving the S/N (signal-to-noise) ratio in each single trial and satisfactorily identifying the contributions of signal and noise to the overall recording. A particularly efficient reduction of ocular artifacts is also achieved. >

A Solution for Reliable and Valid Reduction of Ocular Artifacts, Applied to the P300 ERP

ABSTRACTThe P300 ERP was measured in 10 subjects each for 9 days. The selection of instructions for subjects, the recording technique, the elimination of a few single trials significantly contaminated by eye movements, and the use of a correction procedure for ocular artifacts with calculable reliability and validity resulted in a set of data, in which 94% of the single trials were suitable for further analysis. The correction procedure relies on regression analysis. To reduce coherence between eyeblink activity and ongoing EEG, VEOG and EEG are averaged on eyeblinks. This yields a high reliability and validity of regression factors, determined per day, subject, and lead. In addition, this correction procedure allows for an estimation of the maximal error that must be taken into account. The efficiency of the procedure is demonstrated for single trials and averaged potentials.