Ocular artifact elimination from electroencephalography signals: A systematic review

Ranjan, Rakesh; Sahana, Bikash Chandra; Bhandari, Ashish Kumar

doi:10.1016/j.bbe.2021.06.007

Cited by 48 publications

(16 citation statements)

References 248 publications

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“…Because each EEG electrode records a mixture of neural activity and artifacts with an unknown 'ground-truth', it is difficult to precisely disentangle the neural activity from artifacts when analysing EEG signals. To address this issue, a multitude of methods for pre-processing EEG data have been developed (see Islam et al (2016) and Ranjan et al (2021) for reviews, and Barban et al (2021) and Robbins et al (2020) for comparisons across a range of cleaning methods). The fundamental aim of artifact cleaning is to reduce the influence of artifacts on the EEG data while leaving signals from neural activity unaltered.…”

Section: Introductionmentioning

confidence: 99%

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

Bailey

Biabani

Hill

et al. 2022

Preprint

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Electroencephalographic (EEG) data is typically contaminated with non-neural artifacts which can confound the results of experiments. Artifact cleaning approaches are available, but often require time-consuming manual input and significant expertise. Advancements in artifact cleaning often only address a single artifact, are only compared against a small selection of pre-existing methods, and seldom assess whether a proposed advancement improves experimental outcomes. To address these issues, we developed RELAX (the Reduction of Electroencephalographic Artifacts), an automated EEG cleaning pipeline implemented within EEGLAB that reduces all artifact types. RELAX cleans continuous data using Multiple Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were tested using three datasets containing a mix of cognitive and resting recordings (N = 213, 60 and 23 respectively). RELAX was compared against six commonly used EEG cleaning approaches across a wide range of artifact cleaning quality metrics, including signal-to-error and artifact-to-residue ratios, measures of remaining blink and muscle activity, and the amount of variance explained by experimental manipulations after cleaning. RELAX with MWF and wICA_ICLabel showed amongst the best performance for cleaning blink and muscle artifacts while still preserving neural signal. RELAX with wICA_ICLabel (and no MWF) may perform better at detecting the effect of experimental manipulations on alpha oscillations in working memory tasks. The pipeline is easy to implement in MATLAB and freely available on GitHub. Given its high cleaning performance, objectivity, and ease of use, we recommend RELAX for data cleaning across EEG studies.

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Section: Introductionmentioning

confidence: 99%

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

Bailey

Biabani

Hill

et al. 2022

Preprint

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“…Regression is still a popular and commonly used method of artifact removal (Ranjan et al, 2021). The method requires a reference channel, which was chosen by us as Fp1 (the electrode closest to the eye).…”

Section: Linear Regressionmentioning

confidence: 99%

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

2022

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Electroencephalography (EEG) signals are disrupted by technical and physiological artifacts. One of the most common artifacts is the natural activity that results from the movement of the eyes and the blinking of the subject. Eye blink artifacts (EB) spread across the entire head surface and make EEG signal analysis difficult. Methods for the elimination of electrooculography (EOG) artifacts, such as independent component analysis (ICA) and regression, are known. The aim of this article was to implement the convolutional neural network (CNN) to eliminate eye blink artifacts. To train the CNN, a method for augmenting EEG signals was proposed. The results obtained from the CNN were compared with the results of the ICA and regression methods for the generated and real EEG signals. The results obtained indicate a much better performance of the CNN in the task of removing eye-blink artifacts, in particular for the electrodes located in the central part of the head.

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“…To address the issue of artifacts in EEG data, many EEG cleaning methods have been developed (see Islam et al (2016) and Ranjan et al (2021) for reviews, and Barban et al (2021) and Robbins et al (2020) for comparisons across multiple cleaning methods). Although these existing EEG pre-processing methods have been demonstrated to meet their aim of reducing artifacts while preserving neural activity, there are still several outstanding issues.…”

Section: Introductionmentioning

confidence: 99%

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 2: Application to Event-Related Potentials

Bailey

Hill

Biabani

et al. 2022

Preprint

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Electroencephalography (EEG) is commonly used to examine neural activity time-locked to the presentation of a stimulus, referred to as an Event-Related Potential (ERP). However, EEG is also influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning can reduce artifacts, but often requires time-consuming manual decisions. Most automated cleaning methods require frequencies <1Hz to be filtered out of the data, so are not recommended for ERPs (which often contain <1Hz frequencies). In our companion article, we introduced RELAX (the Reduction of Electroencephalographic Artifacts), an automated and modular cleaning pipeline that reduces artifacts with Multiple Wiener Filtering (MWF) and/or wavelet enhanced independent component analysis (wICA) applied to artifact components detected with ICLabel (wICA_ICLabel) (Bailey et al., 2022). To evaluate the suitability of RELAX for data cleaning prior to ERP analysis, multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines. Cleaning performance was compared across a range of artifact cleaning metrics and in the amount of variance in ERPs explained by different conditions in a Go-Nogo task. RELAX with MWF and wICA_ICLabel cleaned the data the most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect experimental effects better for some ERP measures. Importantly, RELAX can high-pass filter data at 0.25Hz, so is applicable to analyses involving ERPs. The pipeline is easy to implement via EEGLAB in MATLAB and is freely available on GitHub. Given its performance, objectivity, and ease of use, we recommend RELAX for EEG data cleaning.

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Ocular artifact elimination from electroencephalography signals: A systematic review

Cited by 48 publications

References 248 publications

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 2: Application to Event-Related Potentials

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