Ocular Artifacts Research Articles

Artifact removal using deep WVFLN for brain signal diagnosis through IoMT

Electroencephalography(EEG), helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond range. In the case of e-health cares, the internet is the medium for remote patients.To extract clean clinical information from EEG signals, it is essential to remove unwanted signals, called as artifacts, that are due to different causes including at the time of acquisition. In this piece of work, the authors considered the EEG signal contaminated with ocular artifacts. The clean EEG as well as artifactual EEG are taken from the openly available Mendeley database for verifying the performance of the proposed algorithm.Being the artifactual signal is non-linear and non-stationary the Deep Wavelet Vector Functional Link Network(WVFLN) model is used in this case. The Machine Learning approach has taken a leading role in every field of current research and WVFLN is one of them. For the proof of adaptive nature, the model is designed with EEG as a reference and artifactual EEG as input.To vary the weight and reduce the error, an exponentially weighted Recursive Least Square(RLS) algorithm is used to update the output layer weight of the novel WVFLN model. The Wavelet coefficients are considered in this model with a radial basis function to satisfy the required signal experimentation. It is found that the result is excellent in terms of Mean Square Error(MSE), Root Mean Square Error(RMSE).The MSE and RMSE are found as 0.18μV2 and 0.4242 μV respectively.Also, the proposed method is compared with the earlier methods to show its efficacy.

The Dominance of Anticipatory Prefrontal Activity in Uncued Sensory-Motor Tasks.

Anticipatory event-related potentials (ERPs) precede upcoming events such as stimuli or actions. These ERPs are usually obtained in cued sensory–motor tasks employing a warning stimulus that precedes a probe stimulus as in the contingent negative variation (CNV) paradigms. The CNV wave has been widely studied, from clinical to brain–computer interface (BCI) applications, and has been shown to emerge in medial frontoparietal areas, localized in the cingulate and supplementary motor areas. Several dated studies also suggest the existence of a prefrontal CNV, although this component was not confirmed by later studies due to the contamination of ocular artifacts. Another lesser-known anticipatory ERP is the prefrontal negativity (pN) that precedes the uncued probe stimuli in discriminative response tasks and has been localized in the inferior frontal gyrus. This study aimed to characterize the pN by comparing it with the CNV in cued and uncued tasks and test if the pN could be associated with event preparation, temporal preparation, or both. To achieve these aims, high-density electroencephalographic recording and advanced ERP analysis controlling for ocular activity were obtained in 25 volunteers who performed 4 different visuomotor tasks. Our results showed that the pN amplitude was largest in the condition requiring both time and event preparation, medium in the condition requiring event preparation only, and smallest in the condition requiring temporal preparation only. We concluded that the prefrontal CNV could be associated with the pN, and this activity emerges in complex tasks requiring the anticipation of both the category and timing of the upcoming stimulus. The proposed method can be useful in BCI studies investigating the endogenous neural signatures triggered by different sensorimotor paradigms.

The importance of ocular artifact removal in single-trial ERP analysis: The case of the N250 in face learning

ObjectiveSingle-trial event-related potentials (ERPs) offer fine-grained information about the trajectories of the neurocognitive processes but are highly sensitive to any artifacts in the EEG signal. The primary aim of this study was to assess the impact of ocular artifact removal on the single-trial N250 ERP analysis of face learning in individual participants. MethodsWe present a detailed description of our research-grade EEG hardware setup and a highly reproducible code (https://osf.io/aqhmn/) for generating time series of single-trial N250 ERP amplitudes and precise identification of a changepoint between face memory trace acquisition and maintenance. Ocular artifacts were removed using a new semi-automatic approach with only one hyperparameter based on the correlation between EEG components from independent component analysis (ICA) and the EOG signal. ResultsResults from the simulation study showed that our ocular artifact filtration decreased the average RMSE by half and achieved the highest increase of SNR among all the compared methods. It decreased standard deviations and improved the fit of the broken-line regression models for all participants by 25% ± 17% (min. 2%, max. 63%). Conclusions and significanceOcular artifact filtration had a substantial positive impact on the regression modeling of single-trial ERP amplitudes. Lack of ocular artifact removal can drastically distort the conclusions about the face learning process from single-trial N250 ERP experiments for individual participants. The changepoint locations changed for 13 out of 15 participants. This is the first published analysis of time series of single-trial N250 ERP amplitudes in face learning.

Automated CCA-MWF Algorithm for Unsupervised Identification and Removal of EOG Artifacts From EEG.

Affective brain computer interface (ABCI) enables machines to perceive, understand, express and respond to people's emotions. Therefore, it is expected to play an important role in emotional care and mental disorder detection. EEG signals are most frequently adopted as the physiology measurement in ABCI applications. Eye blinking and movements introduce lots of artifacts into raw EEG data, which seriously affect the quality of EEG signal and the subsequent emotional EEG feature engineering and recognition. In this paper, we propose a fully automatic and unsupervised ocular artifact identification and removal algorithm named automated canonical correlation analysis (CCA)-multi-channel wiener filter (MWF) (ACCAMWF). Firstly, spatial distribution entropy (SDE) and spectral entropy (SE) are computed to automatically annotate artifact segments. Then, CCA algorithm is used to extract neural signal from artifact contaminated data to further supplement the clean EEG data. Finally, MWF is trained to remove ocular artifacts from multiple channel EEG data adaptively. Extensive experiments have been carried out on semi-simulated EEG/EOG dataset and real eye blinking-contaminated EEG dataset to verify the effectiveness of our method when compared to two state-of-the-art algorithms. The results clearly demonstrate that ACCAMWF is a promising solution for removing EOG artifacts from emotional EEG data.

Drowsiness Detection Using Ocular Indices from EEG Signal.

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.

Ocular Artifacts realization through optimized scheme

Ocular Artifacts realization through optimized scheme

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.

L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery

Objective . Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. Approach . In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. Main results . A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved. Significance . The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.

Human state anxiety classification framework using EEG signals in response to exposure therapy.

Human anxiety is a grave mental health concern that needs to be addressed in the appropriate manner in order to develop a healthy society. In this study, an objective human anxiety assessment framework is developed by using physiological signals of electroencephalography (EEG) and recorded in response to exposure therapy. The EEG signals of twenty-three subjects from an existing database called “A Database for Anxious States which is based on a Psychological Stimulation (DASPS)” are used for anxiety quantification into two and four levels. The EEG signals are pre-processed using appropriate noise filtering techniques to remove unwanted ocular and muscular artifacts. Channel selection is performed to select the significantly different electrodes using statistical analysis techniques for binary and four-level classification of human anxiety, respectively. Features are extracted from the data of selected EEG channels in the frequency domain. Frequency band selection is applied to select the appropriate combination of EEG frequency bands, which in this study are theta and beta bands. Feature selection is applied to the features of the selected EEG frequency bands. Finally, the selected subset of features from the appropriate frequency bands of the statistically significant EEG channels were classified using multiple machine learning algorithms. An accuracy of 94.90% and 92.74% is attained for two and four-level anxiety classification using a random forest classifier with 9 and 10 features, respectively. The proposed state anxiety classification framework outperforms the existing anxiety detection framework in terms of accuracy with a smaller number of features which reduces the computational complexity of the algorithm.

Automated Ocular Artifacts Removal Framework Based on Adaptive Chirp Mode Decomposition

The removal of ocular artifacts (OAs) from electroencephalogram (EEG) signals is crucial for effective and accurate analysis of the signals in different neurological and brain computer interface (BCI) applications. Towards this aspect, various multi-channel and single-channel techniques have been proposed for the removal/suppression of OAs from EEG signals. Recently, due to the rising demand of resource-limited ambulatory systems, single-channel techniques are more preferred for OAs removal. Although the existing techniques effectively remove OAs, they introduce distortion in the denoised EEG signal and the corresponding neurological features. Therefore, in this paper, we propose a framework for automated detection and removal of OAs from single-channel EEG signal based on discrete Fourier transform (DFT) and adaptive chirp mode decomposition (ACMD). The proposed framework is built in four stages: application of DFT to single-channel EEG signal; rejection of low-frequency baseline wander components; processed EEG signal decomposition using ACMD; rejection of mode containing OAs based on proposed peak point count threshold criteria. The performance of the proposed framework is assessed using noise-free EEG and EEG corrupted with OAs signals with different shapes and frequencies taken from three publicly available databases and in terms of different performance metrics. Further, comparative performance analysis demonstrates that the proposed framework outperforms the existing OAs removal techniques based on wavelet thresholding, variational mode decomposition (VMD), and Savitzky-Golay filter (SG-filter).

Removal of ocular artifacts from electroencephalo-graph by improving variational mode decomposition

Ocular artifacts in Electroencephalography (EEG) recordings lead to inaccurate results in signal analysis and process. Variational Mode Decomposition (VMD) is an adaptive and completely non-recursive signal processing method. There are two parameters in VMD that have a great influence on the result of signal decomposition. Thus, this paper studies a signal decomposition by improving VMD based on squirrel search algorithm (SSA). It's improved with abilities of global optimal guidance and opposition based learning. The original seasonal monitoring condition in SSA is modified. The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions. Opposition-based learning is introduced to reposition the position of the population in this stage. It is applied to optimize the important parameters of VMD. GOSSA-VMD model is established to remove ocular artifacts from EEG recording. We have verified the effectiveness of our proposal in a public dataset compared with other methods. The proposed method improves the SNR of the dataset from −2.03 to 2.30.

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.

Visual Stimuli-Based Dynamic Commands With Intelligent Control for Reactive BCI Applications

In this study, inconspicuous visual stimuli with hidden targets are considered as a new paradigm for reactive brain-computer interface (BCI) applications suitable for actuating a motorized system with a dynamic command from user. To drive the motor’s control signal level as close as possible to the targeted dynamic command via wireless transmission, an embeddable intelligent control scheme is introduced to improve the overall electroencephalography (EEG)-based decoding strategy of the BCI system. The proposed technique which can induce motivated attention only requires a single EEG channel, and the intelligent control scheme is constructed with a decoder consisting of a multilayer perceptron (MLP) neural network and a recursive digital Boxcar filter to suppress the influence of noise and ocular artifacts that are typically caused by user’s involuntary movements. Results from thirty subjects showed that the predictive ability of the BCI system was significantly improved via the proposed decoder compared to the performance of MLP models alone and those with low pass and Kalman filters which were two existing methods commonly used to alleviate the aforementioned perturbations in real-time applications. The BCI’s predictive ability could also be further enhanced by selecting a suitable stimulus to construct a generic MLP model for each gender due to notable performance disparities between male and female groups. The findings of this study will have the potential to increase the degree of freedom in reactive BCI applications particularly when embedded control systems with multiple actuator speeds or accelerations are desired such as those used to control mobile robotics.

Denoising EEG Signals for Real-World BCI Applications Using GANs.

As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals.

One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram

Cognitive/mental task classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in designing portable brain-computer interface (BCI) and neurofeedback (NFB) systems. However, real-time recorded EEG signals are often contaminated with ocular artifacts (OAs) and muscle artifacts (MAs), which deteriorate the hand-crafted features extracted from EEG signal, resulting inadequate classification of cognitive tasks. Therefore, we investigate the use of deep learning techniques which do not require manual feature extraction or artifact suppression. In this paper, we propose a shallow one-dimensional convolutional neural network (1D-CNN) architecture for cognitive task classification. The robustness of the proposed architecture is evaluated using artifact-free and artifact-contaminated EEG signals taken from two publicly available databases (i.e, Keirn and Aunon (K) database and EEGMAT (E) database) and in-house (R) database recorded using single-channel device in performing not only cognitive/non-cognitive binary task classification but also cognitive/cognitive multi-tasks classification. Evaluation results demonstrate that the proposed architecture achieves the highest subject-independent classification accuracy of 99.70% and 100.00% for multi-class classification and pair-wise classification respectively in database K. Further, subject-independent classification accuracies of 99.00% and 98.00% are achieved in databases E and R respectively. Comparative performance analysis demonstrates that the proposed architecture outperforms existing approaches not only in terms of classification accuracy but also in robustness against artifacts.

Adaptive Single-Channel EEG Artifact Removal With Applications to Clinical Monitoring.

Electroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision. We propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a data-driven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques. We assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia. We show that our method can successfully attenuate various types of artifacts in single-channel EEG. Thanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.

The Importance of Ocular Artifact Removal in Single-Trial ERP Analysis: The Case of the N250 in Face Learning

The Importance of Ocular Artifact Removal in Single-Trial ERP Analysis: The Case of the N250 in Face Learning

Real-Time Cognitive Load Measurement in Classroom Environment using a Dry-Electrode EEG system

Cognitive load (CL) theory suggests that instructional materials need to be designed for reducing unnecessary CL and has been regarded as one of the most influential theories in science education. How to measure individual CL is still under investigation. In this study, we developed an eight-channel dry-electrode electroencephalogram (EEG) system and proposed an algorithm to real-time measure the depth of working memory of the N-back task in a classroom environment. The ocular artifact was removed by using the recursive least-square (RLS) method. Time-frequency analysis was applied to extract event-related theta- band activities in the artifact-suppressed EEG signals. Eight participants had the active duration for theta-band activities as 1.44±0.36 mv, 1.70±0.22 mv, and 1.97±0.04 mv for 0-back, 1-back, and 2-back tasks, respectively. In contrast to the previous research that has used spectral power of particular frequency bands as signal features, we found the detection of active duration provides better discrimination power in classifying different CL levels, compared to that of the classification using features of spectral power. The result in this study demonstrates the feasibility of theta-band EEG as an indicator to measure students’ cognitive load in a classroom environment.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

Objective. Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which—depending on their extent—may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts. Approach. In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches. Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts. Significance. Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.

The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines

In studies of event-related brain potentials (ERPs), numerous decisions about data processing are required to extract ERP scores from continuous data. Unfortunately, the systematic impact of these choices on the data quality and psychometric reliability of ERP scores or even ERP scores themselves is virtually unknown, which is a barrier to the standardization of ERPs. The aim of the present study was to optimize processing pipelines for the error-related negativity (ERN) and error positivity (Pe) by considering a multiverse of data processing choices. A multiverse analysis of a data processing pipeline examines the impact of a large set of different reasonable choices to determine the robustness of effects, such as the impact of different decisions on between-trial standard deviations (i.e., data quality) and between-condition differences (i.e., experimental effects). ERN and Pe data from 298 healthy young adults were used to determine the impact of different methodological choices on data quality and experimental effects (correct vs. error trials) at several key stages: highpass filtering, lowpass filtering, ocular artifact correction, reference, baseline adjustment, scoring sensors, and measurement procedure. This multiverse analysis yielded 3,456 ERN scores and 576 Pe scores per person. An optimized pipeline for ERN included a 15 Hz lowpass filter, ICA-based ocular artifact correction, and a region of interest (ROI) approach to scoring. For Pe, the optimized pipeline included a 0.10 Hz highpass filter, 30 Hz lowpass filter, regression-based ocular artifact correction, a -200 to 0 ms baseline adjustment window, and an ROI approach to scoring. The multiverse approach can be used to optimize pipelines for eventual standardization, which would support efforts toward establishing normative ERP databases. The proposed process of analyzing the data-processing multiverse of ERP scores paves the way for better refinement, identification, and selection of data processing parameters, ultimately improving the precision and utility of ERPs.