Averaged Event-related Potentials Research Articles

Combining EEG and eye-tracking in virtual reality: Obtaining fixation-onset event-related potentials and event-related spectral perturbations.

Extensive research conducted in controlled laboratory settings has prompted an inquiry into how results can be generalized to real-world situations influenced by the subjects' actions. Virtual reality lends itself ideally to investigating complex situations but requires accurate classification of eye movements, especially when combining it with time-sensitive data such as EEG. We recorded eye-tracking data in virtual reality and classified it into gazes and saccades using a velocity-based classification algorithm, and we cut the continuous data into smaller segments to deal with varying noise levels, as introduced in the REMoDNav algorithm. Furthermore, we corrected for participants' translational movement in virtual reality. Various measures, including visual inspection, event durations, and the velocity and dispersion distributions before and after gaze onset, indicate that we can accurately classify the continuous, free-exploration data. Combining the classified eye-tracking with the EEG data, we generated fixation-onset event-related potentials (ERPs) and event-related spectral perturbations (ERSPs), providing further evidence for the quality of the eye-movement classification and timing of the onset of events. Finally, investigating the correlation between single trials and the average ERP and ERSP identified that fixation-onset ERSPs are less time sensitive, require fewer repetitions of the same behavior, and are potentially better suited to study EEG signatures in naturalistic settings. We modified, designed, and tested an algorithm that allows the combination of EEG and eye-tracking data recorded in virtual reality.

The assessment of the impact of antiepileptic drugs on cognitive functions via N-200/P-300 potentials and neuropsychological measures.

The effects of antiseizure medications (ASMs) on cognitive functions have not been fully elucidated. The primary aim of this study was to demonstrate potential changes in cognitive functions in patients diagnosed with epilepsy from both neuropsychological and electrophysiological perspectives. Our secondary objective was to assess the effects of administered ASM on cognitive functions by categorizing patients into different monotherapy and polytherapy groups. A single-center, prospective patient registry study was conducted between May 2022 and June 2023. The inclusion criteria included epilepsy patients aged 18 to 50 years who were receiving ASM) treatment, either as inpatients or outpatients, and who did not have any syndromic diagnosis that may lead to cognitive disfunciton (such as primary progressive myoclonic epilepsies, Down syndrome and so on), and did not diagnosed previously or during examination that could affect dementia or cognitive functions. Patients who were scheduled to initiate new ASM treatment were evaluated using the Montreal Cognitive Assessment (MoCA) scale and Event-Related Potentials (ERP) assessment both before commencing treatment and three months thereafter. A total of 320 participants were included in the study; 20 healthy controls and 300 epilepsy patients were included. Statistically significant differences were observed between the healthy control group and the epilepsy group in terms of average Montreal Cognitive Assessment (MoCA) scores and event-related potentials (ERPs) (n200, p300 latencies, n2p3 amplitudes) (p<0.05). Similarly, statistically significant differences were observed between the monotherapy and polytherapy groups in terms of average MoCA and ERP scores (p<0.05). This study demonstrated the detrimental effects of certain ASMs, particularly topiramate and carbamazepine, on cognitive functions. Furthermore, the negative impact on cognitive performance became more pronounced with an increasing number of concurrently used ASMs (polytherapy), with topiramate showing notable effects.

Electrophysiological correlates of symbolic numerical order processing.

Determining if a sequence of numbers is ordered or not is one of the fundamental aspects of numerical processing linked to concurrent and future arithmetic skills. While some studies have explored the neural underpinnings of order processing using functional magnetic resonance imaging, our understanding of electrophysiological correlates is comparatively limited. To address this gap, we used a three-item symbolic numerical order verification task (with Arabic numerals from 1 to 9) to study event-related potentials (ERPs) in 73 adult participants in an exploratory approach. We presented three-item sequences and manipulated their order (ordered vs. unordered) as well as their inter-item numerical distance (one vs. two). Participants had to determine if a presented sequence was ordered or not. They also completed a speeded arithmetic fluency test, which measured their arithmetic skills. Our results revealed a significant mean amplitude difference in the grand average ERP waveform between ordered and unordered sequences in a time window of 500-750 ms at left anterior-frontal, left parietal, and central electrodes. We also identified distance-related amplitude differences for both ordered and unordered sequences. While unordered sequences showed an effect in the time window of 500-750 ms at electrode clusters around anterior-frontal and right-frontal regions, ordered sequences differed in an earlier time window (190-275 ms) in frontal and right parieto-occipital regions. Only the mean amplitude difference between ordered and unordered sequences showed an association with arithmetic fluency at the left anterior-frontal electrode. While the earlier time window for ordered sequences is consistent with a more automated and efficient processing of ordered sequential items, distance-related differences in unordered sequences occur later in time.

Optimal filters for ERP research I: A general approach for selecting filter settings.

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Enhanced average for event-related potential analysis using dynamic time warping

Electroencephalography (EEG) provides a way to understand, and evaluate neurotransmission. In this context, time-locked EEG activity or event-related potentials (ERPs) are often used to capture neural activity related to specific mental processes. Normally, they are considered on the basis of averages across a number of trials. However, there exist notable variability in latency jitter, jitter, and amplitude, across trials, and, also, across users; this causes the average ERP waveform to blur, and, furthermore, diminish the amplitude of underlying waves.For these reasons, a strategy is proposed for obtaining ERP waveforms based on dynamic time warping (DTW) to adapt, and adjust individual trials to the averaged ERP, previously calculated, to build an enhanced average by making use of these warped signals. At the sight of the experiments carried out on the behaviour of the proposed scheme using publicly available datasets, this strategy reduces the attenuation in amplitude of ERP components thanks to the reduction of the influence of variability of latency and jitter, and, thus, improves the averaged ERP waveforms.

Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics.

Characterizing the brain's dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain's response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time-frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

Cognitive control subprocess deficits and compensatory modulation mechanisms in patients with frontal lobe injury revealed by EEG markers: a basic study to guide brain stimulation

BackgroundFrontal lobe injury (FLI) is related to cognitive control impairments, but the influences of FLI on the internal subprocesses of cognitive control remain unclear.AimsWe sought to identify specific biomarkers for...

Binaural Beat Effects on Attention: A Study Based on the Oddball Paradigm.

The impact of binaural beats (BBs) on human cognition and behavior remains and various methods have been used to measure their effect, including neurophysiological, psychometric, and human performance evaluations. The few approaches where the level of neural synchronicity and connectivity were measured by neuroimaging techniques have only been undertaken in spontaneous mode. The present research proposes an approach based on the oddball paradigm to study BB effect by estimating the level of attention induced by BBs. Evoked activity of 25 young adults between 19 and 24years old with no hearing impairments nor clinical neurological history were analyzed. The experiment was conducted in two different sessions of 24.5min. The first part consisted of 20-min BB stimulation in either theta (BBθ) or beta (BBβ). After the BB stimulation, an oddball paradigm was applied in each BB condition to assess the attentional effect induced by BBs. Attention enhancement is expected for BBβ with respect to BBθ. Target event related potentials (ERPs) were mainly analyzed in the time and time-frequency domains. The frequency analysis was based on continuous wavelet transform (CWT), event-related spectral perturbation (ERSP), and inter-trial phase coherence (ITPC). The study revealed that the P300 component was not significantly different between conditions (BBθ vs. BBβ). However, the target grand average ERP in BBθ condition was mainly composed of 8Hz-frequency components, appearing before 400ms post-stimulus, and mainly on the centro-parietal regions. In contrast, the target grand average ERP in BBβ condition was mainly composed of frequency components below 6Hz, mainly appearing at 400ms post-stimulus on the parieto-occipital regions. Furthermore, ERPs in the BBθ condition were more phase locked than the BBβ condition.

Ensemble deep clustering analysis for time window determination of event-related potentials

ObjectiveCluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. MethodsWe designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time window. ResultsAfter applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20 dB to −5 dB white Gaussian noise) was added to the prepared data. ConclusionCompared to the state-of-the-art clustering methods, a superior clustering performance was yielded from both ERP data. Furthermore, more stable and precise time windows were elicited as the noise increased. SignificanceOur study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect after preprocessing.

Anticipatory reward dysfunction in alcohol dependence: An electroencephalography monetary incentive delay task study

A wealth of functional magnetic resonance imaging monetary incentive delay task (MIDT) research has shown alcohol dependency is associated with a hypoactive striatal response during gain anticipation (gain > neutral) and loss anticipation (loss > neutral). Electroencephalography (EEG) holds clinical advantages over fMRI (high temporal resolution, low cost, portable) however its use to study reward processing in alcohol dependence is limited. We aimed to carry out the first EEG MIDT (eMIDT) study in alcohol dependence. 21 abstinent alcohol dependent individuals and 26 controls performed an MIDT while neural activity was recorded using 64-channel EEG. Trial averaged event-related potentials (ERPs) and single-trial machine learning discriminant analyses were applied to EEG data. Clinical variables related to severity of dependence were collected and relationships with ERP data explored. Alcohol dependent individuals, compared with healthy controls, had blunted cue-P3 amplitudes for gain and loss anticipation (interaction: p = 0.019); and elevated contingent negative variation amplitudes for all conditions (gain, loss, neutral)(main effect: p < 0.001) which was associated with increased alcohol consumption (p = 0.002). The machine learning analyses demonstrated alcohol dependent individuals had reduced ability to discriminate between loss and neutral cues between 328 – 350 ms (p = 0.040), 354 – 367 ms (p = 0.047) and 525 – 572 ms (p = 0.022). The eMIDT approach is demonstrated to be a low-cost, sensitive measure of dysfunctional anticipatory reward processing in alcohol dependence, which we propose is ideal for big data approaches to prognostic psychiatry and translation into clinical practice.

Assessment of a single trial impact on the amplitude of the averaged event related potentials.

Widely used in neuroscience the averaging of event related potentials is based on the assumption that small responses to the investigated events are present in every trial but can be hidden under the random noise. This situation often takes place, especially in experiments performed at hierarchically lower levels of sensory systems. However, in the studies of higher order complex neuronal networks evoked responses might appear only under particular conditions and be absent otherwise. We encountered this problem studying a propagation of interoceptive information to the cortical areas in the sleep-wake cycle. Cortical responses to various visceral events were present during some periods of sleep, then disappeared for a while and restored again after a period of absence. Further investigation of the viscero-cortical communication required a method that would allow labeling the trials contributing to the averaged event related responses-"efficient trials," and separating them from the trials without any response. Here we describe a heuristic approach to solving this problem in the context of viscero-cortical interactions occurring during sleep. However, we think that the proposed technique can be applicable to any situation where neuronal processing of the same events is expected to be variable due to internal or external factors modulating neuronal activity. The method was first implemented as a script for Spike 2 program version 6.16 (CED). However, at present a functionally equivalent version of this algorithm is also available as Matlab code at https://github.com/george-fedorov/erp-correlations.

Источники и значимость вариативности потенциалов мозга человека в интерфейсе мозг–компьютер

In the brain-computer interface based on the P300 wave (P300 BCI), the selection of the command by the user becomes possible due to focusing the user's attention on the external stimulus/command and extraction of the response to this stimulus in the form of the event-related potential (ERP) components from EEG. To obtain the ERP signal, stimuli should be repeated many times, however, in view of the existing variability in latency of the response to certain stimuli, the averaged ERPs may give a distorted view of the nature of such responses and reduce accuracy of the interface. The study was aimed to develop an effective method for identification of the effects of the ERP components' latency variability and for accounting these effects in the P300 BCI, as well as to identify the possible impact of psychophysiological factors on the nature of ERP variability. We have conducted a BCI-based study of 19 healthy subjects involving extraction and adjustment of latency in the N1 and P300 spatial components, which play a key role in the command classification in the P300 BCI, to explore the mechanisms underlying variability. Such an approach ensured higher accuracy compared to the use of conventional EEG leads, and the highest increase of 10% was observed when using the minimum number of the stimulus repetitions. Furthermore, modifications of the interface allowing one to ensure a higher level of the user's focus on the task and a more accurate visual fixation on the target objects contributed to the increase in the amplitude of the ERP components by reducing variability of the responses to single stimuli. The findings emphasize the important role of the processes underlying the ERP components' variability and provide an effective tool for scientific exploration of such processes and the development of advanced BCI systems.

The implicit preference evaluation for the ceramic tiles with different visual features: Evidence from an event-related potential study.

Ceramic tiles are popular because of their various forms, and they are often used to decorate the environment. However, few studies have applied objective methods to explore the implicit preference and visual attention of people toward ceramic tile features. Using event-related potential technology can provide neurophysiological evidence for the study and applications of tiles. This study explored the influence of pattern, lightness, and color system factors of ceramic tiles on the preferences of people using a combination of subjective questionnaires and event-related potential (ERP) technology. Twelve different conditions of tiles (2 × 3 × 2) were used as stimuli. EEG data were collected from 20 participants while they watched the stimuli. Subjective preference scores and average ERPs were analyzed using analysis of variance and correlation analysis. (1) Pattern, lightness, and color system factors significantly affected the subjective preference scores for tiles; the unpatterned tiles, light-toned tiles, and warm-colored tiles received higher preference scores. (2) The preferences of people for different features of tiles moderated ERP amplitudes. (3) The light-toned tiles with a high preference score caused a greater N100 amplitude than the medium-toned and dark-toned tiles; and the patterned tiles and warm-colored tiles with low preference scores induced greater P200 and N200 amplitudes. In the early stage of visual processing, light-toned tiles attracted more attention, possibly because of the positive emotional effects related to the preference. The greater P200 and N200 elicited by the patterned and neutral-colored tiles in the middle stage of visual processing indicates that patterned and neutral-colored tiles attracted more attention. This may be due to negativity bias, where more attention is allocated to negative stimuli that people strongly dislike. From the perspective of cognitive processes, the results indicate that the lightness of ceramic tiles is the factor that people first detect, and the visual processing of pattern and color system factors of ceramic tiles belong to a higher level of visual processing. This study provides a new perspective and relevant information for assessing the visual characteristics of tiles for environmental designers and marketers involved in the ceramic tiles industry.

ДИНАМІКА ЕЕГ ПІД ЧАС СПРИЙНЯТТЯ ЕМОЦІЙНИХ ВИРАЗІВ ОБЛИЧ ЗА РІЗНОГО ЧАСУ ДЕМОНСТРАЦІЇ: ДОСЛІДЖЕННЯ ПОТЕНЦІАЛІВ, ПОВ'ЯЗАНИХ ІЗ ПОДІЯМИ

The face is one of the most significant elements of non-verbal communication, which allows humans to quickly get various types of information about a specific person. Therefore, understanding facial expressions is crucial for normal social interaction, as it lets us determine the emotional states and intentions of other people. The purpose of this study was to investigate the influence of the time factor on the happy and fearful facial expression processing, reflected in the structure and components of event-related potentials (ERPs). Accordingly, two parallel examinations were conducted with stimulus presentation periods of 500 ms and 100 ms. For the obtained data, two separate analyses (for happy and fearful faces) of the averaged ERP curves corresponding to different stimulus exposure periods were executed. The initial stages of processing (EPN, P3) demonstrated that the difference in the structure and amplitude characteristics of the ERP curve was not that significant, but it demonstrated an increase in attentional resources involvement and a strengthening of the primary emotional analysis. Later ERP components (N400, LPP) showed a more compelling difference, that reflected the complexity of semantic decoding, subsequent conscious evaluation, and processes related to decision-making. Thus, reducing the time of image display from 500 ms to 100 ms created the expected difficulties for the perception and processing of emotional facial expressions and was not sufficient for the normal course of these processes.

N170 component analysis of single-trial EEG based on electrophysiological source imaging.

Event-related potentials (ERP) are brain-evoked potentials that reflect the neural activity of the brain. However, it is difficult to isolate the ERP components of our interest because single-trial EEG is disturbed by other signals, and the average ERP analysis in turn loses single-trial information. In this paper, we used electrophysiological source imaging (ESI) to analyze the N170 component of single-trial EEG triggered by face stimulation. The results show that ESI is feasible for the analysis of N170 and that there are left-right differences in the area of the fusiform gyrus associated with face stimulation in the brain. Clinical Relevance- Analysis of the N170 of single-trial EEG by ESI may help in the diagnosis of patients with prosopagnosia and may also help physicians clinically in determining whether the fusiform gyrus region is damaged.

Functional integration of mirror neuron system and sensorimotor cortex under virtual self-actions visual perception

Virtual reality (VR) technology, with the advantage of immersive visual experience, has been increasingly applied in the rehabilitation therapy of motor deficits. The functional integration of the mirror neuron system and the sensorimotor cortex under the visual perception of actions is one of the theoretical bases for the application of action observation in the neurorehabilitation of motor deficits. Whether the visual experience changes brought by VR technology can further promote this functional integration to be further confirmed. Using the exact low-resolution brain electromagnetic tomography (eLORETA) source localization method, we calculated and statistically tested the whole brain cortical voxel current density estimation under the Electroencephalogram (EEG) signals collected during action observation under the first-person and third-person perspectives in the VR scene for twenty healthy adults. Furthermore, the functional connectivity between the mirror neuron system and the sensorimotor cortex was analyzed using the lagged phase synchronization method. Under the first-person perspective in the VR scene, the current density changes of the core cortices of the mirror neuron system lead to a larger average event-related potential, more significant suppression in the α1 and α2 frequency bands of EEG signals, and a significant enhancement of functional connectivity between the core cortices of the mirror neuron system and the sensorimotor cortex. These findings indicate that compared with the traditional action observation, the visual reappearance of self-actions in the VR scene further stimulates the activity of the core cortices of the mirror neuron system, and promotes the functional integration of the core cortices of the mirror neuron system and the sensorimotor cortex.

Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event-related brain potentials.

Natural language processing models based on machine learning (ML-NLP models) have been developed to solve practical problems, such as interpreting an Internet search query. These models are not intended to reflect human language comprehension mechanisms, and the word representations used by ML-NLP models and human brains might therefore be quite different. However, because ML-NLP models are trained with the same kinds of inputs that humans must process, and they must solve many of the same computational problems as the human brain, ML-NLP models and human brains may end up with similar word representations. To distinguish between these hypotheses, we used representational similarity analysis to compare the representational geometry of word representations in two ML-NLP models with the representational geometry of the human brain, as indexed with event-related potentials (ERPs). Participants listened to stories while the electroencephalogram was recorded. We extracted averaged ERPs for each of the 100 words that occurred most frequently in the stories, and we calculated the similarity of the neural response for each pair of words. We compared this 100×100 similarity matrix to the 100×100 similarity matrix for the word pairs according to two ML-NLP models. We found significant representational similarity between the neural data and each ML-NLP model, beginning within 250ms of word onset. These results indicate that ML-NLP systems that are designed to solve practical technology problems have a representational geometry that is correlated with that of the human brain, presumably because both are influenced by the structural properties and statistics of language.

Modeling electrophysiological measures of decision-making and performance monitoring in neurotypical children engaging in a speeded flanker task.

This study aims to use structural equation modeling (SEM) to investigate the role of error processing in behavioral adaptation in children by testing relationships between error-related and stimulus-related event-related potentials (ERPs) obtained from two sessions of a speeded Eriksen flanker task. First, path models of averaged ERP components and mean response times (N1→P2→N2→P3→RTs) while controlling for trait effects, age, and sex, on each was examined separately for correct and incorrect trials from each session. While the model demonstrated acceptable fit statistics, the four models yielded diverse results. Next, path models for correct and incorrect trials were tested using latent variables defined by factoring together respective measures of ERP component amplitudes from each session. Comparison of correct and incorrect models revealed significant differences in the relationships between the successive measures of neural processing after controlling for trait effects. Moreover, latent variable models controlling for both trait and session-specific state variables yielded excellent model fit while models without session-specific state variables did not. In the final model, the error-related neural activity (i.e., the ERN and Pe) from incorrect trials was found to significantly relate to the stream of neural processes contributing to trials with the correct behavior. Importantly, the relationship between RT and error detection in the final model signifies a brain-and-behavior feedback loop. These findings provided empirical evidence that supports the adaptive orienting theory of error processing by demonstrating how the neural signals of error processing influence behavioral adaptations that facilitate correct behavioral performance.

Is It Time to Reconsider the ‘Gold Standard’ for Nativelikeness in ERP Studies on Grammatical Processing in a Second Language? A Critical Assessment Based on Qualitative Individual Differences

Abstract In most event-related potential (ERP) studies on the second language (L2) processing, the native speaker (L1) control group’s grand average ERP pattern serves as the ‘gold standard’ that the L2 group has to reach to be labeled ‘native-like’. This relies on the assumption that the grand average is representative of all or most individuals in a group. Recent research, however, has shown that there can be considerable systematic qualitative variability between individuals even in coherent L1 samples, especially in studies on morphosyntactic processing. We discuss how these qualitative individual differences can undermine previous findings from the gold standard paradigm, and critically assess the main ERP components used as markers for nativelike grammatical processing, namely the left-anterior negativity and the P600. We argue that qualitative variation reflects the dynamics characteristic of nativelike grammatical processing and propose a model for experimental designs that can capture these processing dynamics and, thereby, has the potential to provide a more fine-grained understanding of nativelike attainment in an L2.

Peak selection and latency jitter correction in developmental event-related potentials.

Event-related potentials (ERPs) provide great insight into neural responses, yet developmental ERP work is plagued with inconsistent approaches to identifying and quantifying component latency. In this analytical review, we describe popular conventions for the selection of time windows for ERP analysis and assert that a data-driven strategy should be applied to the identification of component latency within individual participants' data. This may overcome weaknesses of more general approaches to peak selection; however, it does not account for trial-by-trial variability within a participant. This issue, known as ERP latency jitter, may blur the average ERP, misleading the interpretation of neural mechanisms. Recently, the ReSync MATLAB toolbox has been made available for correction of latency jitter. Although not created specifically for pediatric ERP data, this approach can be adapted for developmental researchers. We have demonstrated the use of the ReSync toolbox with individual infant and child datasets to illustrate its utility. Details about our peak detection script and the ReSync toolbox are provided. The adoption of data processing procedures that allow for accurate, study-specific component selection and reduce trial-by-trial asynchrony strengthens developmental ERP research by decreasing noise included in ERP analyses and improving the representation of the neural response.