Event-related Potential Waveforms Research Articles

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.

Brain vital signs as a quantitative measure of cognition: Methodological implementation in a care home environment

IntroductionManaging cognitive function in care homes is a significant challenge. Individuals in care have a variety of scores across standard clinical assessments, such as the Mini-Mental Status Exam (MMSE), and many of them have scores that fall within the range associated with dementia. A recent methodological advance, brain vital sign monitoring through auditory event-related potentials, provides an objective and sensitive physiological measurement to track abnormalities, differences, or changes in cognitive function. Taking advantage of point-of-care accessibility, the current study evaluated the methodological feasibility, the assessment of whether a particular research method can be successfully implemented, of quantitatively measuring cognition of care home residents using brain vital signs. Secondarily, the current study examined the relationship between brain vital signs, specifically the cognitive processing associated N400 component, and MMSE scores in care home residents. Materials and methodsBrain vital signs used the established N100 (auditory sensation), P300 (basic attention), and N400 (cognitive processing) event-related potential (ERP) components. A total of 52 residents were enrolled, with all participants evaluated using the MMSE. Participants were assigned into homogeneous groups based on their MMSE scores, and were categorized into low (n = 14), medium (n = 17), and high (n = 13) MMSE groups. Both brain vital sign measures and underlying ERP waveforms were examined. Statistical analyses used partial least squares correlation (PLS) analyses in which both MMSE and age were included as factors, as well as jackknife approaches, to test for significant brain vital sign changes. ResultsThe current study successfully measured and analyzed standardized, quantifiable brain vital signs in a care home setting. ERP waveform data showed specific N400 changes between MMSE groups as a function of MMSE score. PLS analyses confirmed significant MMSE-related and age-related differences in the N400 amplitude (p < 0.05, corrected). Similarly, the jackknife approach emphasized the N400 latency difference between the low and high MMSE groups. Discussion and conclusionIt was possible to acquire brain vital signs measures in care home residents. Additionally, the current study evaluated brain vital signs relative to MMSE in this group. The comparison revealed significant decreasing in N400 response amplitude (cognitive processing) as a function of both MMSE score and age, as well as a slowing of N400 latency. The findings indicate that objective neurophysiological measures of impairment are detectable in care home residents across the span of MMSE scores. Direct comparison to MMSE- and age-related variables represents a critical initial step ahead of future studies that will investigate relative improvements in sensitivity, validity, reliability and related advantages of brain vital sign monitoring.

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.

Effects of sensory modality and task relevance on omitted stimulus potentials.

Omitted stimulus potentials (OSPs) occur when a sensory stimulus is unexpectedly omitted. They are thought to reflect predictions about upcoming sensory events. The present study examined how OSPs differ across the sensory modalities of predicted stimuli. Twenty-nine university students were asked to press a mouse button at a regular interval of 1-2s, which was immediately followed by either a visual or auditory stimulus in different blocks. The stimuli were sometimes omitted (p = 0.2), to which event-related potentials (ERPs) were recorded. The results showed that stimulus omissions in both modalities elicited ERP waveforms consisting of three components, oN1, oN2, and oP3. The peak latencies of these components were shorter in the auditory modality than in the visual modality. The amplitudes of OSPs were larger when participants were told that the omission indicated their poor performance (i.e., they pressed a button at an irregular interval) than when it was irrelevant to their performance. These findings suggest that OSPs occur from around 100ms in a modality-specific manner and increase in amplitude depending on the task relevance of stimulus omissions.

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.

GAM-based individual difference measures for L2 ERP studies

ERPs (Event-Related Potentials) have become a widely-used measure to study second language (L2) processing. To study individual differences, traditionally a component outcome measure is calculated by averaging the amplitude of a participant's brain response in a pre-specified time window of the ERP waveform in different conditions (e.g., the ‘Response Magnitude Index’; Tanner, Mclaughlin, Herschensohn & Osterhout, 2013). This approach suffers from the problem that the definition of such time windows is rather arbitrary, and that the result is sensitive to outliers as well as participant variation in latency. The latter is particularly problematic for studies on L2 processing. Furthermore, the size of the ERP response (i.e., amplitude difference) of an L2 speaker may not be the best indicator of near-native proficiency, as native speakers also show a great deal of variability in this respect, with the ‘robustness’ of an L2 speaker's ERP response (i.e., how consistently they show an amplitude difference) potentially being a more useful indicator. In this paper we introduce a novel method for the extraction of a set of individual difference measures from ERP waveforms. Our method is based on participants’ complete waveforms for a given time series, modelled using generalized additive modelling (GAM; Wood, 2017). From our modelled waveform, we extract a set of measures which are based on amplitude, area and peak effects. We illustrate the benefits of our method compared to the traditional Response Magnitude Index with data on the processing of grammatical gender violations in 66 Slavic L2 speakers of German and 29 German native speakers. One of our measures in particular appears to outperform the others in characterizing differences between native speakers and L2 speakers, and captures proficiency differences between L2 speakers: the ‘Normalized Modelled Peak’. This measure reflects the height of the (modelled) peak, normalized against the uncertainty of the modelled signal, here in the P600 search window. This measure may be seen as a measure of peak robustness, that is, how reliable the individual is able to show a P600 effect, largely independently of where in the P600 window this occurs. We discuss implications of our results and offer suggestions for future studies on L2 processing. The code to implement these analyses is available for other researchers.

Exploration of Categorization and Category-Based Induction on Waste Sorting: A Follow-Up Observation by NeuroSky MindWave.

Waste sorting, as an embodiment of behavioral cognition, is regulated by two cognitive processes, namely, Categorization (C) and Category-Based Induction (CBI). This study employed the event-related potential (ERP) technique to assess whether there is a transformation between C and CBI in waste sorting cognition, in order to help individuals establish a correct waste sorting behavior. We reported a case of intervention in waste sorting education with a 58-year-old Chinese woman to discriminate whether such intervention facilitates the transition between C and CBI. The results showed that the waste sorting cognition follows a developmental model as C-CBI-C, where education may help the subject build a cognitive framework for waste sorting by altering inherent misperceptions and filling gaps in classification knowledge. The results also noticed that FN400 is identified as a characteristic waveform in the CBI process, by which it is indicated that the first 4 to 7 days of education is a critical period for establishing the cognitive framework. Through a comparison of the ERP waveforms at different stages of intervention, the results are insightful to help individuals improve their cognition of waste sorting.

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...

Early maturation of sound duration processing in the infant’s brain

The ability to process sound duration is crucial already at a very early age for laying the foundation for the main functions of auditory perception, such as object perception and music and language acquisition. With the availability of age-appropriate structural anatomical templates, we can reconstruct EEG source activity with much-improved reliability. The current study capitalized on this possibility by reconstructing the sources of event-related potential (ERP) waveforms sensitive to sound duration in 4- and 9-month-old infants. Infants were presented with short (200 ms) and long (300 ms) sounds equiprobable delivered in random order. Two temporally separate ERP waveforms were found to be modulated by sound duration. Generators of these waveforms were mainly located in the primary and secondary auditory areas and other language-related regions. The results show marked developmental changes between 4 and 9 months, partly reflected by scalp-recorded ERPs, but appearing in the underlying generators in a far more nuanced way. The results also confirm the feasibility of the application of anatomical templates in developmental populations.

Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition

Noninvasive brain–computer interfaces (BCIs) show great potential in applications including sleep monitoring, fatigue alerts, neurofeedback training, etc. While noninvasive BCIs do not impose any procedural risk to users (as opposed to invasive BCIs), the acquisition of high-quality electroencephalograms (EEGs) in the long term has been challenging due to the limitations of current electrodes. Herein, we developed a semidry double-layer hydrogel electrode that not only records EEG signals at a resolution comparable to that of wet electrodes but is also able to withstand up to 12 h of continuous EEG acquisition. The electrode comprises dual hydrogel layers: a conductive layer that features high conductivity, low skin-contact impedance, and high robustness; and an adhesive layer that can bond to glass or plastic substrates to reduce motion artifacts in wearing conditions. Water retention in the hydrogel is stable, and the measured skin-contact impedance of the hydrogel electrode is comparable to that of wet electrodes (conductive paste) and drastically lower than that of dry electrodes (metal pin). Cytotoxicity and skin irritation tests show that the hydrogel electrode has excellent biocompatibility. Finally, the developed hydrogel electrode was evaluated in both N170 and P300 event-related potential (ERP) tests on human volunteers. The hydrogel electrode captured the expected ERP waveforms in both the N170 and P300 tests, showing similarities in the waveforms generated by wet electrodes. In contrast, dry electrodes fail to detect the triggered potential due to low signal quality. In addition, our hydrogel electrode can acquire EEG for up to 12 h and is ready for recycled use (7-day tests). Altogether, the results suggest that our semidry double-layer hydrogel electrodes are able to detect ERPs in the long term in an easy-to-use fashion, potentially opening up numerous applications in real-life scenarios for noninvasive BCI.

Successful reproduction of a large EEG study across software packages

As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results.In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N = 500) from (Williams et al., 2021) using three of the most commonly used open-source Matlab-based EEG software tools: EEGLAB, Brainstorm and FieldTrip. After reproducing the same original preprocessing workflow in each software, the resulting event-related potentials (ERPs) were qualitatively and quantitatively compared in order to examine the degree of consistency/discrepancy between software packages. Our findings show a good degree of convergence in terms of the general profile of ERP waveforms, peak latencies and effect size estimates related to specific signal features. However, considerable variability was also observed in the magnitude of the absolute voltage observed with each software package as reflected by the similarity values and observed statistical differences at particular channels and time instants. In conclusion, we believe that this study provides valuable clues to better understand the impact of the software tool on the analysis of EEG results.

Behavioral and electrophysiological evidence for valence-space congruency effect of emotional pictures in a spatial Stroop task

Previous studies have shown that people implicitly associate the emotional valence of abstract words with vertical position (i.e., positive words up, negative words down), resulting in the so-called valence-space congruency effect. Research has demonstrated that there is a valence-space congruency effect when it comes to emotional words. It's interesting to see that whether the emotional pictures with different levels of valence are mapped to distinct vertical space positions. Here, the event-related potential (ERP) and time-frequency techniques were employed to investigate the neural basis of the valence-space congruency effect of emotional pictures in a spatial Stroop task. Firstly, this study showed that the reaction time of the congruent condition (i.e., positive pictures in the top and negative pictures in the bottom of the screen) was significantly shorter than that of the incongruent condition (i.e., positive pictures in the bottom and negative pictures in the top of the screen), suggesting that exposure to stimuli with positive or negative valence, regardless of whether these stimuli were comprised of words or pictures, would be enough to invoke the vertical metaphor. Moreover, we found that the congruency between the vertical position and the valence of emotional pictures could significantly modulate the amplitude of the P2 component and the Late Positive Component (LPC) in ERP waveforms, as well as the post-stimulus alpha-ERD in the time-frequency plane. This study has conclusively demonstrated the presence of a space-valence congruency effect in emotional pictures and has elucidated the underlying neurophysiological mechanisms associated with the valence-space metaphor.

Updating the relationship of the Ne/ERN to task-related behavior: A brief review and suggestions for future research.

The error negativity/error-related negativity (Ne/ERN) is one of the most well-studied event-related potential (ERP) components in the electroencephalography (EEG) literature. Peaking about 50 ms after the commission of an error, the Ne/ERN is a negative deflection in the ERP waveform that is thought to reflect error processing in the brain. While its relationships to trait constructs such as anxiety are well-documented, there is still little known about how the Ne/ERN may subsequently influence task-related behavior. In other words, does the occurrence of the Ne/ERN trigger any sort of error corrective process, or any other behavioral adaptation to avoid errors? Several theories have emerged to explain how the Ne/ERN may implement or affect behavior on a task, but evidence supporting each has been mixed. In the following manuscript, we review these theories, and then systematically discuss the reasons that there may be discrepancies in the literature. We review both the inherent biological factors of the neural regions that underlie error-processing in the brain, and some of the researcher-induced factors in analytic and experimental choices that may be exacerbating these discrepancies. We end with a table of recommendations for future researchers who aim to understand the relationship between the Ne/ERN and behavior.

Neural correlates of face perception modeled with a convolutional recurrent neural network

Objective. Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We aimed to develop a computational model of visual ERP generation to study this phenomenon which consisted of a three-dimensional convolutional neural network (CNN) connected to a recurrent neural network (RNN). Approach. The CNN provided image representation learning, complimenting sequence learning of the RNN for modeling visually-evoked potentials. We used open-access data from ERP Compendium of Open Resources and Experiments (40 subjects) to develop the model, generated synthetic images for simulating experiments with a generative adversarial network, then collected additional data (16 subjects) to validate predictions of these simulations. For modeling, visual stimuli presented during ERP experiments were represented as sequences of images (time x pixels). These were provided as inputs to the model. By filtering and pooling over spatial dimensions, the CNN transformed these inputs into sequences of vectors that were passed to the RNN. The ERP waveforms evoked by visual stimuli were provided to the RNN as labels for supervised learning. The whole model was trained end-to-end using data from the open-access dataset to reproduce ERP waveforms evoked by visual events. Main results. Cross-validation model outputs strongly correlated with open-access (r = 0.98) and validation study data (r = 0.78). Open-access and validation study data correlated similarly (r = 0.81). Some aspects of model behavior were consistent with neural recordings while others were not, suggesting promising albeit limited capacity for modeling the neurophysiology of face-sensitive ERP generation. Significance. The approach developed in this work is potentially of significant value for visual neuroscience research, where it may be adapted for multiple contexts to study computational relationships between visual stimuli and evoked neural activity.

Comparing event-related potentials of retrospective and prospective metacognitive judgments during episodic and semantic memory

It is unclear whether metacognitive judgments are made on the basis of domain-generality or domain-specificity. In the current study, we compared both behavioral and event-related potential (ERP) correlates of retrospective (retrospective confidence judgments: RCJs), and prospective (feeling of knowing: FOK) metacognitive judgments during episodic and semantic memory tasks in 82 participants. Behavioral results indicated that FOK judgments reflect a domain-specific process, while RCJ reflect a domain-general process. RCJ and FOK judgments produced similar ERP waveforms within the memory tasks, but with different temporal dynamics; thus supporting the hypothesis that retrospective and prospective metacognitive judgments are distinct processes. Our ERP results also suggest that metacognitive judgments are linked to distributed neural substrates, rather than purely frontal lobe functioning. Furthermore, the role of intra-subject and inter-subject differences in metacognitive judgments across and within the memory tasks are highlighted.

Jointly modeling behavioral and EEG measures of proactive control in task switching.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation ofthe relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels. We examined three joint models: The first characterized the core link between EEG and criterion, the second added a switch preparation input parameter and the third also added a task preparation input parameter. The criterion-EEG link was strongest just before target onset. Inclusion of switch and task preparation parameters did not improve the performance of the criterion-EEG link but was necessary to accurately model the ERP waveform morphology. While we successfully jointly modeled latent model parameters and EEG data from a task-switching paradigm, these findings show that customized cognitive models are needed that are tailored to the multiple cognitive control processes underlying task-switching performance. This is the first paper to implement joint modeling of behavioral measures and single-trial electroencephalography (EEG) data derived from the cue-target interval in a cued-trials task-switching paradigm. Model hyperparameters showed a strong link between response criterion and the pre-target negativity amplitude. Additional parameters (switch preparation, task preparation) were necessary to model the cue-locked ERP waveform morphology. This is consistent with multiple cognitive control processes underlying proactive control and points to the need for more nuanced models of task-switching performance.

Principal component analysis of auditory ERPs discriminates mild cognitive impairment and Alzheimer’s disease from normal aging

AbstractBackgroundSensory gating refers to an inhibitory mechanism of the brain that protects higher cortical centers from being flooded with repetitive, redundant sensory inputs. Sensory gating is modulated by cholinergic transmission, the deterioration of which is a prominent neurochemical feature of Alzheimer’s disease (AD), the most common form of dementia in the elderly. The study investigated whether alterations in inhibitory gating, assessed by event‐related potentials (ERPs) in response to repeated auditory stimuli, can discriminate AD and mild cognitive impairment (MCI) from normal aging.MethodEvent‐related electroencephalogram was recorded from 38 AD patients, 37 MCI patients, and 38 elderly cognitively normal control (NC) participants in a conditioning–testing paradigm. ERP waveforms, stimulus‐locked changes in power, and inter‐trial phase clustering (ITPC) were extracted and submitted to a covariance‐based principal component analysis (PCA) to yield data‐driven component measures.ResultNon‐parametric cluster‐based permutation tests revealed statistically significant differences for ERP waveforms in separating AD, for power in separating AD and MCI, and for ITPC in separating AD, MCI, and NC from the other two groups pooled together.ConclusionThe study confirms that alterations in auditory sensory gating can discriminate between AD, MCI, and normal aging. Auditory ERPs might serve as an objective, non‐invasive, and cost‐effective method for evaluating cognitive decline suggestive of AD and to monitor disease progression, therapeutic efficacy, or effects of novel drug candidates in clinical trials.

A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.