Mnemotecnias para la aplicación e interpretación del electroencefalograma de amplitud integrada en recién nacidos / Mnemonics for the use and interpretation of amplitude-integrated electroencephalography in newborn infants

La electroencefalografía (EEG) siempre ha sido considerada una materia especializada, que amerita de entrenamiento para su aplicación e interpretación; esto ha provocado que el acceso a estos estudios quedara confinado a neurólogos y neurofisiólogos. El recién nacido ingresado en la unidad de cuidados intensivos neonatales (UCIN) amerita de monitorización neurológica para establecer diagnóstico y pronóstico, por lo que se necesita una herramienta sencilla y accesible para el personal de la UCIN. Estas características han sido cubiertas por el electroencefalograma de amplitud integrada (aEEG) que, a través de patrones visuales simples de la actividad cerebral, permite el abordaje de la condición neurológica. El objetivo de este ensayo se orienta al manejo de mnemotecnias que faciliten la identificación de patrones visuales normales y patológicos en el aEEG. La nomenclatura empleada, aunque puede parecer simple, pretende crear una idea fácilmente asimilable de los conceptos básicos para la aplicación e interpretación de la neuromonitorización con aEEG.

An electroencephalography (EEG) has always been considered a specialized field, whose use and interpretation requires training. For this reason, access to these monitoring studies has been restricted to neurologists and neurophysiologists. Newborn infants admitted to the neonatal intensive care unit (NICU) require neurophysiological monitoring to establish their diagnosis and prognosis, so a simple and accessible tool is required for NICU staff. Such features have been covered by amplitude-integrated electroencephalography (aEEG), which, through simple visual patterns of brain activity, allows to approach neurological conditions. The objective of this study is to help with the management of mnemonics that facilitate the identification of normal and pathological visual patterns in an aEEG. Although simple in appearance, this nomenclature is intended to create an easy-to-understand idea of basic concepts for the use and interpretation of neurophysiological monitoring with aEEG.

Wireless ear EEG to monitor drowsiness.

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

Brain and grammar: revealing electrophysiological basic structures with competing statistical models.

Acoustic, lexical, and syntactic information are simultaneously processed in the brain requiring complex strategies to distinguish their electrophysiological activity. Capitalizing on previous works that factor out acoustic information, we could concentrate on the lexical and syntactic contribution to language processing by testing competing statistical models. We exploited electroencephalographic recordings and compared different surprisal models selectively involving lexical information, part of speech, or syntactic structures in various combinations. Electroencephalographic responses were recorded in 32 participants during listening to affirmative active declarative sentences. We compared the activation corresponding to basic syntactic structures, such as noun phrases vs. verb phrases. Lexical and syntactic processing activates different frequency bands, partially different time windows, and different networks. Moreover, surprisal models based on part of speech inventory only do not explain well the electrophysiological data, while those including syntactic information do. By disentangling acoustic, lexical, and syntactic information, we demonstrated differential brain sensitivity to syntactic information. These results confirm and extend previous measures obtained with intracranial recordings, supporting our hypothesis that syntactic structures are crucial in neural language processing. This study provides a detailed understanding of how the brain processes syntactic information, highlighting the importance of syntactic surprisal in shaping neural responses during language comprehension.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Exploring the neuropsychological basis of behavioral contagion during learning about another agent's social preferences: Evidence from an ERP study.

Social contagion is a pervasive phenomenon and an important social influence that involves the rapid dissemination (propagation) of behaviors, attitudes, emotions, or ideas from one person to another, often without conscious reflection or rational thought. This phenomenon is closely related to conformity, by which a person changes his/her original ideas and attitude and imitates certain behavior of others. Although some behavioral research has been carried out on contagion and conformity, there is very little neuropsychological understanding of these phenomena. Existing research on social influence and conformity has predominantly focused on tasks like mental rotation or rating tasks involving facial expressions, with fewer studies exploring risk preferences and temporal discounting. However, there is a notable gap in the literature when it comes to examining social influence and conformity using other­regarding preference models derived from heterodox economics. To address this research gap, the present study investigates the neuropsychological underpinnings of social contagion by utilizing event­related potentials (ERPs) recorded while subjects engage in mini­dictator games. The behavioral analysis revealed that contagion had an impact on the participants' preferences, leading to a change in their choices. We observed a P300 component in the midline and right posterior during the time window of 200­350 ms after stimulus onset, which showed a significant increase in mean amplitude when participants observed others' behavior, compared to when they made decisions based on their own preferences. Moreover, the lack of late positive potential in the time window of 500­650 ms suggests that the presence of P300 may indicate difficulty in making decisions. In summary, by analyzing both behavioral and ERP data, this study may provide a more comprehensive understanding of the cognitive and neural processes that drive conformity and contagion behavior. Our analysis has the potential to inform policymakers in developing effective interventions for promoting positive social behaviors and reducing negative ones.

Primary somatosensory cortex CB1 and 5­HT1A receptors interaction in the penicillin model of epilepsy.

Cannabinoid and serotonin systems regulate many biological processes. The aim of the present study was to investigate the functional interaction between the cannabinoid and serotonergic systems of the primary somatosensory region (S1) of the brain in epileptiform activity caused by penicillin. The ACEA (an agonist of CB1 receptor), AM­251 (an antagonist of CB1 receptor), 8­OH­DPAT (an agonist of 5­HT1A receptor) and WAY­100635 (an antagonist of 5­HT1A receptor) were administered into the S1 after the same site administration of penicillin in urethane­anesthetized rats. Electrocorticographic recording was done for a 90­min period. The spike waves number and amplitude were recorded in 15­min intervals. Areas under the curve (AUC) of the above­mentioned spike alterations was calculated in 90 min. Spike waves with frequency of 30/min and amplitude of 1.3 mV were appeared after penicillin microinjection. The ACEA (50 ng), 8­OH­DPAT (500 ng) and ACEA (10 ng) plus 8­OH­DPAT (100 ng) reduced epileptiform activity. The AM­251 (50 ng) and WAY­100365 (500 ng) prevented the reducing effects of ACEA (50 ng) and 8­OH­DPAT (500 ng). The AM­251 alone increased spike waves frequency. The AUC results supported the effects of the above­mentioned treatments. The results showed that activating CB1 and 5­HT1A receptors in the S1 may reduce the epileptiform activity caused by penicillin. Therefore, alone and together activation of central CB1 and 5­HT1A receptors might be considered in the management of epilepsy treatment.

My choice, my actions: self-determination, not instrumental value of outcomes enhances outcome monitoring during learning.

Freedom of choice enhances our sense of agency. During goal-directed behavior, the freedom to choose between different response options increases the neural processing of positive and negative feedback, indicating enhanced outcome monitoring under conditions of high agency experience. However, it is unclear whether this enhancement is predominantly driven by an increased salience of self- compared to externally determined action outcomes or whether differences in the perceived instrumental value of outcomes contribute to outcome monitoring in goal-directed tasks. To test this, we recorded electroencephalography while participants performed a reinforcement learning task involving free choices, action-relevant forced choices, and action-irrelevant forced choices. We observed larger midfrontal theta power and N100 amplitudes for feedback following free choices compared with action-relevant and action-irrelevant forced choices. In addition, a Reward Positivity was only present for free but not forced choice outcomes. Crucially, our results indicate that enhanced outcome processing is not driven by the relevance of outcomes for future actions but rather stems from the association of outcomes with recent self-determined choice. Our findings highlight the pivotal role of self-determination in tracking the consequences of our actions and contribute to an understanding of the cognitive processes underlying the choice-induced facilitation in outcome monitoring.

Effects of daily listening to 6 Hz binaural beats over one month: an event-related potentials study.

The aim of the present study was to identify cognitive alterations, as indicated by event-related potentials (ERPs), after one month of daily exposure to theta binaural beats (BBs) for 10 minutes. The recruited healthy subjects (n = 60) were equally divided into experimental and control groups. For a month, the experimental group was required to practice BBs listening daily, while the control group did not. ERPs were assessed at three separate visits over a span of one month, with a two-week interval between each visit. At each visit, ERPs were measured before and after listening. The auditory and visual ERPs significantly increased the auditory and visual P300 amplitudes consistently at each visit. BBs enhanced the auditory N200 amplitude consistently across all visits, but the visual N200 amplitude increased only at the second and third visits. Compared to the healthy controls, daily exposure to BBs for two weeks resulted in increased auditory P300 amplitude. Additionally, four weeks of BBs exposure not only increased auditory P300 amplitude but also reduced P300 latency. These preliminary findings suggest that listening to BBs at 6 Hz for 10 minutes daily may enhance certain aspects of cognitive function. However, further research is needed to confirm these effects and to understand the underlying mechanisms. Identifying the optimal duration and practice of listening to 6 Hz BBs could potentially contribute to cognitive enhancement strategies in healthy individuals.

Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification.

Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.

A MultiModal Vigilance (MMV) dataset during RSVP and SSVEP brain-computer interface tasks.

Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation.

Peripheral nesfatin-1 reduces basal brain activity but has limited effect on epilepsy-like conditions.

In this study, we investigated the effects of peripheral nesfatin-1 on basal brain activity and 4-aminopyridine (4-AP)-induced epileptiform activity, and its relationship with the electrocorticogram (ECoG) power spectrum and EEG bands. Forty-nine male Wistar rats were divided into seven groups: control sham, 4-AP (2.5 mg/kg i.p.), Nesfatin-1 (1, 2, and 4 µg/kg i.p.), Nesfatin-1 (2 µg/kg) post-treatment, and Nesfatin-1 (2 µg/kg) pre-treatment. Recordings were conducted for 70 min under ketamine/xylazine (90/10 mg/kg) anesthesia. In the post-treatment group, nesfatin-1 was injected 20 min after 4-AP induction. In the pre-treatment groups, nesfatin-1 was administered following basal recordings and before 4-AP injection. 4-AP induced epileptiform activity in all animals, peaking at 30 min. Nesfatin-1 (2 µg/kg) reduced basal brain activity (p < 0.05) and decreased alpha, delta, and theta bands in ECoG. Post-treatment of nesfatin-1 did not affect 4-AP-induced activity (p > 0.05) but increased gamma band activity (p > 0.05). Pre-treatment of nesfatin-1 reduced epileptiform activity between 50 and 60 min (p < 0.05), decreased delta bands, and increased gamma bands (p > 0.05). We conclude that peripheral nesfatin-1 modulates normal brain activity but has limited effects on abnormal discharges.

Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modeling.

Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modeling of postsynaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and depression (LTD). We combined our model with data from postmortem RNA expression studies (CommonMind gene-expression datasets) to assess the consequences of altered expression of plasticity-regulating genes for the amplitude of LTP and LTD. Our results show that the expression alterations observed post mortem, especially those in the anterior cingulate cortex, lead to impaired protein kinase A (PKA)-pathway-mediated LTP in synapses containing GluR1 receptors. We validated these findings using a genotyped electroencephalogram (EEG) dataset where polygenic risk scores for synaptic and ion channel-encoding genes as well as modulation of visual evoked potentials were determined for 286 healthy controls. Our results provide a possible genetic mechanism for plasticity impairments in schizophrenia, which can lead to improved understanding and, ultimately, treatment of the disorder.

Laminar pattern of sensory-evoked dynamic high-frequency oscillatory activity in the macaque auditory cortex.

High-frequency (>60 Hz) neuroelectric signals likely have functional roles distinct from low-frequency (<30 Hz) signals. While high-gamma activity (>60 Hz) does not simply equate to neuronal spiking, they are highly correlated, having similar information encoding. High-gamma activity is typically considered broadband and poorly phase-locked to sensory stimuli and thus is typically analyzed after transformations into absolute amplitude or spectral power. However, those analyses discard signal polarity, compromising the interpretation of neuroelectric events that are essentially dipolar. In the spectrotemporal profiles of field potentials in auditory cortex, we show high-frequency spectral peaks not phase-locked to sound onset, which follow the broadband peak of phase-locked onset responses. Isolating the signal components comprising the high-frequency peaks reveals narrow-band high-frequency oscillatory events, whose instantaneous frequency changes rapidly from >150 to 60 Hz, which may underlie broadband high-frequency spectral peaks in previous reports. The laminar amplitude distributions of the isolated activity had two peak positions, while the laminar phase patterns showed a counterphase relationship between those peaks, indicating the formation of dipoles. Our findings suggest that nonphase-locked HGA arises in part from oscillatory or recurring activity of supragranular-layer neuronal ensembles in auditory cortex.

Clinical Reasoning: Hyperventilation-Induced Alternating Hemiplegia With Concomitant Hemispheric EEG Slowing in a 7-Year-Old Girl With Headache.

A 7-year-old right-handed girl presented to the pediatric neurology outpatient clinic after 5 episodes of headache over the previous 3 months. Her family history was positive for migraine in the mother and maternal grandmother and for febrile seizures in the older sister. The neurologic examination and cognitive profile were normal. Five seconds after the end of hyperventilation, video-EEG showed high-amplitude delta waves predominantly over the left hemisphere with concomitant acute aphasia and right-sided weakness. After the event, which self-resolved over 8 minutes, the girl showed intact recall. A second instance of hyperventilation evoked the appearance of pseudo-rhythmic slow activity localized to the right hemisphere, associated with left-sided weakness, 20 seconds after the end of the test. This event spontaneously resolved in 3 minutes and was followed by headache.An exaggerated physiologic response to hyperventilation, the possible epileptic nature of the events, and a migraine variant were all considered in the differential. Nonetheless, the EEG slowing is shorter in duration and generalized in physiologic and paraphysiological conditions. A clear ictal morphology and evolution of the EEG activity were lacking in this case, and migraine attacks induced by hyperpnea have not been reported to date. Instead, EEG alterations similar to that observed in our patient are described in association with vascular abnormalities. We report the clinical presentation and diagnostic workup of a rare cerebrovascular disorder, highlighting the key features in the differential. Our case emphasizes the clinical value of the EEG rebuild-up phenomenon, which can help the clinician in achieving a prompt diagnosis.

Dynamic mechanisms that couple the brain and breathing to the external environment.

Brain and breathing activities are closely related. However, the exact neurophysiological mechanisms that couple the brain and breathing to stimuli in the external environment are not yet agreed upon. Our data support that synchronization and dynamic attunement are two key mechanisms that couple local brain activity and breathing to external periodic stimuli. First, we review the existing literature, which provides strong evidence for the synchronization of brain and breathing in terms of coherence, cross-frequency coupling and phase-based entrainment. Second, using EEG and breathing data, we show that both the lungs and localized brain activity at the Cz channel attune the temporal structure of their power spectra to the periodic structure of external auditory inputs. We highlight the role of dynamic attunement in playing a key role in coordinating the tripartite temporal alignment of localized brain activity, breathing and input dynamics across longer timescales like minutes. Overall, this perspective sheds light on potential mechanisms of brain-breathing coupling and its alignment to stimuli in the external environment.

Early Family Experiences and Neural Activity in Rural Pakistani Children: The Differential Role of Gender.

Adversity within low- and middle-income countries (LMICs) poses severe threats to neurocognitive development, which can be partially mitigated by high-quality early family experiences. Specifically, maternal scaffolding and home stimulation can buffer cognitive development in LMIC, possibly by protecting underlying neural functioning. However, the association between family experiences and neural activity remains largely unexplored in LMIC contexts. This study explored the relation of early family experiences to later cognitive skills and absolute gamma power (21-45 Hz), a neural marker linked to higher-order cognitive skills. Drawing data from the PEDS trial, a longitudinal study in rural Pakistan, we examined maternal scaffolding at 24 months and home stimulation quality at 18 months as predictors of verbal IQ, executive functions, and absolute gamma at 48 months for 105 mother-child dyads (52 girls). Maternal scaffolding interacted with gender to predict absolute gamma power, such that higher maternal scaffolding was related to higher gamma more strongly for girls. Maternal scaffolding also interacted with absolute gamma to predict executive functions, such that higher gamma was related to better executive functions only when maternal scaffolding was average to high. Individual differences in early family experiences may partially buffer the neural underpinnings of cognitive skills from adversity in LMIC.

Crossmodal hierarchical predictive coding for audiovisual sequences in the human brain.

Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.

Classifying coherent versus nonsense speech perception from EEG using linguistic speech features.

When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli: a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants. We modeled the neural tracking of linguistic features of the speech signals using a deep-learning model in a match-mismatch task that relates EEG signals to speech, while accounting for lexical segmentation features reflecting acoustic processing. The deep learning model effectively classifies coherent versus nonsense languages. We also observed significant differences in tracking patterns between comprehensible and incomprehensible speech stimuli within the same language. It demonstrates the potential of deep learning frameworks in measuring speech understanding objectively.

The influence of mental calculations on brain regions and heart rates.

Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroencephalogram (EEG) and electrocardiogram (ECG) signals of 36 healthy subjects during the performance of arithmetic tasks. To process EEG signals in different frequency bands, the energy and entropy of entropy (EoE) were extracted from the power spectrum and phase spectrum, respectively. Statistical analysis was conducted to determine meaningful features. These features were sent into support vector machine (SVM) and multi-layer perception (MLP) classifiers to assess their capability in separating math and rest classes. Results indicated the highest classification accuracy of 98.4% for classifying good counters in math and rest state using the MLP method. Based on the majority of features selected for each EEG channel, discriminative brain areas were identified. Analyzing EEG signals proved that math calculation may have multiple influences on various parts of the brain. By comparing good counters' brain activities to those in a resting state, prominent changes were observed in the F4, C4, T4, T5, P3, and O2 areas. However, O1 and O2 channels showed significant changes in the brain of bad counters compared to the resting state. Considering ECG signals also demonstrated that during math calculation the number of heart rates per minute surpasses the rest state. These alterations can occur due to cognitive abilities or emotional processes which were observed to be prominent in subjects who performed more accurate calculations.

Optimizing electroencephalogram duration for efficient detection of epileptiform abnormalities in diverse patient groups: a retrospective study.

BACKGROUND: There is no standardized EEG duration guideline for detecting epileptiform abnormalities in patients, and research on this topic is scarce. This study aims to determine an optimal EEG duration for efficient detection of epileptiform abnormalities across different patient groups. METHODS: Retrospective analysis was performed on EEG recordings and clinical data of patients with the first seizure and epilepsy. Patients were categorized based on various factors, including the interval time since the last seizure, use of anti-seizure medication (ASM), and seizure frequency. The detection ratio (DR) of epileptiform abnormalities and latency time for their discovery were calculated. Statistical analyses, including chi-square tests, logistic regression, and survival analysis were utilized to illustrate DR and latency times. RESULTS: In whole-night EEG recordings, the DR was 37.6% for the first seizure group and 57.4% for the epilepsy group. Although the maximum latency times were 720 min in both two groups, DR in the first seizure group was distinctly decreased beyond 300 min. Significant factors influencing the DR included the use of ASM in the first seizure group (P < 0.05) and seizure frequency in the epilepsy group (P < 0.001). For epilepsy patients who experience a seizure at least once a month or undergo timely EEG recordings (within 24 h after a seizure), the DR significantly increases, and the maximum latency time is reduced to 600 min (P < 0.001). Additionally, the DR was significantly reduced after 240 min in epilepsy patients who had been seizure-free for more than one year. CONCLUSIONS: In this retrospective study, we observed a maximum latency of 720 min for detecting epileptiform abnormalities in whole-night EEG recordings. Notably, epilepsy patients with a higher seizure frequency or timely EEG recordings demonstrated both a higher detection ratio and a shorter maximum latency time. For patients exhibiting a low detection ratio, such as those experiencing their first seizure or those with epilepsy who have been seizure-free for more than a year, a shorter EEG duration is recommended. These findings underscore the importance of implementing customized EEG strategies to meet the specific needs of different patient groups.