Electroencephalographic Analysis Research Articles

Sleep spindles as a predictor of cognitive motor dissociation and recovery of consciousness after acute brain injury.

Cognitive motor dissociation (CMD) can improve the accuracy to predict recovery of behaviorally unresponsive patients with acute brain injury, but acquisition and analysis of task-based electroencephalography (EEG) are technically challenging. N2 sleep patterns, such as sleep spindles on EEG, have been associated with good outcomes, rely on similar thalamocortical networks as consciousness and could provide less technically challenging complementary outcome predictors. In this prospective observational cohort study of 226 acutely brain injured patients, well-formed sleep spindles (WFSS) were more likely present in those with CMD than in those without CMD, often preceding the detection of CMD. WFSS were associated with a shorter time to recovery of consciousness, and both CMD and WFSS independently predicted recovery of independence, controlling for age, admission neurological status and injury type. WFSS are seen in approximately every third behaviorally unresponsive patient after acute brain injury, frequently precede detection of CMD and are a promising complementary predictor for recovery of consciousness and functional independence.

Early Prediction of the Evolution of Self-Limited Epilepsy With Centrotemporal Spikes to Epileptic Encephalopathy With Spike-and-Wave Activation in Sleep: A Prediction Model Construction Based on Quantitative Electroencephalography Characteristics.

To predict the progression of children with self-limited epilepsy with centrotemporal spikes (SeLECTS) to epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS). We conducted a retrospective analysis of early clinical and electroencephalography (EEG) data. Clinical parameters included demographic and epilepsy-related characteristics. EEG were qualitatively (localization, lateralization, synchrony, non-Rolandic discharges, nondipole spikes, multiple spikes, focal slow-wave activity) and quantitatively (spike-wave index [SWI], spike-wave frequency [SWF], power spectral density [PSD], phase-locking value [PLV], phase lag index [PLI], weighted phase lag index [wPLI], characteristic path length [CPL], clustering coefficient [CC], small-worldness [Sigma]) analyzed. A logistic regression-based prediction model was further formulated and evaluated. This study included 50 children with seizure-free typical SeLECTS and 76 who developed EE-SWAS. Multivariable logistic regression revealed that early EEG features-SWF, relative PSD in the alpha band, wPLI and CPL in the delta band-were associated with the risk of encephalopathic transformation. The model demonstrated good performance with an area under the curve of 0.817 (95% confidence interval 0.736-0.898). The model showed a good fit and clinical benefit. Initial quantitative EEG characteristics of SeLECTS can predict the development of EE-SWAS, suggesting distinct disease characteristics and pathogeneses in children at risk of encephalopathic transformation.

Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms.

Observer-dependent infant pain scales have limitations including discontinuous assessments and the lack of healthcare professionals' availability. We hypothesized that applying agnostic machine learning approaches to neonatal electroencephalographic (EEG) analysis may reveal features of the infant response to acute pain. EEG was recorded from 30 neonates undergoing acutely painful procedures (18 males, 34.0-41.7 weeks gestation at birth). EEG recordings were randomly assigned to training (n = 20) and testing (n = 10) datasets. Functional connectivity measures were calculated for each infant before and after pain-inducing procedures. A grid search including five machine learning models was conducted on the training dataset, and each model was evaluated using leave-one-subject-out cross-validation. An optimal model, having the highest F-1 score, was obtained and evaluated on the independent testing dataset. A gradient boosting model with 12 features showed optimal performance, with 90% area under the receiver operating characteristic curve suggesting high specificity (0.90) and precision (0.90). The five highest ranked features corresponded to EEG electrode pairs: T7-P4, Fz-CP5, FC1-TP10, CP6-Cz, and Fz-F3, suggesting involvement of the contralateral temporal gyrus, opercular cortex, thalamus, and bilateral insula in infant pain processing. Preliminary changes in functional connectivity indicate infant pain processing. Future machine learning algorithms can integrate physiological and behavioral parameters with EEG changes to accurately assess the complexity of infant pain responses. Trial Registration: ClinicalTrials.gov identifier: NCT03330496.

Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.

Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. The natural NDA by increasing N leads to the higher micro and macro area under the curve (AUC) for receiver operating curve values for the bigger N values than usage of synthetic NDA. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. For example, H more than 2-3 times higher for some PAs, i.e., it means that the shorter EEG fragments (< 2 s) demonstrate the scaling behavior of the higher complexity than the longer fragments. As far as these results were obtained by the relatively small DNN with the low resource requirements, this approach can be promising for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.

FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training

Objective.Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of efficiency, effectiveness and computation cost.Approach.In this study, we propose a multiscale temporal convolution and spatial-spectral attention network with adversarial training for automatically filtering artifacts, named filter artifacts network (FLANet). The multiscale convolution module can extract sufficient temporal information and the spatial-spectral attention network can extract not only non-local similarity but also spectral dependencies. To make data denoising more efficient and accurate, we adopt adversarial training with novel loss functions to generate outputs that are closer to pure signals.Main results.The results show that the method proposed in this paper achieves great performance in artifact removal and valid information preservation on EEG signals contaminated by different types of artifacts. This approach enables a more optimal trade-off between denoising efficacy and computational overhead.Significance.The proposed artifact removal framework facilitates the implementation of an efficient denoising method, contributing to the advancement of neural analysis and neural engineering, and can be expected to be applied to clinical research and to realize novel human-computer interaction systems.

Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments

Active transportation, such as cycling, improves mobility and general health. However, statistics reveal that in low- and middle-income countries, male and female cycling participation rates differ significantly. Existing literature highlights that women’s willingness to use bicycles is significantly influenced by their perception of security. This study employs virtual reality (VR) cycling simulation and electroencephalography (EEG) analysis to investigate factors influencing female cyclists’ perceptions of security in Tehran. A total of 52 female participants took part in four scenarios within a VR bicycle simulator, which simulates various environmental settings. In this experiment, participants’ brainwave signals are gathered through an EEG device, and a questionnaire with their stated preferences is filled out. The Gaussian mixture approach is used to cluster brainwave patterns based on security perception from EEG data. Subsequently, four supervised machine learning methods, random forest, support vector machine, logistic regression, and multilayer perceptron, are utilized to classify influential factors on security perception using clustered EEG data. Consequently, the support vector machine model, with an F1 score of 0.74, appears to be the most effective technique for the classification of environmental and surveillance factors. Furthermore, the SelectKBest algorithm determines that factors such as the presence of obstacles like kiosks, cycling routes passing through tunnels and underpasses, the level of incivility in the urban cycling environment, and the presence of informal surveillance have the biggest impact on female cyclists’ security perception.

A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages.

Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data. We applied principal component and independent component (IC) analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into ICs. The ICs were visually labeled for clear seizure transitions and bifurcation morphologies (BifMs), which were then examined using Bayesian multilevel modeling in the context of clinical factors. Our analysis reveals that certain onset bifurcations (saddle node on invariant circle and supercritical Hopf) are more prevalent during wakefulness compared with their reduced rate during nonrapid eye movement (NREM) sleep, particularly NREM3. We discuss the possible implications of our results in the context of modeling approaches and suggest additional avenues to continue this exploration. Furthermore, we demonstrate the feasibility of automating this classification process using machine learning, achieving high performance in identifying seizure-related ICs and classifying interspike interval changes. Our findings suggest that the noise in surface EEG may obscure certain BifMs, and we suggest technical improvements that could enhance detection accuracy. Expanding the dataset and incorporating long-term biological rhythms, such as circadian and multiday cycles, may provide a more comprehensive understanding of seizure dynamics and improve clinical decision-making.

Individualized Spectral Features in First-Episode and Drug-Naïve Major Depressive Disorder: Insights From Periodic and Aperiodic Electroencephalography Analysis.

Individualized Spectral Features in First-Episode and Drug-Naïve Major Depressive Disorder: Insights From Periodic and Aperiodic Electroencephalography Analysis.

GABALAGEN Alleviates Stress-Induced Sleep Disorders in Rats.

(1) Background: Gamma-aminobutyric acid (GABA) is an amino acid and the primary inhibitory neurotransmitter in the brain. GABA has been shown to reduce stress and promote sleep. GABALAGEN (GBL) is the product of fermented fish collagen by Lactobacillus brevis BJ20 and Lactobacillus plantarum BJ21, naturally enriched with GABA through the fermentation process and characterized by low molecular weight. (2) Methods: The present study evaluated the GABAA affinity of GBL through receptor binding assay. The sedative effects of GBL were investigated through electroencephalography (EEG) analysis in an animal model of electro foot shock (EFS) stress-induced sleep disorder, and then we examined the expression of orexin and the GABAA receptor in the brain region using immunohistochemistry and an enzyme-linked immunosorbent assay (ELISA). (3) Results: We found that on the binding assay, GBL displayed high affinity to the GABAA receptor. Also, after treatment with GBL, the percentage of the total time in rapid eye movement (REM) and non-rapid eye movement (NREM) sleep was significantly and dose-dependently increased in EFS-induced rats. Consistent with behavioral results, the GBL-treated groups showed that the expression of GABAA receptor immune-positive cells in the VLPO was markedly and dose-dependently increased. Also, the GBL-treated groups showed that the expression of the orexin-A level in LH was significantly decreased. (4) Conclusions: GBL showed efficacy and potential to be used as an anti-stress therapy to treat sleep deprivation through the stimulation of GABAA receptors and the consequent inhibition of orexin activity.

Do EEG Metrics Derived from Trailers Predict the Commercial Success of Movies? A Systematic Analysis of Five Independent Datasets

This research explores whether neural activity in response to movie trailers is consistently related to commercial success for the movies they promote. A systematic analysis of electroencephalography (EEG) recordings from a large sample of participants (n = 188) viewing a large set of movie trailers (k = 161) originating from multiple and internationally diverse datasets was performed. Several metrics that have previously been associated with commercial success—alpha, theta, beta, and gamma oscillations; alpha asymmetry; and intersubject correlations—were investigated. Only gamma activity was predictive of commercial success (box office revenue) above and beyond characteristics of the movie and self-reported liking. Importantly, the predictive power of gamma activity generalizes to new movies: Out-of-sample predictions improve significantly with gamma activity as a predictor. In addition, the findings provide initial evidence that mental processes associated with understanding and comprehension may underlie this gamma response. For movie marketing, the findings suggest that gamma activity elicited by movie trailers can be used to optimize trailers for maximum understanding and to inform prerelease marketing decisions. This work represents a significant step forward for the field of consumer neuroscience by confirming the value of EEG-derived metrics in predicting marketing-relevant outcome measures.

Repeated High-dose Fentanyl Administration in Rats Reveals Minimal Tolerance to Unconsciousness, Bradycardia, Muscle Rigidity, and Respiratory Depression.

Fentanyl is a synthetic opioid that is widely used in anesthesiology, but its illicit use is rapidly increasing. At high doses, fentanyl induces unconsciousness and muscle rigidity, the mechanisms of which are poorly understood. Since animal models are needed to study these effects, the aim of this study was to establish a rat model of fentanyl abuse and investigate the effects of repeated high-dose fentanyl injections on loss of righting reflex, heart rate, respiratory depression, muscle, and brain activity. Male and female Sprague-Dawley rats were studied (n = 40). A bolus of 100 µg/kg fentanyl was administered intravenously twice a week for 5 consecutive weeks. Time to return of righting reflex after fentanyl injection and changes in electromyography/electroencephalography activity as well as heart rate were analyzed. Additionally, arterial blood gas analysis for evaluation of ventilation was performed. Mixed-effect models with Dunnett test and effect sizes were used for statistical analysis. Repeated injections resulted in a U-shaped change in time to return of righting reflex with the longest latency after the first exposure (median, 50 [first to third quartile, 36 to 56] min) and the shortest after the fifth exposure (16 [13 to 33] min). After fentanyl administration, heart rate dropped immediately by 225 beats/min (95% CI, 179 to 271; F = 3,952.16; P < 0.001), while electromyography activity increased by 291% (95% CI, 212 to 370; F = 27.51; P < 0.001) and partial pressure of arterial carbon dioxide increased by 49.4 mmHg (95% CI, 40.6 to 58.2; F = 75.97; P < 0.001) within 5 min after injection. Additionally, pH decreased by 0.48 (95% CI, 0.41 to 0.54; F = 142.00; P < 0.01), and partial pressure of arterial oxygen decreased by 50.4 mmHg (40.8 to 60.0; F = 57.90; P < 0.001). Repeated fentanyl exposures did not significantly affect the extent of these changes (EMG, F = 1.63, P = 0.237; partial pressure of arterial carbon dioxide, F = 1.23, P = 0.312; heart rate, F = 1.05, P = 0.400; pH, F = 3.05, P = 0.066; arterial partial pressure of oxygen, F = 3.35, P = 0.052). Electroencephalography analysis revealed that repeated fentanyl exposures elicited significantly higher absolute power in frequencies greater than 20 Hz as indicated by an area under the receiver operator characteristics curve greater than 0.7. The authors established a rodent model of repeated high-dose fentanyl administration. Overall, significant evidence of tolerance was not observed after 10 exposures of high-dose fentanyl for any of the analyzed parameters. These results suggest that tolerance does not develop for fentanyl-induced unconsciousness, muscle rigidity, or respiratory depression.

A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis

In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. The structure of this paper is organized according to the categorization within the machine learning community, with representation learning as the foundational concept that encompasses both discriminative and generative approaches. We delve into self-supervised learning methods that enable the robust representation of brain signals, which are fundamental for a variety of downstream applications. Within the realm of discriminative methods, we explore advanced techniques such as graph neural networks (GNN), foundation models, and approaches based on large language models (LLMs). On the generative front, we examine technologies that leverage EEG data to produce images or text, offering novel perspectives on brain activity visualization and interpretation. This survey provides an extensive overview of these cutting-edge techniques, their current applications, and the profound implications they hold for future research and clinical practice. The relevant literature and open-source materials have been compiled and are consistently updated at https://github.com/wpf535236337/LLMs4TS.

Power Spectral Density and Default Mode Network Connectivity in Generalized Epilepsy Syndromes: What to Expect from Drug-Resistant Patients.

Background: Recent studies have described unique aspects of default mode network connectivity in patients with idiopathic generalized epilepsy (IGE). A complete background in this field could be gained by combining this research with spectral analysis. Objectives: An important objective of this study was to compare linear connectivity and power spectral densities across different activity bands of patients with juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), generalized tonic-clonic seizures alone (EGTCSA), and drug-resistant IGE (DR-IGE) with healthy, age-matched controls. Methods: This was an observational case-control study. We performed EEG spectral analysis in MATLAB and connectivity analysis with LORETA for 39 patients with IGE and 12 drug-resistant IGE (DR-IGE) and healthy, age-matched subjects. We defined regions of interest (ROIs) from the default mode network (DMN) and performed connectivity statistics using time-varying spectra for paired samples. Using the same EEG data, we compared mean power spectral density (PSD) with epilepsy subgroups and controls across different activity bands. Results: We obtained a modified value for the mean power spectral density in the beta band for the JME group as follows. The connectivity analysis showed that, in general, there was increased linear connectivity in the DMN for the JAE, JME, and EGCTSA groups compared to the healthy controls. Reduced linear connectivity between regions of the DMN was found for DR-IGE. Conclusions: Spectral analysis of electroencephalography (EEG) for generalized epilepsy syndromes seems to be less informative than connectivity analysis for DMN. DMN connectivity analysis, especially for DR-IGE, opens up the possibility of finding biomarkers related to drug response in IGE.

Spectral EEG and heart rate changes associated with leg movements during the suggested immobilization test in patients with restless legs syndrome.

Restless legs syndrome is usually associated with periodic limb movements during sleep, which are defined as repetitive, stereotyped movements in sleep. Changes in spectral analysis of electroencephalography and heart rate were shown to be associated with periodic limb movements during sleep and non-periodic leg movements in sleep. Considering the circadian distribution of symptoms of restless legs syndrome, we investigated spectral electroencephalography and heart rate accompanying periodic limb movements, isolated leg movements and short-interval leg movements during suggested immobilization test. The mean age of 53 patients was 51.9 ± 13 years, 54.7% were females. Prominent increases in electroencephalography activation were associated with periodic limb movements, isolated leg movements and short-interval leg movements during the suggested immobilization test, which were significant in all spectral bands (p < 0.001). An increase in all electroencephalography bands started ~10 s before periodic limb movements, isolated leg movements and short-interval leg movements; increases in delta and theta band activities ended ~10 s after the movements, while increases in alpha and beta band activities lasted for about ~20 s. Maximum increases in delta, theta, alpha and beta bands were all observed after periodic limb movements and short-interval leg movements, but before isolated leg movements in theta and alpha bands, and after isolated leg movements in delta and beta bands. A notably longer increase in alpha and beta bands was evident for periodic limb movements and short-interval leg movements. An increase in heart rate was prominent at 4-12 s after movement onset in short-interval leg movements, being significantly higher than those associated with periodic limb movements and isolated leg movements. Our study shows that, in patients with restless legs syndrome, periodic limb movements, isolated leg movements and short-interval leg movements during suggested immobilization test are associated with prominent cortical and cardiac activation, which warrants confirmation in larger restless legs syndrome cohorts and requires long-term follow-up studies to delineate its possible clinical consequences.

Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs

Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This paper introduces EEGCCT, an application of compact convolutional transformers designed specifically to improve the analysis of motor imagery tasks in EEG. Unlike traditional approaches, EEGCCT model significantly enhances generalization from limited data, effectively addressing a common limitation in EEG datasets. We validate and test our models using the open-source BCI Competition IV datasets 2a and 2b, employing a Leave-One-Subject-Out (LOSO) strategy to ensure subject-independent performance. Our findings demonstrate that EEGCCT not only outperforms conventional models like EEGNet in standard evaluations but also achieves better performance compared to other advanced models such as Conformer, Hybrid s-CViT, and Hybrid t-CViT, while utilizing fewer parameters and achieving an accuracy of 70.12%. Additionally, the paper presents a comprehensive ablation study that includes targeted data augmentation, hyperparameter optimization, and architectural improvements.

Feature selection based on game theory optimization to achieve desired performance metrics in seizure onset detection

Feature selection based on game theory optimization to achieve desired performance metrics in seizure onset detection

Application of 2D Extension of Hjorth’s Descriptors to Distinguish Defined Groups of Bee Pollen Images

Adulteration of food products is a serious problem in the current economy. Honey has become the third most counterfeit food product in the world and requires effective authentication methods. This article presents a new approach to the differentiation of bee pollen, which can support the development of a methodology to test honey quality based on the analysis of bee pollen. The proposed method is built on applying the Hjorth descriptors—Activity, Mobility, and Complexity—known from electroencephalography (EEG) analysis, for 2D bee pollen images. The sources for extracting the bee pollen images were the photos of honey samples, which were taken using a digital camera with a resolution of 5 megapixels connected to the tube of an optical microscope. The honey samples used were prepared according to the Polish standard PN-88/A-77626 (related to the European standard CELEX-32001L0110-PL-TXT). The effectiveness of the proposed method was positively verified for three selected groups of bee pollen—Brassica napus, Helianthus, and Phacelia—containing 35 images. Statistical analysis confirms the ability of the Hjorth descriptors to differentiate the indicated bee pollen groups. Based on the results obtained, there is a significant difference between the bee pollen groups under consideration regarding Activity p&lt;0.00001, Mobility p&lt;0.0001, and Complexity p&lt;0.00001.

Altered cortical functional networks in Wilson's Disease: A resting-state electroencephalogram study

Altered cortical functional networks in Wilson's Disease: A resting-state electroencephalogram study

GABALAGEN Facilitates Pentobarbital-Induced Sleep by Modulating the Serotonergic System in Rats.

Gamma-aminobutyric acid (GABA) is one of the inhibitory neurotransmitters with beneficial effects including sedative properties. However, despite various clinical trials, scientific evidence regarding the impact on sleep of orally ingested GABA, whether natural or synthesized through biological pathways, is not clear. GABALAGEN (GBL) is the product of fermented collagen by Lactobacillus brevis BJ20 (L. brevis BJ20) and Lactobacillus plantarum BJ21 (L. plantarum BJ21), enriched with GABA and characterized by low molecular weight. The aim of this study was to investigate the effect of GBL on sleep improvement via a receptor binding assay in a pentobarbital-induced sleep-related rat model. We utilized a pentobarbital-induced sleep-related rat model to conduct this research. The present study investigated the sedative effects of GBL through electroencephalography (EEG) analysis in the pentobarbital-induced sleep animal model. Exploration of the neural basis of these positive effects involved evaluating orexin in the brain via immunohistochemical methods and 5-HT in the serum using an enzyme-linked immunosorbent assay (ELISA). Furthermore, we conducted a binding assay for 5-HT2C receptors, as these are considered pivotal targets in the mechanism of action for sleep aids. Diazepam (DZP) was used as a positive control to compare the efficacy of GBL. Results: In the binding assay, GBL displayed binding affinity to the 5-HT2C receptor (IC50 value, 5.911 µg/mL). Administration of a low dose of GBL (GBL_L; 100 mg/kg) increased non-rapid eye movement sleep time and decreased wake time based on EEG data in pentobarbital-induced rats. Administration of a high dose of GBL (GBL_H; 250 mg/kg) increased non-rapid eye movement sleep time. Additionally, GBL groups significantly increased concentration of the 5-HT level in the serum. GBL_H decreased orexin expression in the lateral hypothalamus. Conclusion: Overall, the sedative effect of GBL may be linked to the activation of serotonergic systems, as indicated by the heightened affinity of the 5-HT2C receptor binding and elevated levels of 5-HT observed in the serum. This suggests that GBL holds promise as a novel compound for inducing sleep in natural products.

Neuro-Insights in Marketing Research: A PRISMA-Based Analysis of EEG Studies on Consumer Behavior

This study presents a comprehensive bibliometric and systematic analysis of electroencephalography (EEG) studies in consumer behavior within marketing research while exploring its academic and practical implications. Following the PRISMA protocol, a rigorous examination of 53 articles from the Web of Science database (WoS) was conducted. The analysis highlights that EEG has predominantly investigated consumer behavior across various marketing stimuli, including products, advertising, pricing, and branding. Notably, advertising emerged as the primary focus, encompassing 49% of the analyzed articles (26). The USA emerged as the leading country in neuromarketing, with a notable contribution from the University of California System. Frontiers in Neuroscience emerged as the most prolific journal. EEG in marketing research enables scholars to bypass verbal biases and gain profound insights into consumers’ responses, significantly contributing to over 90% of their reactions toward marketing stimuli. This study provides valuable insights into the diverse applications of EEG in marketing research, with potential avenues for further investigation in areas such as consumer personality and social consumer neuroscience, which remain relatively underexplored.