Search Results (62)

University students are frequently exposed to high cognitive demands, which can lead to sustained cognitive load and mental fatigue and may negatively affect learning outcomes and well-being. Electroencephalography (EEG) provides a non-invasive, real-time window into neural activity associated with cognitive effort. This systematic review aims to synthesize current evidence on EEG markers of cognitive load and mental fatigue in university students. The review was conducted in accordance with the PRISMA 2020 statement, and electronic databases including PubMed, Scopus, and Web of Science were systematically searched for peer-reviewed studies on EEG-based assessment of cognitive load and mental fatigue in healthy university students. Seven studies met the inclusion criteria, comprising 179 participants. Cognitive load was most often reflected in changes in theta, alpha, and beta power, as well as band ratios such as theta/alpha. Studies manipulating multimedia design principles generally reported lower cognitive load indicators and better learning performance when these principles were applied. EEG-based markers, particularly theta and posterior alpha activity, show promising potential for monitoring cognitive strain in educational settings; however, methodological variability and small sample sizes limit generalizability. Full article

Background and Objectives: Individuals with Parkinson’s disease (PD) experience deficits in working memory (WM) and sustained attention (ATTN), but diagnosing and monitoring these deficits remains challenging. This study compares machine learning (ML) classification models trained on EEG and pupillometry data from WM and ATTN tasks to identify task-specific and shared cognitive biomarkers of PD. Methods: EEG and pupillometry were recorded from PD patients and healthy controls (HC) during a visual change detection WM task and a continuous performance ATTN task. A standardized toolbox extracted 108 features, reduced via PCA and recursive elimination (RE) and classified using an SVM-RBF within a nested, 5-fold cross-validated pipeline, with class balancing (SMOTE) and feature selection performed strictly within training folds to prevent leakage. Results: On internal test folds, WM achieved 71% accuracy (F1 = 0.701) and ATTN achieved 73% (F1 = 0.699); on an independent hold-out set, WM achieved 63% accuracy (F1 = 0.626) and ATTN achieved 70% (F1 = 0.623). ATTN showed higher accuracy and precision, WM showed higher recall, and univariate analyses independently supported several top-ranked features (e.g., theta/beta and alpha/theta ratios); PAI and FAA, though top features in both tasks, reached univariate significance only in ATTN. These results indicate WM and ATTN yield complementary, task-linked neurophysiological signatures relevant to PD classification. Conclusion: At its current stage, this pipeline functions as a research tool for biomarker discovery rather than a clinical diagnostic, though larger, externally validated samples could support future screening and monitoring applications. Full article

Background: Children growing up in alcohol-affected families are exposed to chronic stress, adverse childhood experiences (ACEs), emotional insecurity, and environmental instability, all of which may influence neurodevelopmental processes. Numerous EEG markers have been proposed as indicators of attentional regulation, emotional functioning, and stress responsivity; however, their relative diagnostic and practical value remains unclear. The aim of the present study was to verify whether commonly reported EEG markers remain valid indicators of neurofunctional difficulties in children from alcohol-affected families, to establish their hierarchy of importance, and to determine how identified neurofunctional profiles may inform the sequencing of educational interventions and the development of individualized support plans used by educators and social workers. Methods: The study included children aged 6–10 years from alcohol-affected families (n = 20) and a control group from non-dysfunctional family environments (n = 25). Resting-state EEG recordings were conducted under eyes-open and eyes-closed conditions, with analyses focused on the eyes-open condition. Quantitative EEG (qEEG) indices included global, frontal, prefrontal, and midline Theta–Beta Ratio (TBR), frontal alpha asymmetry (FAA), temporal beta stress and parietal beta2 tension. EEG preprocessing was performed using EEGLAB and included artifact rejection, filtering, epoch segmentation, and spectral power analysis. Group differences were analyzed using Welch’s t-tests with Benjamini–Hochberg correction for multiple comparisons. Results: The analyzed EEG markers differed in their ability to distinguish children from alcohol-affected families and controls. The strongest effects were observed for Theta–Beta Ratio (TBR) measures, particularly in frontal and prefrontal regions, indicating impairments in attention regulation, executive functioning, and self-control. Elevated temporal beta stress and parietal beta2 tension reflected increased physiological arousal and chronic stress. In contrast, frontal alpha asymmetry (FAA), commonly associated with depressive emotional processing, was not significant after correction for multiple comparisons. The obtained findings enabled the establishment of a hierarchy of neurofunctional markers, with attentional and executive-function indicators demonstrating greater importance than markers related to depressive symptomatology. Conclusions: The EEG profile of children from alcohol-affected families is characterized primarily by chronic stress, heightened physiological activation, and impaired attention regulation rather than by neurophysiological patterns associated with depression. The results suggest that educational difficulties in this group may stem mainly from deficits in attention control, inhibitory processes, and cognitive flexibility. Consequently, educational interventions should prioritize learning strategies, attentional training, and self-regulated learning skills. The identified hierarchy of EEG markers may also support the development of individualized educational plans and social-support programs, including participation in structured extracurricular activities and interventions aimed at strengthening executive and learning-related competencies. However, given the pilot nature of the present study and the relatively small sample size, these findings should be considered preliminary. Replication in larger, more diverse, and independent cohorts is necessary to confirm the stability, reliability, and generalizability of the identified neurofunctional profile and the proposed hierarchy of qEEG markers before they can be recommended for broader educational and social applications. Full article

Background: Traditional diagnosis-based classifications often fail to capture neurocognitive heterogeneity among children with developmental disabilities (DD). Establishing function-based subtyping is essential for developing individualized education frameworks that move beyond categorical labels. Methods: This pilot study employed a data-driven clustering approach integrating neurophysiological and cognitive indices to identify functional subtypes in 18 school-aged children (8 typically developing; 10 with DD). Input features included EEG-derived theta/beta ratio (TBR) and cognitive variables from the CANTAB Multitasking Test (MTT). Ecological validity was evaluated using the Virtual Kitchen Errand Task for Children (VKET-C). Results: K-means clustering revealed three distinct groups. In terms of MTT performance, Cluster 1 exhibited high accuracy and short response latencies. Cluster 2 demonstrated a “Slow but Accurate” pattern, with prolonged reaction times irrespective of diagnosis. Cluster 3 presented a “Fast but Error-prone” profile, showing significantly higher TBR values and increased error rates, indicative of cognitive impulsivity. Notably, clusters did not align with diagnostic boundaries. The three identified clusters significantly differentiated commission errors on the VKET-C task and showed greater explanatory power for VR task performance than diagnosis-based classifications. Conclusions: Cluster-based classification better differentiated VR task performance, particularly commission errors, than traditional diagnosis-based grouping. Integrating diagnosis with neurocognitive deep phenotyping approaches may enable more individualized intervention and educational support for children. Full article

SARS-CoV-2 infection is associated with not only acute respiratory symptoms but is also characterized by strong neurotropism which may contribute to the development of the multisystem post-COVID syndrome (PASC). Patients frequently report chronic neurocognitive disorders such as brain fog, significant attention deficits and increased susceptibility to epileptiform discharges. The aim of this review is to systematize the knowledge regarding deviations in quantitative electroencephalography (QEEG) recordings in convalescents and to evaluate the utility of this method as an objective biomarker. This work constitutes a comprehensive literature review integrating the latest data on neuroinflammation, blood-brain barrier damage and changes in cortical oscillatory dynamics induced by the infection. The literature analysis indicates that the virus may induce a pathological excitation and inhibition imbalance (E/I imbalance) in neuronal networks. In QEEG studies this manifests as excessive activity of slow bands (Theta, Delta), a deficit of rhythms responsible for attention and sensorimotor integration (SMR) and a pathologically elevated Theta to Beta ratio (TBR). In conclusion, QEEG can serve as an objective and highly sensitive tool supporting the diagnosis and stratification of patients with neurocognitive complications of Long COVID. The integration of precise electrophysiological phenotyping with targeted behavioral neuromodulation (e.g., EEG-Biofeedback) fits into the paradigm of personalized medicine and offers a prospective strategy for mitigating long-term neurological burdens. Full article

Music therapy (MT) is increasingly being integrated into intensive care unit (ICU) settings to modulate pain, stress, and emotional dysregulation. Although clinically promising, objective biomarkers for quantifying its neurophysiological effects are still missing. In this context, the electroencephalogram (EEG) represents a valid tool to assess cortical dynamics associated with cognitive–affective engagement elicited by MT. Our study aims to evaluate the role of electroencephalography as an objective tool for monitoring cortical responses to MT in the ICU. EEGs acquired from nine burn patients undergoing MT in the ICU were considered. Signals were preprocessed to improve the signal-to-noise ratio. Then, six frequency bands (delta, theta, alpha, beta, gamma, and sensorimotor rhythm) were extracted to compute band powers and derive 37 involvement indexes, which were statistically compared across three experimental phases: before, during, and after MT. Results demonstrate that involvement indexes effectively capture neurophysiological shifts induced by MT. Significant differences were observed in 22 indexes when comparing During-MT and Post-MT phases, with 2 indexes being statistically different also when comparing During-MT and Pre-MT phases; 5 indexes differed statistically when comparing Pre-MT and Post-MT phases. These results suggest a transient cortical engagement elicited during MT in ICU settings. Our findings align with previous research reporting EEG (and certain EEG-derived involvement indexes) sensitivity to capture music-induced cognitive and emotional modulation. This confirms electroencephalography potential to objectively reflect MT effects and support its integration in multidisciplinary burn care; however, analysis on larger cohorts is necessary to validate EEG as a clinical tool in MT. Full article

Background and Objectives: Substance use disorders (SUDs) are associated with maladaptive neuroplasticity and chronic dysregulation of cortical arousal. EEG provides a non-invasive tool for quantifying these neurophysiological alterations through spectral power and reactivity indices. Prior research consistently reports elevated beta and diminished alpha activity in SUD, reflecting cortical hyperarousal and reduced inhibitory control. This study sought to identify EEG-based markers of dependence-related neurophysiological alterations by integrating rule-based and score-based models incorporating the theta/beta ratio (TBR), alpha and beta powers, the hyperarousal index, and alpha-blocking measures. Materials and Methods: EEG recordings from 47 individuals with SUD were systematically analyzed, focusing on frontal and central cortical regions. Spectral parameters were derived using power spectral density estimation, and composite indices were computed via Python-based signal analysis. A rule-based Dependence Likelihood variable and a continuous Dependence Score (0–1 scale) classified cases as dependence-related (≥0.7), borderline (0.5–0.7), or normal (<0.5). Results: Low alpha power and an elevated hyperarousal index (mean = 3.45) characterized most participants. Dependence-related EEG profiles were identified in 87.2% of cases (mean score = 0.86). Alpha blocking remained intact in 46.8% of cases, whereas post-hyperventilation recovery was attenuated in 61.7% of cases. Segmental analysis indicated sustained cortical activation with low TBR (0.37) and elevated beta across all conditions. Conclusions: Quantitative EEG analysis revealed consistent hyperarousal and inhibitory deficits in SUD. The combined Dependence Likelihood and Score framework provides an interpretable, reproducible approach for identifying dependence-related EEG signatures and holds promise as a biomarker in addiction neurophysiology. Full article

Objective: This study examined whether changes in electroencephalography (EEG)-derived indices, photoplethysmography (PPG)-derived autonomic nervous system indices, heart rate, and rating of perceived exertion (RPE) post-high-intensity exercise differ depending on sleep duration. Methods: Forty physically healthy female university students in their twenties were randomly assigned to the sleep restriction (SR) or normal sleep (NS) group. EEG-derived indices—the theta-to-beta ratio (TBR) and spectral edge frequency at 90% (SEF-90)—and PPG-derived autonomic nervous system indices (HRV index, sympathetic activity, and parasympathetic activity) were measured for one minute at rest before exercise and for one minute immediately after exercise. Heart rate was assessed at rest, immediately after exercise, and at 5, 10, and 15 min post-exercise. The group × time interaction effects were assessed using two-way mixed-design analysis of variance, followed by post hoc analyses. Results: TBR increased significantly post-exercise in the SR group (p = 0.002) with no significant change in the NS group. SEF-90 decreased significantly in the SR group (p < 0.001) with no significant change in the NS group. The HRV index decreased significantly in the SR group (p = 0.004) with no significant change in the NS group. Sympathetic activity increased and parasympathetic activity decreased significantly in the SR group (both p < 0.001). Heart rate was significantly higher in the SR group at rest (p < 0.001), immediately after exercise (p = 0.020), and 5 min post-exercise (p = 0.009). RPE was significantly higher in the SR group (p = 0.003). Conclusions: In healthy young adult women, the central and autonomic nervous systems respond differently to high-intensity exercise depending on sleep duration. Full article

Introduction: Resting-state EEG (rsEEG) is a scalable window onto trait-like “executive readiness,” but findings have been fragmented by task impurity on the executive-function (EF) side and heterogeneous EEG pipelines. This review synthesizes rsEEG features that reliably track EF in healthy samples across development and aging and evaluates moderators such as cognitive reserve. Materials and methods: Following PRISMA 2020, we defined PECOS-based eligibility (human participants; eyes-closed/eyes-open rsEEG; spectral, aperiodic, connectivity, topology, microstate, and LRTC features; behavioral EF outcomes) and searched MEDLINE/PubMed, Embase, PsycINFO, Web of Science, Scopus, and IEEE Xplore from inception to 30 August 2025. Two reviewers were screened/double-extracted; the risk of bias in non-randomized studies was assessed using the ROBINS-I tool. Sixty-three studies met criteria (plus citation tracking), spanning from childhood to old age. Results: Across domains, tempo, noise, and wiring jointly explained EF differences. Faster individual/peak alpha frequency (IAF/PAF) related most consistently to manipulation-heavy working may and interference control/vigilance in aging; alpha power was less informative once periodic and aperiodic components were separated. Aperiodic 1/f parameters (slope/offset) indexed domain-general efficiency (processing speed, executive composites) with education-dependent sign flips in later life. Connectivity/topology outperformed local power: efficient, small-world-like alpha networks predicted faster, more consistent decisions and higher WM accuracy, whereas globally heightened alpha/gamma synchrony—and rigid high-beta organization—were behaviorally sluggish. Within-frontal beta/gamma coherence supported span maintenance/sequencing, but excessive fronto-posterior theta coherence selectively undermined WM manipulation/updating. A higher frontal theta/beta ratio forecasts riskier, less adaptive choices and poorer reversal learning for decision policy. Age and reserve consistently moderated effects (e.g., child frontal theta supportive for WM; older-adult slow power often detrimental; stronger EO ↔ EC connectivity modulation and faster alpha with higher reserve). Boundary conditions were common: low-load tasks and homogeneous young samples usually yielded nulls. Conclusions: RsEEG does not diagnose EF independently; single-band metrics or simple ratios lack specificity and can be confounded by age/reserve. Instead, a multi-feature signature—faster alpha pace, steeper 1/f slope with appropriate offset, efficient/flexible alpha-band topology with limited global over-synchrony (especially avoiding long-range theta lock), and supportive within-frontal fast-band coherence—best captures individual differences in executive speed, interference control, stability, and WM manipulation. For reproducible applications, recordings should include ≥5–6 min eyes-closed (plus eyes-open), ≥32 channels, vigilant artifact/drowsiness control, periodic–aperiodic decomposition, lag-insensitive connectivity, and graph metrics; analyses must separate speed from accuracy and distinguish WM maintenance vs. manipulation. Clinical translation should prioritize stratification and monitoring (not diagnosis), interpreted through the lenses of development, aging, and cognitive reserve. Full article

Background: Pain management for preterm infants has emerged as a key intervention aimed at enhancing their developmental trajectories. However, little is known regarding the response and recovery of the neonatal brain following procedural pain. This study examined the temporal dynamics of electroencephalography (EEG) power in preterm infants during and up to 30 min after procedural pain. Methods: fifty-seven datasets were collected from preterm infants (mean gestational age 32.5 ± 3.3 weeks). We computed Time–Frequency analysis for EEG power and EEG power ratio relative to baseline across eight EEG channels in the low delta (1–2 Hz), high delta (2–4 Hz), theta (4–8 Hz), alpha (8–16 Hz), and beta (16–20 Hz) during the procedure, immediately after, and at intervals up to 30 min post-procedure. Results: EEG power increased significantly in all channels and frequency bands during the procedure compared to baseline (p < 0.05), declined immediately after but remained above baseline (p < 0.05), and recovered to near-baseline levels by four minutes post-procedure (p > 0.05), except for alpha and beta power at C3 and C4, which were lower than baseline (p < 0.05). The EEG power ratio at the frontal, occipital, and temporal showed the greatest power changes in the beta. The C3 and C4 exhibited the most prominent relative changes in the low delta. Conclusion: the preterm brain exhibits widespread responses to procedural pain and recovers gradually, not returning to the resting state for at least four minutes after a painful procedure. These results underscore the potential benefit of quantifying the time-integral of EEG power, rather than its peak intensity, when developing a biosensor for procedural pain using neonatal EEG. Full article

Background/Objectives: Post-traumatic epileptogenesis is a frequent and clinically relevant consequence of traumatic brain injury (TBI), often contributing to worsened neurological and functional outcomes. In patients experiencing early post-injury seizures, rehabilitative strategies that support recovery while considering increased epileptogenic risk are needed. This case study explores the potential benefits of combining neurofeedback (NFB) with motor therapy on cognitive and motor recovery. Methods: A patient hospitalized for severe TBI who experienced an acute symptomatic seizure in the early post-injury phase underwent baseline quantitative EEG (qEEG), neuromotor, functional, and neuropsychological assessments. The patient then completed a three-week rehabilitation program (five days/week) including 30 sensorimotor rhythm (SMR) NFB sessions (35 min each) combined with daily one-hour motor therapy. qEEG and clinical assessments were repeated post-intervention and at 6-month follow-up. Results: Post-intervention qEEG showed significant reductions in Delta and Theta power, reflecting decreased cortical slowing and enhanced neural activation. Relative power analysis indicated reduced Theta activity and Alpha normalization, suggesting improved cortical stability. Increases were observed in Beta and High-beta activity, alongside significant reductions in the Theta/Beta ratio, consistent with improved attentional regulation. Neuropsychological outcomes revealed reliable improvements in global cognition, memory, and visuospatial abilities, mostly maintained or enhanced at follow-up. Depressive and anxiety symptoms decreased markedly. Motor and functional assessments demonstrated meaningful improvements in motor performance, coordination, and functional independence. Conclusions: Findings suggest that integrating NFB with motor therapy may support recovery processes and be associated with sustained neuroplastic changes in the early post-injury phase after TBI, a condition associated with elevated risk for post-traumatic epilepsy. Full article

Background/Objectives: Psychiatric conditions are highly prevalent and among the leading causes of disability worldwide. Comorbidities are common in psychiatric patients but are not adequately addressed in diagnostic manuals such as the DSM-5. Understanding the impact of comorbidities on patients’ symptoms and brain activity could improve the personalization of therapeutic approaches, leading to better outcomes. Given the complexity of this task, a feasible strategy is to examine how comorbidities affect brain activity and a condition commonly observed in psychiatric patients, such as cognitive impairment. Methods: In this study, we assessed impulsiveness, working memory performance, and theta/beta ratio in controls and in subjects exhibiting symptoms of depression, ADHD, and suicide risk. Participants differed in the presence of alcohol use disorders, in addition to the aforementioned symptoms, either presenting no alcohol use disorder (DAS), hazardous alcohol consumption (DAS-H), or risk of alcohol dependence (DAS-D). Results: All three comorbid groups (DAS, DAS-H, DAS-D) showed increased impulsiveness compared with controls, while the DAS-D group also exhibited higher motor impulsiveness than both the DAS and DAS-H groups. A widespread increase in theta/beta ratio was observed only in the DAS group. Conclusions: These results indicate that comorbid alcohol use disorders modulate motor impulsiveness and theta/beta ratio in subjects with symptoms of depression, ADHD, and suicide risk. The findings underscore the importance of considering comorbidities when personalizing treatment strategies for psychiatric patients. Full article

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles. Full article

Background/Objective: Timely and successful treatments for Alzheimer’s disease (AD) depend on early detection. The Multimer Detection System (MDS-OAβ) for quantifying plasma oligomeric amyloid-β (OAβ) has shown promise as a biomarker of amyloid disease. The theta-to-alpha ratio (TAR) and theta-to-beta ratio (TBR) are two examples of spectral power metrics that can be used in resting-state quantitative EEG (qEEG) to evaluate brain function non-invasively. This study used resting-state EEG (rEEG) recordings obtained while the subjects were both eyes-open (EO) and eyes-closed (EC) to investigate the relationship between regional qEEG power ratios and plasma MDS-OAβ levels in older adults. Methods: The analysis comprised 174 patients between the ages of 60 and 85, with 2 in the low-MDS-OAβ group and 82 in the high-MDS-OAβ group. The clinical plasma cutoff was 0.78 ng/mL. All participants underwent rEEG recordings and plasma OAβ quantification. EEG pre-processing included bandpass filtering (0.5–100 Hz), average re-referencing, artifact rejection using independent component analysis (ICA), and spectral power estimation using Welch’s method. The TAR and TBR were calculated across five lobar regions (frontal, central, parietal, occipital, and temporal) during both EO and EC conditions. To normalize data distributions, EEG ratio variables were log-transformed prior to statistical analysis. Group comparisons and linear regression analyses were conducted to evaluate the associations between EEG power ratios and MDS-OAβ levels. Adjusted regression models included age, years of education, and neuropsychological test scores as covariates. Statistical significance was set at p < 0.05. Results: No significant associations were found between TAR and plasma MDS-OAβ levels across any lobar regions under either EO or EC conditions. In contrast, TBR exhibited consistent and significant negative associations with MDS-OAβ levels, particularly under EC conditions. Adjusted regression models revealed that higher MDS-OAβ levels were associated with lower TBR values in the central (β = −0.059, p = 0.015), parietal (β = −0.072, p = 0.006), occipital (β = −0.067, p = 0.040), and temporal (β = −0.053, p = 0.018) lobes, with the strongest inverse relationship observed in the parietal lobe. A similar, though slightly weaker, pattern was observed during EO conditions, with significant inverse associations in the frontal, central, and temporal lobes. Conclusions: Our findings indicate that, after adjusting for covariates, increased plasma MDS-OAβ levels are significantly associated with a reduced TBR, particularly in the parietal and central lobes, under both EO and EC resting-state conditions. In contrast, no significant associations were observed with TAR. These results suggest that a lower TBR may reflect an increased peripheral amyloid burden and highlight its potential as a sensitive qEEG biomarker for early amyloid-related brain changes in older adults. Full article

Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8–13 Hz) reliably indexes emotional valence with 75–85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal–midline theta (4–8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85–98% accuracy for subject identification and 70–95% for state classification. However, significant challenges persist: spatial resolution remains limited (2–3 cm), inter-individual variability is substantial (alpha peak frequency: 7–14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective–cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain–computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration. Full article