Search Results (91)

This study presents a sensor-based condition monitoring framework for the diagnosis and prognosis of neurophysiological states using electroencephalographic (EEG) signals. Leveraging a comparative deep learning architecture, we evaluate a baseline Variational Autoencoder against a Transformer-based Autoencoder to model latent representations of EEG dynamics across three therapeutic phases: pre-intervention, during intervention, and post-intervention. The proposed methodology aligns with sensor-based fault diagnosis principles by treating deviations from stable neurophysiological states as diagnostic indicators and temporal phase transitions as markers of therapeutic stage progression. Using a dataset of 94 EEG sessions from six subjects with diverse neurological conditions, we demonstrate that the Transformer Autoencoder, through its self-attention mechanism, captures cross-band spectral relationships more effectively than the VAE, resulting in denser within-phase clusters and improved separation between therapeutic stages. Quantitative evaluation reveals small but statistically significant effects between pre- and during-intervention phases (${\eta }_{\mathrm{partial}}^2=0.0388$) and pre- and post-intervention phases (${\eta }_{\mathrm{partial}}^2=0.0470$), predominantly driven by delta, theta, beta, and gamma rhythms. These findings illustrate how sensor-based latent state monitoring can provide interpretable, data-driven insights for condition assessment and phase transition assessment between sessions in complex dynamic systems, with potential applicability beyond clinical domains to industrial condition monitoring and fault diagnosis tasks. The framework confirms that it offers qualitative indicators, rather than predictive clinical outputs. Full article

Background: Rhythmic knowledge enables the precise timing of actions in dynamic environments. Although rhythm learning has been extensively studied, it remains debated whether such learning arises primarily from the perceptual encoding of rhythmic inputs or from the repetitive execution of periodic actions. Methods: To address this question, we developed a temporal-rhythm serial reaction time (TR-SRT) paradigm that dissociates rhythmic structures in perceptual inputs from the timing of motor responses. Across three experiments, participants learned rhythms under visuomotor (Experiment 1, N = 27), visual-only (Experiment 2, N = 26), or motor-only (Experiment 3, N = 26) conditions while electroencephalography was recorded. Results: Behavioral learning slopes revealed robust rhythm learning in both the visuomotor and visual-only conditions, whereas no learning emerged when rhythmic structure was confined to motor timing alone. Post-learning awareness tests further indicated that the acquired rhythmic knowledge was predominantly implicit. Consistently, global (whole-brain) theta-band magnitude (4.8–5.2 Hz) was enhanced only in the conditions that supported rhythm learning. Conclusions: These findings indicate that rhythm learning depends primarily on perceptual temporal structure rather than the repetition of rhythmic actions and identify increased global theta oscillations as a neural signature of this perceptually driven and largely implicit learning process. 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/Objectives: Advanced childhood cerebral X-linked adrenoleukodystrophy (cALD) is traditionally regarded as an irreversible terminal phase of neurodegeneration driven by inflammatory demyelination and axonal loss. Experimental evidence indicates that endogenous bioelectrical fields regulate central nervous system organisation, raising the possibility that functional network collapse in cALD may be biologically modifiable, even in the presence of persistent structural damage. This study examined whether longitudinal modulation of endogenous bioelectrical network organisation is associated with sustained clinical and neurophysiological stabilisation in advanced cALD. Methods: We performed a longitudinal observational analysis of two paediatric patients with advanced childhood cerebral X-linked adrenoleukodystrophy undergoing repeated neuroregenerative treatment cycles. Standardised scalp electroencephalography was recorded during spontaneous wakefulness and repeated over months under comparable vigilance conditions. Multimodal analysis included conventional EEG, quantitative EEG, independent component analysis, and standardised low-resolution electromagnetic tomography (sLORETA). Clinical function was assessed using validated measures of consciousness, swallowing, and voluntary motor behaviour. Results: Across patients, longitudinal recordings demonstrated sustained stabilisation of consciousness, swallowing, and voluntary motor function, accompanied by reproducible reorganisation of pathological brain rhythms. Delta and theta oscillations showed a consistent topographical redistribution from limbic–frontoinsular networks towards sensorimotor and parietal integrative cortices. These changes were observed across modalities and timepoints and are unlikely to reflect spontaneous fluctuation, delayed effects of haematopoietic stem cell transplantation, or state-dependent EEG variation. Conclusions: Advanced childhood cerebral X-linked adrenoleukodystrophy is associated with disorganisation of endogenous bioelectrical network activity. In this longitudinal analysis, large-scale network reorganisation was temporally associated with sustained clinical stabilisation, supporting a view of late-stage cALD as a dynamic disorder of network-level vulnerability, rather than a fixed terminal state. Full article

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders where early detection is vital. However, the need for long-term monitoring is incompatible with data-scarce settings, and methods trained on one subject often fail on another due to cross-subject variability. To address these limitations, this study proposes a cross-subject, single-channel electroencephalography (EEG)-based method that uses Multi-Entropy Feature Concatenation (MEFC) to classify AD and FTD. First, single-channel EEG is processed through the Discrete Wavelet Transform (DWT) to extract five rhythms: delta, theta, alpha, beta, and gamma. Subsequently, Permutation Entropy (PE), Singular Spectrum Entropy (SSE), and Sample Entropy (SE) are calculated for each rhythm and concatenated to form a combined MEFC to characterize the non-linear dynamic properties of EEG. Lastly, Dynamic Time Warping (DTW), Pearson Correlation Coefficient (PCC), Wavelet Coherence (WC), and Hilbert Transform Correlation (HTC) are employed to measure the similarity between unknown rhythmic MEFC and those from AD, FTD, and Healthy Control (HC) groups, performing a data-driven classification via similarity measurement. Experimental results on 88 subjects in the AHEPA dataset demonstrate that the beta-rhythm with PCC yields a three-class accuracy of 76.14% using single-channel FP2. In another dataset, the Florida-Based dataset, involving 48 subjects, theta-rhythm with WC achieves a two-class accuracy of 83.33% using FP2. Furthermore, a MATLAB R2023b-based toolbox is developed using the proposed method. Such outcomes are impressive, given the limited data per individual (data-efficient), reliable performance across new subjects (cross-subject), and compatibility with wearable devices (single-channel), providing a novel entropy-based approach for EEG-based applications in biomedical engineering. 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

Functional constipation (FC) is a common functional gastrointestinal disorder thought to arise from the brain–gut axis dysfunction, yet direct human neurophysiological evidence is lacking. We recorded high-density electroencephalography (EEG) data in 21 FC patients and 37 healthy controls across resting, cognitive, and defecation-related tasks. We observed that FC patients displayed a consistent, task-dependent signature compared with healthy controls. At the regional level, FC patients exhibited increased alpha during both resting and defecation-related tasks, reduced temporal gamma during defecation-related tasks, as well as elevated temporal theta during the cognitive task. At the global level, we found altered network properties, such as global efficiency in the delta and beta band networks during resting and defecation-related tasks. These findings establish a direct neurophysiological link between specific, condition-dependent perturbations in cortical rhythm activity and FC pathophysiology. Our work implicates the brain–gut axis in symptom generation and opens a path toward EEG-based biomarkers and targeted neuromodulatory therapies. Full article

The cannabinoid 1-receptor (CB1R) is found in particularly high levels in the hippocampus (HPC). Increased CB1R density and binding are observed in patients with schizophrenia, and epidemiological studies suggest that regular cannabis use during adolescence is a risk factor for the disease. CB1R was shown to interfere with neuronal network oscillations and to impair sensory gating and memory function. Neuronal oscillations are essential in multiple cognitive functions, and their impairment was documented in neurological and psychiatric diseases. The aim of this study was to investigate how adolescent pre-treatment with the CB1R-selective agonist CP-55940 may lead to abnormalities in theta synchronization in adulthood. Rats were pre-treated with CP-55940 or vehicle during adolescence (daily injections in PND 32–36 or PND 42–46). They were then tested in adulthood (PND over 70) under urethane anesthesia. Hippocampal theta rhythm was elicited by brainstem stimulation at five intensity levels 1 hour before and up to 5 h after injection. We found a significant decrease in elicited theta power after CP-55940 in adult rats, which was aggravated further in rats pre-treated in adolescence with the CB1R agonist. The effect was significantly larger in rats pre-treated during early adolescence (PND 32–36) compared to the group pre-treated during late adolescence (PND 42–46). We conclude that (1) exposure to cannabis during adolescence leads to increased sensitivity to CB1R agonist in adulthood, and (2) early adolescence, a critical period for development of HPC networks generating theta rhythms, is particularly prone to this sensitivity. 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/Objectives: Excessive smartphone use may negatively affect cognitive functions, including attention. While sensorimotor rhythm, beta, and theta waves have been linked to concentration, the electroencephalography (EEG) frequency band that most reliably serves as a neurophysiological marker of concentration is unclear. Therefore, we aimed to evaluate the effects of glucose tablet candy ingestion on attention following smartphone use in healthy adults. Methods: A randomized, double-blind, placebo-controlled crossover trial was conducted in 16 healthy adults aged 18–39 years. Participants performed a 30 min smartphone-based information search task. Attention was assessed before and after the task using the Cognitrax test battery, and participants ingested either a glucose tablet candy (containing 26 g of glucose) or a placebo (no glucose) between tests. EEG was performed during attention tests using a patch-type device. Subjective sensations, including attention, fatigue, and mental clarity (clear-headedness), were evaluated using a visual analog scale (VAS). The primary outcome was attention test scores, and secondary outcomes included EEG power and VAS ratings. Results: Glucose tablet candy ingestion after smartphone use significantly improved mean correct response time and error response scores in part 2 of the four-part continuous performance test, a subtest within Cognitrax, compared to that with the placebo. Additionally, glucose intake significantly attenuated the decrease in right prefrontal beta EEG power observed with the placebo. Improvements were also observed in self-reported physical fatigue and mental clarity on the VAS following glucose ingestion. Conclusions: The ingestion of the glucose (26 g) tablet candy improved sustained attention after smartphone use in healthy adults aged 18–39 years and was associated with changes in brain activity. These results suggest that the glucose tablet candy may help counteract the decline in concentration following cognitively demanding smartphone use. Full article

Background: Studies have shown that input comparison in the hippocampus between the Schaffer collateral (SC) input in apical dendrites and the perforant path (PP) input in the apical tufts dramatically changes the activity of pyramidal cells (PCs). Equally, dendritic inhibition was shown to control PC activity by minimizing the depolarizing signals in their dendritic trees, controlling the synaptic integration time window, and ensuring temporal firing precision. Objectives: We computationally investigated the diverse roles of inhibitory synapses on the PC dendritic arbors of a CA1 microcircuit model in mnemonic retrieval during the co-occurrence of SC and PP inputs. Results: Our study showed inhibition in the apical PC dendrites mediated thresholding of firing during memory retrieval by restricting the depolarizing signals in the dendrites of non-engram cells, thus preventing them from firing, and ensuring perfect memory retrieval (only engram cells fire). On the other hand, inhibition in the apical dendritic tuft removed interference from spurious EC during recall. When EC drove only the engram cells of the SC input cue, recall was perfect under all conditions. Removal of apical tuft inhibition had no effect on recall quality. When EC drove 40% of engram cells and 60% of non-engram cells of the SC input cue, recall was disrupted, and this disruption was worse when the apical tuft inhibition was removed. When EC drove only the non-engram cells of the cue, then recall was perfect again but only when the population of engram cells was small. Removal of the apical tuft inhibition disrupted recall performance when the population of engram cells was large. Conclusions: Our study deciphers the diverse roles of dendritic inhibition in mnemonic processing in the CA1 microcircuit of the rat hippocampus. Full article

Introduction: Learning disabilities (LD) compromise academic achievement in approximately 5–10% of school-aged children, yet the neurophysiological signatures that could facilitate earlier detection or stratification remain poorly defined. Resting-state electroencephalography (rs-EEG) offers millisecond resolution and is cost-effective, but its findings have never been synthesized systematically across pediatric LD cohorts. Methods: Following a PROSPERO-registered protocol (CRD420251087821) and adhering to PRISMA 2020 guidelines, we searched PubMed, Embase, Web of Science, Scopus, and PsycINFO through 31 March 2025 for peer-reviewed studies that recorded eyes-open or eyes-closed rs-EEG using ≥ 4 scalp electrodes in children (≤18 years) formally diagnosed with LD, and compared the results with typically developing peers or normative databases. Four reviewers independently screened titles and abstracts, extracted data, and assessed the risk of bias using ROBINS-I. Results: Seventeen studies (704 children with LD; 620 controls) met the inclusion criteria. The overall risk of bias was moderate, primarily due to small clinic-based samples and inconsistent control for confounding variables. Three consistent electrophysiological patterns emerged: (i) a 20–60% increase in delta/theta power over mesial-frontal, fronto-central and left peri-Sylvian cortices, resulting in markedly elevated θ/α and θ/β ratios; (ii) blunting or anterior displacement of the posterior alpha rhythm, particularly in language-critical temporo-parietal regions; and (iii) developmentally immature connectivity, characterized by widespread slow-band hypercoherence alongside hypo-connected upper-alpha networks linking left-hemisphere language hubs to posterior sensory areas. These abnormalities were correlated with reading, writing, and IQ scores and, in two longitudinal cohorts, they partially normalized in parallel with academic improvement. Furthermore, a link between reduced posterior/overall alpha and neuroinflammation has been found. Conclusions: Rs-EEG reveals a robust yet heterogeneous electrophysiological profile of pediatric LD, supporting a hybrid model that combines maturational delay with persistent circuit-level atypicalities in some children. While current evidence suggests that rs-EEG features show promise as potential biomarkers for LD detection and subtyping, these findings remain preliminary. Definitive clinical translation will require multi-site, dense-array longitudinal studies employing harmonized pipelines, integration with MRI and genetics, and the inclusion of EEG metrics in intervention trials. Full article

Background: Digital technologies offer innovative opportunities for recovering and maintaining intellectual and mental health. The use of a multitask approach that combines motor component with various cognitive tasks in a virtual environment can optimize cognitive and physical functions and improve the quality of life of cardiac surgery patients. This study aimed to localize current sources of theta and alpha power in patients who have undergone virtual multitask training (VMT) and a control group in the early postoperative period of coronary artery bypass grafting (CABG). Methods: A total of 100 male CABG patients (mean age, 62.7 ± 7.62 years) were allocated to the VMT group (n = 50) or to the control group (n = 50). EEG was recorded in the eyes-closed resting state at baseline (2–3 days before CABG) and after VMT course or approximately 11–12 days after CABG (the control group). Power EEG analysis was conducted and frequency-domain standardized low-resolution tomography (sLORETA) was used to assess the effect of VMT on brain activity. Results: After VMT, patients demonstrated a significantly higher density of alpha-rhythm (7–9 Hz) current sources (t > −4.18; p < 0.026) in Brodmann area 30, parahippocampal, and limbic system structures compared to preoperative data. In contrast, the control group had a marked elevation in the density of theta-rhythm (3–5 Hz) current sources (t > −3.98; p < 0.017) in parieto-occipital areas in comparison to preoperative values. Conclusions: Virtual reality-based multitask training stimulated brain regions associated with spatial orientation and memory encoding. The findings of this study highlight the importance of neural mechanisms underlying the effectiveness of multitask interventions and will be useful for designing and conducting future studies involving VR multitask training. Full article

(1) Background: Neurofeedback (NFB) and biofeedback (BFB) have been shown to reduce stress, enhance physiological self-regulation, improve cognitive performance, and accelerate response times. Stimulating the sensorimotor rhythm (12–15 Hz) is particularly effective in improving working memory and selective attention. However, most studies on air force pilots focus on addressing post-traumatic stress disorder rather than investigating how these interventions might enhance performance and safety during flights, as explored in the present study. (2) Methods: Twelve Spanish Air Force fighter pilot trainees (mean age = 22.83 (0.94) years) participated in the study. Six pilots underwent 24 sessions of combined NFB and BFB training (experimental group), while six served as controls. (3) Results: The experimental group demonstrated improved heart rate variability during baseline, alarm sounds, math tasks, and real flights, which is indicative of greater parasympathetic modulation. A significant decrease in the Theta/SMR ratio was observed in the experimental group during the same conditions, suggesting improved focus, with lower values than the control group. Cognitive performance improved in the experimental group, with higher accuracy and a greater number of completed operations during math tasks. Regarding dual-task performance, the experimental group showed lower reaction time and a better ratio taps/reaction post-intervention. Psychological benefits included reduced cognitive, somatic, and state anxiety levels, along with increased self-confidence. (4) Conclusions: Neurofeedback and biofeedback training, integrated with real flights, simulators, and virtual reality, can enhance physiological regulation, cognitive performance, and emotional resilience, contributing to improved performance and safety in air force pilots. Full article