EEG Variables Research Articles

Validating brain activity measures as reliable indicators of individual diagnostic group and genetically mediated sub-group membership in Fragile X Syndrome

Recent failures translating preclinical behavioral treatment effects to positive clinical trial results in humans with Fragile X Syndrome (FXS) support refocusing attention on biological pathways and associated measures, such as electroencephalography (EEG), with strong translational potential and small molecule target engagement. This study utilized guided machine learning to test promising translational EEG measures (resting power and auditory chirp oscillatory variables) in a large heterogeneous sample of individuals with FXS to identify best performing EEG variables for reliably separating individuals with FXS, and genetically-mediated subgroups within FXS, from typically developing controls. Best performing variables included resting relative frontal theta power, all combined posterior-head resting power bands, posterior peak alpha frequency (PAF), combined PAF across all measured regions, combined theta, alpha, and gamma power during the chirp, and all combined chirp oscillatory variables. Sub-group analyses for resting EEG best discriminated non-mosaic FXS males via frontal theta resting relative power (AUC = 0.8759), even with data reduced to a 20-channel clinical montage (AUC = 0.9062). In the chirp task, FXS females and non-mosaic males were nearly perfectly discriminated by combined theta, alpha, and gamma power (AUC = 0.9444) and a combination of all variables (AUC = 0.9610), respectively. Results support use of resting and auditory oscillatory tasks to reliably identify neural deficit in FXS, and to identify specific translational targets for genetically-mediated sub-groups, supporting potential points for stratification.

Neuro-cognitive Insights into Engineering Design: Exploring EEG Predictive Associations with Task Performance

Abstract This paper investigates the relationship between brain activity, measured by Electroencephalography (EEG) data, and the performance assessment result of engineering design activities involving different cognitive processes. Employing a novel signal processing pipeline, we analyzed EEG variations of 37 subjects during two design tasks that mostly leverage, respectively, convergent and divergent thinking: the Design with Morphological Table (DwMT) task and the Problem-Solving (PS) task. The EEG recordings underwent meticulous artifact removal, allowing for a comprehensive investigation into the statistical relationships between frequency bands, channels, and design outcome performance metrics. The developed models linking better design outcomes with brain (de)synchronization demonstrated remarkable accuracy, precision, and recall across performance metrics for both tasks. Notably, the EEG data in theta band measured from the frontal area at both hemispheres and a left parietal/occipital channel were essential for estimating better design performance with brain desynchronization. On the contrary, the model based on brain synchronization produces precise estimations of design performance with alpha band and channels in temporal and parietal areas. These findings highlight EEG variation as a viable proxy for design performance, paving the way for more effective performance prediction models with fewer sensors. Overall, this research contributes to the emerging field of neurocognitive design assessment and underscores the potential for EEG-based predictions in engineering design tasks.

Darts fast-learning reduces theta power but is not affected by Hf-tRNS: A behavioral and electrophysiological investigation

Sports trainers have recently shown increasing interest in innovative methods, including transcranial electric stimulation, to enhance motor performance and boost the acquisition of new skills during training. However, studies on the effectiveness of these tools on fast visuomotor learning and brain activity are still limited. In this randomized single-blind, sham-controlled, between-subjects study, we investigated whether a single training session, either coupled or not with 2 mA online high-frequency transcranial random noise stimulation (hf-tRNS) over the bilateral primary motor cortex (M1), would affect dart-throwing performance (i.e., radial error, arm range of motion, and movement variability) in 37 healthy volunteers. In addition, potential neurophysiological correlates were monitored before and after the training through a 32-electrode portable electroencephalogram (EEG). Results revealed that a single training session improved radial error and arm range of motion during the dart-throwing task, but not movement variability. Furthermore, after the training, resting state-EEG data showed a decrease in theta power. Radial error, arm movement, and EEG were not further modulated by hf-tRNS. This indicates that a single training session, regardless of hf-tRNS administration, improves dart-throwing precision and movement accuracy. However, it does not improve movement variability, which might require multiple training sessions (expertise resulting in slow learning). Theta power decrease could describe a more efficient use of cognitive resources (i.e., attention and visuomotor skills) due to the fast dart-throwing learning. Further research could explore different sports by applying longer stimulation protocols and evaluating other EEG variables to enhance our understanding of the lasting impacts of multi-session hf-tRNS on the sensorimotor cortex within the framework of slow learning and training assistance.

Exploring gender differences in acoustic-thermal comfort and performance in a simulated working environment

This study investigated gender differences in acoustic-thermal comfort and cognitive performance in three workload levels in eight simulated work environments with two noise levels (55 and 75 dBA) and four air temperatures (18, 22, 26, and 30 °C). Subjective perceptions, including noise annoyance and fatigue, physiological measurements like Heart Rate Variability (HRV), respiration rate, and EEG variables (ALPHA, BETA, THETA) were used to assess comfort. In addition, the cognitive performance of men and women was evaluated on three levels: simple, medium, and complex. Women generally reported higher annoyance levels than men, especially at 75 dB noise level and increasing temperatures. Women experienced higher fatigue in warmer conditions than men. Women exhibited higher accuracy and lower reaction time than men at 18 °C and 22 °C with a quiet noise level (55 dBA) and simple and medium tasks. However, women acted accurately in complex tasks but had a higher reaction time. On the contrary, as the tasks are complicated and air temperature (26 °C and 30 °C) increases, men have higher accuracy and further reaction time than women. Accuracy and reaction time were diminished in both genders at 75 dBA noise level. Significant changes in the physiological parameters were observed at 18–22 °C for men and at 26–30 °C for women with the same noise level. Women had higher alpha and beta waves at 18–22 °C, indicating greater comfort and alertness, but these values decreased as air temperature rose. Overall, the study emphasizes the importance of considering gender differences when designing indoor work environments for optimal comfort and performance.

Towards predicting PTSD symptom severity using portable EEG-derived biomarkers.

Posttraumatic Stress Disorder (PTSD) is a heterogeneous mental health disorder that occurs following traumatic experience. Understanding its neurobiological basis is crucial to advance early diagnosis and treatment. Electroencephalography (EEG) can be used to explore the neurobiological basis of PTSD. However, only limited research has explored mobile EEG, which is important for scalability. This proof-of-concept study delves into mobile EEG-derived biomarkers for PTSD and their potential implications. Over four weeks, we measured PTSD symptoms using the PTSD checklist for DSM-5 (PCL-5) at multiple timepoints, and we recorded multiple EEG sessions from 21 individuals using a mobile EEG device. In total, we captured 38 EEG sessions, each comprising two recordings that lasted approximately 180 seconds, to evaluate reproducibility. Next, we extracted Shannon entropy, as a measure of the randomness or unpredictability of the signal and spectral power for the fronto-temporal regions of interest, including electrodes at AF3, AF4, T7, and T8 for each EEG recording session. We calculated the partial correlation between the EEG variables and PCL-5 measured closest to the EEG session, using age, sex, and the grouping variable 'batch' as covariates. We observed a significant negative correlation between Shannon entropy in fronto-temporal regions and PCL-5 scores. Specifically, this association was evident in the AF3 (r = -0.456, FDR-corrected p = 0.01), AF4 (r = -0.362, FDR-corrected p = 0.04), and T7 (r = -0.472, FDR-corrected p = 0.01) regions. Additionally, we found a significant negative association between the alpha power estimated from AF4 and PCL-5 (r=-0.429, FDR-corrected p=0.04). Our findings suggest that EEG data acquired using a mobile EEG device is associated with PTSD symptom severity, offering valuable insights into the neurobiological mechanisms underlying PTSD.

Repetitive active and passive cognitive stimulations induce EEG changes in patients with Rett syndrome.

Despite being considered a rare disease, Rett syndrome is a leading cause of profound cognitive impairment in females. This study explores game-based cognitive stimulation to enhance attention during learning tasks, offering an alternative treatment perspective. Fifteen diagnosed Rett syndrome girls participated in four 24-minute sessions, including a 5-minute initial resting state recording. Primary indicators for analysis included relative power and spectral entropy. Significant findings indicated variations among conditions (resting state, active task, passive task) in response to stimulation. Notably, over four days, evolution occurred, characterized by decreasing delta power and increasing theta and beta power. Topographic maps confirmed these shifts, highlighting affected brain areas. Linear regression emphasized the most significant impact on the first day, with subsequent shifts towards higher frequencies, particularly during the resting state. By the fourth day, resting-state patterns resembled those during cognitive activities. Findings suggest cognitive stimulation induces substantial EEG spectral changes, potentially linked to cognitive enhancements in Rett syndrome. The shift towards higher frequency bands and increased spectral entropy align with enhanced brain activation during cognitive sessions, underscoring the potential of cognitive stimulation therapies and calling for further research to optimize abilities in individuals with Rett syndrome. Game-based cognitive stimulation induces substantial EEG changes in individuals with Rett syndrome, enhancing cognitive functions, notably attention during learning. This study conducts a distinctive examination, assessing the habituation paradigm through the combination of game-based cognitive stimulation and learning, providing valuable insights into enhancing attention in Rett syndrome. Impacting understanding of cognitive processes in Rett syndrome, this research reveals significant EEG variations during tasks, emphasizing the potential of cognitive stimulation for attention enhancement and the need for further research in tailored interventions.

EF‐CorrCA: A multi‐modal EEG‐fNIRS subject independent model to assess speech quality on brain activity using correlated component analysis

AbstractAn investigation on the effect of mental activity in quality perception is presented using simultaneous measurement of electroencephalography (EEG) and functional near‐infrared spectroscopy (fNIRS), in a subject‐independent approach. Building a subject‐independent model is a harder problem due to noise and high EEG variability between individuals, correlated components analysis (CorrCA) have been proposed to extract significant correlated components for a single subject that experiences multiple identical trials; this is done by identifying spatio‐temporal patterns of activity that are well preserved across trials. The aim is to build a model based on neurophysiological data to assess text‐to‐speech quality. In order to build a subject independent model, we extended the use of CorrCA such that it can be applied to the subject independent model. The authors used two preprocessing steps, namely the subject dependent and the stimulus dependent preprocessing. The second preprocessing used the denoising source separation (DSS) to remove noise/artefact that are subject specific. The discrete convolution is used for data fusion and the support vector machine for regression. With the proposed model, the fusion of EEG and fNIRS performs better than single modality. Using our defined regression accuracy metrics, the authors obtained accuracy of 81.346% for overall impression, 83.28% for valence and 89.714% for arousal. The model compete the baseline that is subject dependent.

Relationships between electrical conductivity of acupuncture points and adaptation hormones

Background. Modern scientific research has shown that the inherent regulatory system of the body is a neuro-endocrine-immune (NEI) network. A researcher proposed a hypothesis, that the bidirectional positive regulatory role of acupuncture was achieved by NEI network. The purpose of this study is to find out relationships between electrical conductivity (EC) of acupuncture points (AP) and adaptation hormones one of the elements of NEI network. Materials and methods. The object of observation were 10 women (32-76 years) and 10 men (37-67 years) examined twice with a weekly interval. The volunteers were considered practically healthy, but with maladaptation. We recorded EC in AP Pg(ND), TR(X) and MC(AVL) (by complex “Medissa”) as well as serum Cortisol, Aldosterone, Testosterone, Triiodothyronine, Calcitonin and PTH (by the ELISA). For statistical analysis used the software package "Statistica 6.4". Results. The multiple linear regression analysis revealed the closest relationship with AP Testosterone raw level (R=0,485). Testosterone level, normalized by sex and age, was associated with AP to the same extent (R=0,439), but positive correlations were transformed into negative ones. Calcitonin level was also moderately positively correlated with AP (R=0,441). PTH was significantly correlated only with TR(X) lateralization (r=-0,363; p=0,022) and Triiodothyronine only with MC(AVL) lateralization (r=0,315; p=0,048). The connections of Aldosterone with AP lateralization turned out to be insignificant, and from the side of Cortisol, they were negligible at all. Despite moderate pairwise correlations, the canonical correlation between the levels of adaptation Hormones, on the one hand, and EC of AP and their lateralization, on the other hand, turned out to be quite strong (R=0,856). Conclusion. Electrical conductivity of AP and their lateralization is 73% determined by the constellation of adaptation hormones, which are an important component of the NEI network. In the next message, we will demonstrate the existence of connections between AP and parameters of EEG and heart rate variability as well as Immunity.

Entropy in Electroencephalographic Signals Modulates with Force Magnitude During Grasping – A Preliminary Report

The ability to hold objects relies on neural processes underlying grip force control during grasping. Brain activity lateralized to contralateral hemisphere averaged over trials is associated with grip force applied on an object. However, the involvement of neural variability within-trial during grip force control remains unclear. We examined dependence of neural variability over frontal, central, and parietal regions of interest (ROI) on grip force magnitude using noninvasive electroencephalography (EEG). We utilized our existing EEG dataset comprised of healthy young adults performing an isometric force control task, cued to exert 5, 10, or 15% of their maximum voluntary contraction (MVC) across trials and received visual feedback of their grip force. We quantified variability in EEG signal via sample entropy (sequence-dependent) and standard deviation (sequence-independent measure) over ROI. We found lateralized modulation in EEG sample entropy with force magnitude over central electrodes but not over frontal or parietal electrodes. However, modulation was not observed for standard deviation in the EEG activity. These findings highlight lateralized and spatially constrained modulation in sequence-dependent, but not sequence-independent component of EEG variability. We contextualize these findings in applications requiring finer precision (e.g., prosthesis), and propose directions for future studies investigating role of neural entropy in behavior.

Association of EEG characteristics with outcomes following pediatric ICU cardiac arrest: A secondary analysis of the ICU-RESUScitation trial

Background and ObjectivesThere are limited tools available following cardiac arrest to prognosticate neurologic outcomes. Prior retrospective and single center studies have demonstrated early EEG features are associated with neurologic outcome. This study aimed to evaluate the prognostic value of EEG for pediatric in-hospital cardiac arrest (IHCA) in a prospective, multicenter study. MethodsThis cohort study is a secondary analysis of the ICU-Resuscitation trial, a multicenter randomized interventional trial conducted at 18 pediatric and pediatric cardiac ICUs in the United States. Patients who achieved return of circulation (ROC) and had post-ROC EEG monitoring were eligible for inclusion. Patients < 90 days old and those with pre-arrest Pediatric Cerebral Performance Category (PCPC) scores > 3 were excluded. EEG features of interest included EEG Background Category, and presence of focal abnormalities, sleep spindles, variability, reactivity, periodic and rhythmic patterns, and seizures. The primary outcome was survival to hospital discharge with favorable neurologic outcome. Associations between EEG features and outcomes were assessed with multivariable logistic regression. Prediction models with and without EEG Background Category were developed and receiver operator characteristic curves compared. ResultsOf the 1129 patients with an index cardiac arrest who achieved ROC in the parent study, 261 had EEG within 24 h of ROC, of which 151 were evaluable. The cohort included 57% males with a median age of 1.1 years (IQR 0.4, 6.8). EEG features including EEG Background Category, sleep spindles, variability, and reactivity were associated with survival with favorable outcome and survival, (all p < 0.001). The addition of EEG Background Category to clinical models including age category, illness category, PRISM score, duration of CPR, first documented rhythm, highest early post-arrest arterial lactate improved the prediction accuracy achieving an AUROC of 0.84 (CI 0.77–0.92), compared to AUROC of 0.76 (CI 0.67–0.85) (p = 0.005) without EEG Background Category. ConclusionThis multicenter study demonstrates the value of EEG, in the first 24 h following ROC, for predicting survival with favorable outcome after a pediatric IHCA.

Epilepsy, EEG and chromosomal rearrangements.

Chromosomal abnormalities are associated with a broad spectrum of clinical manifestations, one of the more commonly observed of which is epilepsy. The frequency, severity, and type of epileptic seizures vary according to the macro- and microrearrangements present. Even within a single chromosomal anomaly, we most often deal with a phenotypic spectrum. The aim of the study was to look for chromosomal rearrangements with a characteristic electroencephalographic pattern. Only a few disorders have peculiar electroclinical abnormalities: 1p36, 4p16, 6q terminal or trisomy 12p, Angelman syndrome, inv dup 15, 15q13.3 deletions, ring 20, Down syndrome, or Xp11.22-11.23 duplication. We also reviewed studies on epileptic seizures and typical electroencephalographic patterns described in certain chromosomal rearrangements, focusing on the quest for potential electroclinical biomarkers. The comprehensive review concludes with clinical presentations of the most common micro and macro chromosomal rearrangements, such as 17q21.31 microdeletion, 6q terminal deletion, 15q inv dup syndrome, 2q24.4 deletion, Xp11.22-11.23 duplication, 15q13.3 microdeletion, 1p36 terminal deletion, 5q14.3 microdeletion, and Xq28 duplication. The papers reviewed did not identify any specific interictal electroencephalographic patterns that were unique and significant biomarkers for a given chromosomal microrearrangement. The types of seizures described varied, with both generalized and focal seizures of various morphologies being reported. Patients with chromosomal anomalies may also meet the criteria for specific epileptic syndromes such as Infantile Epilepsy Spasms Syndrome (IESS, West syndrome): 16p13.11, 15q13.3 and 17q21.31 microdeletions, 5q inv dup. syndrome; Dravet syndrome (2q24.4 deletion), Lennox-Gastaut syndrome (15q11 duplication. 1q13.3, 5q inv dup.); or Self-Limited Epilepsy with Autonomic Features (SeLEAS, Panayiotopoulos syndrome: terminal deletion of 6q.n), Self-Limited Epilepsy with Centrotemporal Spikes (SeLECT): fragile X syndrome. It is essential to better characterize groups of patients to more accurately define patterns of epilepsy and EEG abnormalities. This could lead to new treatment strategies. Future research is required to better understand epileptic syndromes and chromosomal rearrangements. PLAIN LANGUAGE SUMMARY: This paper presents EEG recording abnormalities in patients with various gene abnormalities that can cause epilepsy. The authors summarize these EEG variations based on a literature review to see if they occur frequently enough in other chromosomal abnormalities (in addition to those already known) to be a clue for further diagnosis.

Assessing the Relationship Between Body Mass Index and Neural Activity of Prefrontal Cortex in Overweight Adults Using EEG-Resting State Data: A Wavelet Transform Analysis

Neuroscientific evidence suggests that weight gain may be associated with changes in brain lobes' volume and function, as well as impulsive behaviour related to eating. However, it remains unclear whether impulsivity behaviour in overweight subjects is linked to abnormal activity in the resting state. To address this question, we propose a novel method to assess the relationship between different levels of body mass index (BMI) and neural activity of the prefrontal cortex (PFC) using electroencephalography (EEG) resting state data. EEG signals recorded during open-eye resting state from 36 subjects were divided into two groups based on BMI: overweight and normal weight subjects. We applied wavelet transform technique to compute the power for decomposed EEG bands and extracted coherence maps to assess the functional connectivity of the PFC. The one-way analysis of variance (ANOVA) was employed to assess the difference in EEG variables between the study groups. The results show a significant increase in the power of the sub-Theta band (4.49-5.34) Hz in overweight subjects compared to normal weight subjects (p-value = 0.001), as well as dysfunctional connectivity between left-right prefrontal sites in the overweight group with decreasing coherence function. These outcomes suggest that the specific PFC-EEG signals observed in overweight individuals are consistent with EEG patterns seen in other impulsivity-related diseases. Therefore, our findings reveal a specific EEG pattern in overweight adults that could be potentially utilized in developing neurotherapy-based treatment methods for overweight management.

Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia

Electroencephalogram (EEG) recordings of children are often used to study the underlying neural basis of causal factors of reading disorders and dyslexia. However, the inter-subject variability in EEG and the unconstrained nature of reading experiments used to elicit these factors made it challenging for traditional EEG analysis methods to extract neural components of these factors. In this work, we aim to explore the use of novel deep neural network architectures and contrastive learning methods to overcome the methodological limitations of traditional techniques and enhance the extraction process of neural components during reading tasks. Notably, we formulate a neural network architecture to extract EEG embedding using contrastive loss that maximizes the neural congruency in non-dyslexic children compared to children with dyslexia. We plan to evaluate our approach on three EEG datasets involving children with dyslexia performing Rapid Automatized Naming (RAN) and Phonological Processing (PA) tasks. The proposed contrastive learning framework will provide an enhanced tool to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties.

Ictal-Interictal Continuum in the Pediatric Intensive Care Unit.

The ictal-interictal continuum (IIC) consists of several electroencephalogram (EEG) patterns that are common in critically ill adults. Studies focused on the IIC are limited in critically ill children and have focused primarily on associations with electrographic seizures (ESs). We report the incidence of the IIC in the pediatric intensive care unit (PICU). We then compare IIC patterns to rhythmic and periodic patterns (RPP) not meeting IIC criteria looking for associations with acute cerebral abnormalities, ES, and in-hospital mortality. This was a retrospective review of prospectively collected data for patients admitted to the PICU at Children's National Hospital from July 2021 to January 2023 with continuous EEG. We excluded patients with known epilepsy and cerebral injury prior to presentation. All patients were screened for RPP. The American Clinical Neurophysiology Society standardized Critical Care EEG terminology for the IIC was applied to each RPP. Associations between IIC and RPP not meeting IIC criteria, with clinical and EEG variables, were calculated using odds ratios (ORs). Of 201 patients, 21% (42/201) had RPP and 12% (24/201) met IIC criteria. Among patients with an IIC pattern, the median age was 3.4years (interquartile range (IQR) 0.6-12years). Sixty-seven percent (16/24) of patients met a single IIC criterion, whereas the remainder met two criteria. ESs were identified in 83% (20/24) of patients and cerebral injury was identified in 96% (23/24) of patients with IIC patterns. When comparing patients with IIC patterns with those with RPP not qualifying as an IIC pattern, both patterns were associated with acute cerebral abnormalities (IIC OR 26 [95% confidence interval {CI} 3.4-197], p = 0.0016 vs. RPP OR 3.5 [95% CI 1.1-11], p = 0.03), however, only the IIC was associated with ES (OR 121 [95% CI 33-451], p < 0.0001) versus RPP (OR 1.3 [0.4-5], p = 0.7). Rhythmic and periodic patterns and subsequently the IIC are commonly seen in the PICU and carry a high association with cerebral injury. Additionally, the IIC, seen in more than 10% of critically ill children, is associated with ES. The independent impact of RPP and IIC patterns on secondary brain injury and need for treatment of these patterns independent of ES requires further study.

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

Neonatal seizures during extra corporeal membrane oxygenation support.

To evaluate EEG monitoring during neonatal ECMO and to identify any correlations between seizure detection to abnormal neuroimaging.Eight-year, service evaluation of neonates who received at least one continuous EEG (cEEG) whilst on ECMO at Great Ormond Street Hospital. Pearson's chi-square test and multivariate logistic regression analysis were used to assess clinical and EEG variables association with seizures and neuroimaging findings.Fifty-seven neonates were studied; 57 cEEG recordings were reviewed. The incidence of seizures was 33% (19/57); of these 74% (14/19) were electrographic-only. The incidence of status epilepticus was 42%, (8/19 with 6 neonates having electrographic-only status and 2 electro-clinical status. Seizures were detected within an hour of recording in 84%, (16/19). The overall mortality rate was 39% (22/57). Seizure detection was strongly associated with female gender (OR 4.8, 95% CI: 1.1-20.4, p = 0.03), abnormal EEG background activity (OR 2.8, 95% CI: 1.1-7.4, p = 0.03) and abnormal EEG focal features (OR 23.6, 95% CI: 5.4-103.9, p = 0.001). There was a strong association between the presence of seizures and abnormal neuroimaging findings (OR 10.9, 95% CI: 2.8-41.9, p = 0.001). Neonates were highly likely to have abnormal neuroimaging findings in the presence of severely abnormal background EEG (OR 9.5, 95% CI 1.7-52.02, p = 0.01) and focal EEG abnormalities (OR 6.35, 95% CI 1.97-20.5, p = 0.002)Conclusion: The study highlights the importance of cEEG in neonates undergoing ECMO. An association between seizure detection and abnormal neuroimaging findings was described. What is Known: • Patients on ECMO are at a higher risk of seiures. • Continuous EEG monitoring is recommended by the ACNS for high risk and ECMO patients. What is New: • In this cohort, neonates with sezirues were 11 times more likely of having abnromal neuroimaging findings. • Neonates with burst suppressed or suppressed EEG background were 9.5 times more likely to have abnormal neuroimaging findings. What does this study add? • This study reports a 33% incidence of neonatal seizures during ECMO. • Neonates with seizures were 11 times more likely to have an abnormal brain scan. • The study captures the real-time approach of EEG monitoring. • Recommended cEEG monitoring should last at least 24h for ECMO patients. • This is the first study to assess this in neonates only.

Objective sleep quality predicts subjective sleep ratings

In both clinical and observational studies, sleep quality is usually assessed by subjective self-report. The literature is mixed about how accurately these self-reports track objectively (e.g. via polysomnography) assessed sleep quality, with frequent reports of little to no association. However, previous research on this question focused on between-subject designs, which may be confounded by trait-level variables. In the current study, we used the novel Budapest Sleep, Experiences and Traits Study (BSETS) dataset to investigate if within-subject differences in subjectively reported sleep quality are related to sleep macrostructure and quantitative EEG variables assessed using a mobile EEG headband. We found clear evidence that self-reported sleep quality in the morning is influenced by within-subject variations in sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. These effects were replicated if detailed sleep composition metrics (percentage and latency of specific vigilance states) or two alternative measures of subjective sleep quality were used instead. We found no effect of the number of awakenings or relative EEG delta and sigma power. Between-subject effects (relationships between individual mean values of sleep metrics and subjective sleep quality) were also found, highlighting that analyses focusing only on these may be erroneous. Our findings show that while previous investigations of this issue may have been confounded by between-subject effects, objective sleep quality is indeed reflected in subjective sleep ratings.

A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding

BackgroundMotor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects’ optimal hyperparameters. New methodsThis study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects. ResultsThe MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust. ConclusionsThe combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI’s utility and robustness.

Cross-Day Data Diversity Improves Inter-Individual Emotion Commonality of Spatio-Spectral EEG Signatures Using Independent Component Analysis

Electroencephalogram (EEG) variability poses a great challenge to the affective brain-computer interface (aBCI) for practical applications. Most aBCI frameworks have been demonstrated successfully but deliberated on single-day data, which can be realistically susceptible to psychophysiological changes and further hinder the exploration of inter-individual EEG commonality. This study proposes a multiple-day scenario that learns exclusively from diverse EEG correlates of emotional responses on different days (i.e., enriched data diversity) by using a unified independent components analysis framework. Given an eight-day dataset of 10 subjects ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., 80 sessions), the results demonstrated that the multiple-day scenario intensified the inter-subject emotion commonality ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the percentage of subjects with the same signature) to a certain extent when considering sufficient cross-day sessions, whereas the most commonly adopted single-day analysis (i.e., diversity-confined) led to session-dependent inferior outcomes. Given the best case, the emotional valence dimension was associated with relatively reproducible frontal beta, central midline gamma, and occipital beta modulations with 30%–40% subject commonality, whereas the arousal counterpart suffered more substantially from EEG variability and barely returned representative signatures. These results suggest that EEG signature representation may be substantially compromised by limited data diversity, impeding the efficacy and generalizability of the aBCI model in real-life settings.

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.