EEG Rhythms Research Articles

Contributions of action potentials to scalp EEG: Theory and biophysical simulations.

Differences in the apparent 1/f component of neural power spectra require correction depending on the underlying neural mechanisms, which remain incompletely understood. Past studies suggest that neuronal spiking produces broadband signals and shapes the spectral trend of invasive macroscopic recordings, but it is unclear to what extent action potentials (APs) influence scalp EEG. Here, we combined biophysical simulations with statistical modelling to examine the amplitude and spectral content of scalp potentials generated by the electric fields from spiking activity. In physiological parameter regimes, we found that APs contribute negligibly to the EEG spectral trend. Consistent with this, comparing our biophysical simulations with previously published data from pharmacologically paralyzed subjects suggested that the EEG spectral trend can be explained by a combination of synaptic timescales and electromyogram contamination. We also modelled rhythmic EEG generation, finding that APs can generate detectable narrowband power between approximately 60 and 1000 Hz, reaching frequencies much faster than would be possible from synaptic currents. Finally, we show that different spectral detrending strategies are required for AP generated oscillations compared to synaptically generated oscillations, suggesting that existing detrending methods for EEG spectra need to be modified for high frequency signals.

EEG correlates of acquiring race driving skills.

Objective 

Race driving is a complex motor task that involves multiple concurrent cognitive processes in different brain regions coordinated to maintain and optimize speed and control. Delineating the neuroplasticity accompanying the acquisition of complex and fine motor skills such as racing is crucial to elucidate how these are gradually encoded in the brain and inform new training regimes. This study aims, first, to identify the neural correlates of learning to drive a racing car using non-invasive electroencephalography (EEG) imaging and longitudinal monitoring. Second, we gather evidence on the potential role of transcranial Direct Current Stimulation (tDCS) in enhancing the training outcome of race drivers. 

Approach
We collected and analyzed multimodal experimental data, including drivers' EEG and telemetry from a driving simulator to identify neuromarkers of race driving proficiency and assess the potential to improve training through anodal tDCS. 

Main Results
Our findings indicate that theta-band EEG rhythms and alpha-band effective functional connectivity between frontocentral and occipital cortical areas are significant neuromarkers for acquiring racing skills. We also observed signs of a potential tDCS effect in accelerating the learning process. 

Significance 
These results provide a foundation for future research to develop innovative race-driving training protocols using neurotechnology.

Unveiling the hidden electroencephalographical rhythms during development: Aperiodic and periodic activity in healthy subjects

ObjectiveThe study analyzes power spectral density (PSD) components, aperiodic (AP) and periodic (P) activity, in resting-state EEG of 240 healthy subjects from 6 to 29 years old, divided into 4 groups. MethodsWe calculate AP and P components using the (Fitting Oscillations and One-Over-f (FOOOF)) plugging in EEGLAB. All PSD components were calculated from 1-45 Hz. Topography analysis, Spearman correlations, and regression analysis with age were computed for all components. ResultsAP and P activity show different topography across frequencies and age groups. Age-related decreases in AP exponent and offset parameters lead to reduced power, while P power decreases (1–6 Hz) and increases (10–15 Hz) with age. ConclusionsWe support the distinction between the AP and P components of the PSD and its possible functional changes with age. AP power is dominant in the configuration of the canonical EEG rhythms topography, although P contribution to topography is embedded in the canonical EEG topography. Some EEG canonical characteristics are similar to those of the P component, as topographies of EEG rhythms (embedded) and increases in oscillatory frequency with age. SignificanceWe support that spectral power parameterization improves the interpretation and neurophysiological and functional accuracy of brain processes.

Event-related desynchronization of eeg sensorimotor rhythms in hemiparesis post-stroke patients

Motor impairment is one of the most prevalent consequences of a stroke, necessitating the implementation of efficacious diagnostic and rehabilitative techniques. An evaluation of alterations in sensorimotor cortical activity during the processes of movement preparation and execution can provide valuable insights into the state of motor circuits following a stroke and the potential for recovery. The objective of the present study was to evaluate the spatiotemporal characteristics of event-related desynchronization (ERD) of sensorimotor EEG rhythms in patients with hemiparesis following a stroke, during movements with the paretic and healthy hands. A total of 19 patients with hemiparesis following a stroke participated in the study. An EEG was recorded while the subject performed a visual-motor task. The analysis focused on the event-related desynchronization in the alpha (6–15 Hz) and beta (15–30 Hz) bands. An asymmetry in the ERD was observed, with a predominant response in the intact hemisphere, regardless of the hand performing the movement. The magnitude of the ERD in the affected hemisphere demonstrated a correlation with the Fugl-Meyer score. Furthermore, a notable correlation was identified between the magnitude of beta-ERD in the affected hemisphere during movements of the healthy limb and the degree of motor function recovery. The results demonstrate the utility of ERD pattern assessment for diagnosing the state of sensorimotor networks after stroke. The detection of a correlation between the magnitude of ERD during movements of the healthy arm and the assessment of sensorimotor functions of the patient expands the possibilities of using EEG to assess patients even with complete absence of movements in the paretic limb.

Improved empirical wavelet transform combined with particle swarm optimization-support vector machine for EEG-based depression recognition

Depression not only inflicts physical harm on patients and diminishes their quality of life, but also imposes a significant burden on families and society. Current diagnostic methods are predominantly employed post-onset, leading to a lack of early intervention opportunities for patients. Therefore, there is a pressing need to develop techniques for detecting early signs of depression to enable timely intervention and potentially improve recovery rates. In this paper, we propose an improved method for the early objective diagnosis of depression utilizing an empirical wavelet transform (EWT) technique enhanced by a particle swarm optimization-support vector machine (PSO-SVM) algorithm. Our approach specifically focuses on the Fpz channel in the prefrontal lobe of the brain, which most accurately reflects the electrical anomalies associated with depression among 128 channels of resting-state electroencephalogram (EEG). The EWT is refined based on the Morlet wavelet, which allows for the precise decomposition of EEG rhythms. From these decompositions, we effectively extract six depression-related EEG features: frequency band power, frequency band power ratio, Shannon entropy, permutation entropy, LZ complexity, and variance. Afterward, these distinguishing characteristics are harnessed to detect depression through the optimized PSO-SVM algorithm. Our approach exhibited a accuracy rate of 81.25% on the MODMA publicly accessible dataset, thereby validating its proficiency in assisting in the diagnosis of depression via the analysis of the EEG Alpha band.

The Presence/Absence of an Awake-State Dominant EEG Rhythm in Delirious Patients Is Related to Different Symptoms of Delirium Evaluated by the Intensive Care Delirium Screening Checklist (ICDSC).

(1) Background: Delirium is a serious condition in patients undergoing treatment for somatic diseases, leading to poor prognosis. However, the pathophysiology of delirium is not fully understood and should be clarified for its adequate treatment. This study analyzed the relationship between confusion symptoms in delirium and resting-state electroencephalogram (EEG) power spectrum (PS) profiles to investigate the heterogeneity. (2) Methods: The participants were 28 inpatients in a general hospital showing confusion symptoms with an Intensive Care Delirium Screening Checklist (ICDSC) score of 4 or above. EEG was measured at Pz in the daytime awake state for 100 s with the eyes open and 100 s with the eyes closed on the day of the ICDSC evaluation. PS analysis was conducted consecutively for each 10 s datum. (3) Results: Two resting EEG PS patterns were observed regarding the dominant rhythm: the presence or absence of a dominant rhythm, whereby the PS showed alpha or theta peaks in the former and no dominant rhythm in the latter. The patients showing a dominant EEG rhythm were frequently accompanied by hallucination or delusion (p = 0.039); conversely, those lacking a dominant rhythm tended to exhibit fluctuations in the delirium symptoms (p = 0.020). The other ICDSC scores did not differ between the participants with these two EEG patterns. (4) Discussion: The present study indicates that the presence and absence of a dominant EEG rhythm in delirious patients are related to different symptoms of delirium. Using EEG monitoring in the care of delirium will help characterize its heterogeneous pathophysiology, which requires multiple management strategies.

Evoked synchronization of theta EEG rhythm during preparation of memory-guided saccades and antisaccaded in normals and in patients with clinically high risk for schizophrenia

Evoked synchronization of theta EEG rhythm during preparation of memory-guided saccades and antisaccaded in normals and in patients with clinically high risk for schizophrenia

EEG Oscillations as Neuroplastic Markers of Neural Compensation in Spinal Cord Injury Rehabilitation: The Role of Slow-Frequency Bands.

Spinal cord injury (SCI) affects approximately 250,000 to 500,000 individuals annually. Current therapeutic interventions predominantly focus on mitigating the impact of physical and neurological impairments, with limited functional recovery observed in many patients. Electroencephalogram (EEG) oscillations have been investigated in this context of rehabilitation to identify effective markers for optimizing rehabilitation treatments. We performed an exploratory cross-sectional study assessing the baseline EEG resting state of 86 participants with SCI as part of the Deficit of Inhibitory as a Marker of Neuroplasticity in Rehabilitation Cohort Study (DEFINE). Our multivariate models demonstrated a positive correlation between frontal delta asymmetry and depression symptoms, while the frontal alpha asymmetry band and anxiety symptoms were negatively correlated. Theta oscillations were negatively associated with motor-evoked potential (MEP), whereas alpha oscillations were positively associated with MEP in all regions of interest and with CPM response as a negative correlation. Based on the potential role of lower-frequency oscillations in exerting a salutogenic compensatory effect, detrimental clinical and neurophysiological markers, such as depression and lower ME, likely induce slow oscillatory rhythms. Alpha oscillations may indicate a more salutogenic state, often associated with various cognitive functions, such as attention and memory processing. These results show an attempt by the CNS to reorganize and restore function despite the disruption caused by SCI. Indeed, this finding also challenges the notion that low-frequency EEG rhythms are associated with cortical lesions. These results may contribute to the development of rehabilitation strategies and potentially improve the clinical outcomes of patients with SCI.

Abnormalities of Cortical Neural Synchronization Mechanisms in Patients With Dementia Due to Parkinson’s and Sintomatic Huntington’s Diseases

AbstractBackgroundParkinson’s disease and Huntington’s disease are both neurodegenerative conditions involving the basal ganglia area of the brain. Both conditions can cause symptoms that affect movement. Cognitive decline or dementia can also occur in both. Resting state EEG (rsEEG) rhythms reflect neurophysiological mechanisms and operational functions related to the fluctuation of brain arousal and quiet vigilance in humans. The hypothesis was that rsEEG sources may be more abnormal in Huntington’s disease patients in symptomatic stage (S‐HD) than patients with dementia due to Parkinson’s disease.MethodClinical and rsEEG datasets in 16 PDD, 18 S‐HD, and 25 matched cognitively unimpaired (Nold) participants ‐ matched as demography, education, and gender ‐ were taken from an international archive. The eLORETA freeware was used to estimate cortical rsEEG sources at delta, theta, alpha1, alpha2, alpha3, beta1, beta2, and gamma frequency bands.ResultResults showed lower amplitude of the posterior alpha activities and higher amplitude of widespread low frequencies bands (i.e., delta and theta) in the PDD and S‐HD groups than in the Healthy group. As compared to the PDD group, the S‐HD showed greater reductions in the rsEEG alpha 2 rhythms in the frontal and temporal regions (see Figure 1).ConclusionThese results suggest that cortical sources of rsEEG rhythms might reflect different abnormalities of the core neurophysiological mechanisms underlying brain arousal in quiet wakefulness and low vigilance in PDD, and S‐HD patients. The mentioned rsEEG markers might be clinically useful in the disease staging, monitoring over time, and drug discovery.

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Bioelectric brain activity in workers with hand-arm vibration in dynamics

Introduction. A few neurophysiologic studies have shown that changes of the bioelectrical brain activity are observed in hand-arm vibration syndrome (HAVS) patients. There is practically no information about the state of bioelectrical brain activity in the hand-arm vibration. The purpose is to study the dynamics of the formation of neurophysiologic changes in workers exposed to hand-arm vibration. Materials and methods. Electroencephalography (EEG) and registration of evoked potentials (EP) was carried out in workers exposed to hand-arm vibration (practically healthy workers, HAVS individuals who continue to work in their occupation), twice in a connected sample. The results are presented as median, upper and lower quartiles, and extensive indicators. The significance of differences was assessed using Pearson’s χ2 test, Fisher’s angular transformation, and Wilcoxon T-test. Results. In dynamics, there is a change in the EEG pattern from disorganized with a predominance of alpha activity to disorganized with a predominance of delta, and theta activity; a decrease in the spectral power of the main EEG rhythms in all main functional leads in both examined groups. Diffuse changes in the EEG indicate a multisystem nature of bioelectrical brain activity disorders with involvement of the cerebral cortex, stem, and diencephalic structures of the brain. Changes in the amplitude-temporal parameters of EP manifested by an excess of the latency of the main peaks and a decrease in amplitude, indicate an imbalance in the central nervous system, general modulating influences on the cerebral cortex of the thalamocortical systems of the brain. Limitations. One occupational group with different levels of health status – practically healthy workers, HAVS individuals who continue to work in their occupation – was enrolled in the study. Conclusion. The one-direction and progressive development of changes in the bioelectrical brain activity has been established, manifested by a restructuring of the electrical activity of the brain, desynchronization of neuronal activity, an increase in the level of functional activity of the brain, and an imbalance of general modulating influences on the cerebral cortex. The identified changes are a manifestation of the nonspecific part of the pathogenesis of HAVS.

Subclinical rhythmic EEG discharge of adults (SREDA) in pediatric population: A case series with systematic review of the literature.

Subclinical rhythmic electrographic discharge of adults (SREDA) is one of the rarest and most challenging non-epileptic electroencephalographic variants. Although the pathogenesis of this activity is unclear, an association with vascular insufficiency and cerebral hypoxia has been proposed. SREDA usually occurs in adulthood, but there are few reports in the pediatric population. We performed a systematic review of the literature, confirming the rarity of this condition in children, and added 5 more subjects. We report on a total of 16 children with SREDA. Sufficient data are available for 15 patients. The mean age at first detection of SREDA was 11.5 years. We observed that 67% (10/15) of the subjects had previous seizures: 80% (8/10) of them had an epilepsy diagnosis and 38% (3/8) had generalized epilepsy. Moreover, 8 of 13 subjects whose medical history was available (61%) had a neurodevelopmental disorder. From an electroencephalographic point of view, we noted a prevalence of bilateral SREDA with atypical localization and abrupt onset and end. Since SREDA can be incorrectly interpreted as an epileptic discharge, with possible therapeutic implications, it is important to consider its possible occurrence also in pediatric patients, perhaps more frequently in those with neurodevelopmental disorders.

Development and validation of a nomogram for the estimation of the prognosis of patients presenting with a febrile seizure

BackgroundFebrile seizures constitute a prevalent emergency in early childhood. Previous studies indicated that certain febrile seizures in children may progress to epilepsy, exerting a substantial impact on children's health and developmental trajectory. The objective of this study was to formulate a predictive nomogram to assess the likelihood of transitioning from febrile seizures to epilepsy in pediatric patients, thereby facilitating informed decisions regarding medical interventions for febrile seizures.MethodsA total of 306 patients were enrolled and categorized into training (70%) and test (30%) cohorts. Clinical characteristics were subjected to comparison utilizing chi-squared and t tests. Multivariate logistic regression was employed to identify significant factors for predicting the risk of transitioning from febrile seizures to epilepsy, leading to the development of a nomogram. The nomogram’s performance was assessed through receiver operating characteristic curves, calibration, and decision curve analysis.ResultsPredictive factors associated with the transition to epilepsy encompassed lower Na, elevated RDW, IL-6, and increased background slow rhythm and epileptiform discharges in EEG. The nomogram, incorporating five factors, exhibited commendable predictive value (AUC train = 0.812, AUC test = 0.791) for assessing the risk of transitioning from febrile seizures to epilepsy. Calibration analyses confirmed reliability, and decision curve analysis underscored its clinical utility.ConclusionsLower Na, elevated RDW, IL-6, background slow rhythm, and epileptiform discharges are associated with the risk of transitioning from febrile seizures to epilepsy. The nomogram stands as a valuable tool for predicting this risk, aiding in the strategic implementation of medical interventions to enhance outcomes for patients with febrile seizures.

Evoked Synchronization of Theta EEG Rhythm during Preparation of Memory-Guided Saccades and Antisaccaded in Normals and in Patients with Clinically High Risk for Schizophrenia

Evoked Synchronization of Theta EEG Rhythm during Preparation of Memory-Guided Saccades and Antisaccaded in Normals and in Patients with Clinically High Risk for Schizophrenia

Quantitative Characterization of Rhythmic and Periodic EEG Patterns in Patients in a Coma After Cardiac Arrest and Association With Outcome.

Rhythmic and periodic patterns (RPPs) on EEG in patients in a coma after cardiac arrest are associated with a poor neurologic outcome. We characterize RPPs using qEEG in relation to outcomes. Post hoc analysis was conducted on 172 patients in a coma after cardiac arrest from the TELSTAR trial, all with RPPs. Quantitative EEG included corrected background continuity index (BCI*), relative discharge power (RDP), discharge frequency, and shape similarity. Neurologic outcomes at 3 months after arrest were categorized as poor (CPC = 3-5) or good (CPC = 1-2). A total of 16 patients (9.3%) had a good outcome. Patients with good outcomes showed later RPP onset (28.5 vs 20.1 hours after arrest, p < 0.05) and higher background continuity at RPP onset (BCI* = 0.83 vs BCI* = 0.59, p < 0.05). BCI* <0.45 at RPP onset, maximum BCI* <0.76, RDP >0.47, or shape similarity >0.75 were consistently associated with poor outcomes, identifying 36%, 22%, 40%, or 24% of patients with poor outcomes, respectively. In patients meeting both BCI* >0.44 at RPP onset and BCI* >0.75 within 72 hours, the probability of good outcomes doubled to 18%. Sufficient EEG background continuity before and during RPPs is crucial for meaningful recovery. Background continuity, discharge power, and shape similarity can help select patients with relevant chances of recovery and may guide treatment. February 4, 2014, ClinicalTrial.gov, NCT02056236.

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition.

Resting State EEG Analysis for Schizophrenia: from Alpha-Rhythm Reduction to Microstates Assessment

Background: due to the emergence of new technologies for analyzing of EEG signal, many new researches in this field have appeared in recent years, including those investigating EEG parameters of schizophrenia. The aim: this publication provides an overview of actual studies on the possibilities of using the assessment of resting state EEG recordings in the diagnostics and prognosis of schizophrenia course. Material and methods: publications were selected in eLibrary, PubMed, Google Scholar and CNKI databases using the keywords: “psychosis”, “schizophrenia”, “EEG”, “resting state”. Methodologically, the atricle is a narrative literature review. Thirty-three sources were selected for analysis. Discussion and conclusion: according to the data available to present date qualitive and quantitative assessment of resting EEGs cannot be used for the instrumental diagnosis of schizophrenia because the most commonly detected increase in the proportion of slow-wave activity is seen in a several disorders. However, some quantitative spectral estimates of resting state EEG could be used to identify poor prognosis response to antipsychotic therapy, as well as for objective assessment of the dynamics of the mental state. Estimation of the power of slow resting EEG rhythms and other methods of assessing the connectivity of different neural networks could be considered as potential markers of the presence of a specific endophenotype. Modern digital technologies, including machine learning and artificial intelligence algorithms, make it possible to identify resting EEG of the schizophrenic patients from healthy controls with accuracy, sensitivity and specificity more than 95%. EEG microstates assessment, which can be used to assess the functioning of large neuronal ensembles, are one of the methods for detecting the endophenotype of schizophrenia.

Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy.

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject's engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.

Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep.

Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.

Benign epileptiform variants in EEG: A comprehensive study of 3000 patients

BackgroundThe analysis of EEG demands expertise and keen observation to distinguish epileptiform discharges from benign epileptiform variants (BEVs), a frequent source of erroneous interpretation. The prevalence of BEVs varies based on geographical, racial, and ethnic characteristics. However, most data on BEVs originates from Western populations, and additional studies on different cohorts would enrich the existing literature. MethodsWe reviewed EEGs from our institutional database to study the prevalence of benign epileptiform variants and analyzed their frequency, topography, and other characteristics. Additionally, we investigated the co-existence of epileptiform discharges with BEVs. ResultsWe identified 296 patients with BEVs after reviewing 3000 EEGs (9.9%). The most common BEV was small sharp spikes (SSS), observed in 114 patients (3.8%). Wicket waves, 6 Hz spike and slow wave, 14 and 6 Hz positive bursts, and Rhythmic Temporal Theta of Drowsiness (RTTD) were identified in 67 (2.2%), 40 (1.3%), 39 (1.3%), and 35 (1.16%) patients, respectively and one patient with Subclinical Rhythmic EEG Discharges in Adults (SREDA). Additionally, we observed the co-existence of epileptiform discharges with BEVs, most commonly with SSS (27.8%). ConclusionsThe present study is a large study with 3000 EEGs to describe the BEV characteristics. BEVs were seen in 9.9% of patients, BSSS being the most common. There were minor differences in frequency, gender or age distribution compared to existing literature. We demonstrated the co-existence of epileptiform discharges. Morphological characteristics remain the cornerstone in recognising BEVs. EEG readers need to be aware of features of BEVs to avoid wrongly interpretation.