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Synthetic data generation has become increasingly popular with the increasing use of generative networks. Recently, Generative Adversarial Network (GAN) architectures have produced exceptional results in synthetic image generation. However, time series generation still needs to be studied. This paper proposes a Conditional GAN-based system to generate unique samples of non-REM sleep electroencephalographic (EEG) signals. The CGAN model had a 1-D Convolution Neural Network based architecture. The model was trained using real EEG from healthy controls. The trained model can generate an artificial 30-second epoch of non-REM sleep whose power spectrum is identical to that of a real sleep EEG.Clinical relevance- Sleep EEG simulation can be used to train and enhance the skillset of fellows and technicians in the sleep medicine field. Variations in EEG signals can be highly complex to model mathematically; however, here, we harness the power of deep learning, using generative models such as CGANs to train, model complex data distributions, and generate diverse and artificial but realistic EEG signals during non-REM sleep.

We introduce a new logic, called Temporal Cohort Logic (TCL), for cohort specification and discovery in clinical and population health research. TCL is created to fill a conceptual gap in formalizing temporal reasoning in biomedicine, in a similar role that temporal logics play for computer science and its applications. We provide formal syntax and semantics for TCL and illustrate the various logical constructs using examples related to human health. Relationships and distinctions with existing temporal logical frameworks are discussed. Applications in electronic health record (EHR) and in neurophysiological data resource are provided. Our approach differs from existing temporal logics, in that we explicitly capture Allen's interval algebra as modal operators in a language of temporal logic (rather than addressing it in the semantic structure). This has two major implications. First, it provides a formal logical framework for reasoning about time in biomedicine, allowing general (i.e., higher-levels of abstraction) investigation into the properties of this approach (such as proof systems, completeness, expressiveness, and decidability) independent of a specific query language or a database system. Second, it puts our approach in the context of logical developments in computer science, allowing potential translation of existing results into the setting of TCL and its variants or subsystems so as to illuminate opportunities and computational challenges involved in temporal reasoning for biomedicine., (©2022 AMIA - All rights reserved.)

Motor Imagery-based Brain Computer Interface (MI-BCI) is a typical active BCI with a main focus on motor intention identification. Hybrid motor imagery (MI) decoding methods that based on multi-modal fusion of Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), especially deep learning-based methods, become popular in recent MI-BCI studies. However, the fusion strategy and network design in deep learning-based methods are complex. To solve this problem, we proposed the multi-channel fusion method (MCF) to simplify current fusion methods, and we designed a multi-channel fusion hybrid network (MCFHNet) based on MCF. MCFHNet combines depthwise convolutional layers, channel attention mechanism, and Bidirectional Long Short Term Memory (Bi-LSTM) layers, which enables strong capability of feature extraction in spatial and temporal domain. The comparison between MCFHNet and representative deep learning-based methods was performed on an open EEG-fNIRS dataset. We found the proposed method can yield superior performance (mean accuracy of 99.641 % in 5-fold cross validation of an intra-subject experiment). This work provides a new option for multi-modal MI decoding, which can be applied in the rehabilitation field based on hybrid BCI systems.

Background: Admission of patients with status epilepticus (SE) to the neurosciences intensive care unit (NICU) may improve management and outcomes compared to general ICUs., Methods: We reviewed all patients with SE admitted to the NICU versus the Medical ICU in our institution between 2005 and 2008. We included only patients with definite or probable SE based on pre-defined criteria. We collected demographic and clinical data, including severity of admission scores and adjusted short-term outcomes for admission and management in the two ICUs., Results: There were 168 visits in 151 patients for definite or probable SE, 46 (27 %) of which were in the NICU and 122 (73 %) in the MICU. APACHE II scores were significant higher in the MICU group (17.5 vs 13.4, p = 0.003) and age in the NICU (58.3 vs 51.5 years, p = 0.041). More continuous EEGs were ordered in the NICU (85 vs 30 %, p < 0.001), where fewer patients were intubated, but more eventually tracheostomized. The NICU had a higher rate of complex partial SE and more alert or somnolent patients, whereas the MICU had a higher rate of generalized SE and more stuporous or comatose patients. Admission diagnoses also differed, with the NICU having higher rate of strokes and the MICU higher rate of toxometabolic etiologies (39 vs 12 % and 11 vs 21 %, p = 0.002). After adjustment, no difference was found in mortality, the ICU or hospital length of stay and modified Rankin score at discharge., Conclusion: SE treatment revealed increased use of continuous EEG in NICU-admitted patients, but without concomitant reduction in LOS or discharge outcomes compared to the MICU.

Sleep medicine is an important component of current head and neck surgery practices. Furthermore, obstructive sleep apnea is an anatomic disease of the upper respiratory tract, a region of anatomy best known to head and neck surgeons. If head and neck surgeons choose to participate in the surgical treatment of sleep apnea, they must develop expertise in the evaluation and home sleep testing aspects of sleep apnea. Because positive airway pressure remains the front line treatment, they must also develop expertise in the prescription of positive airway pressure therapy.

This article examines the value of medical technology through the case of electroencephalograms (EEGs), devices used to visualize brain activity and diagnose seizures. Drawing on ethnographic fieldwork, the article shows that EEGs are valued differently by patients and medical practitioners. While practitioners value EEGs for their clinical utility, i.e., ability to inform clinical decisions, patients value EEGs even in the absence of clinical utility. Indeed, patients derive long-lasting therapeutic effects from this diagnostic technology. These findings intervene in the utilitarian calculus of therapeutic value—a mode of reasoning that equates value with clinical utility—commonly deployed in biomedicine and engineering and call for a recognition of alternative notions such as the therapeutic value of being witnessed and cared for by medical experts via EEGs and other technologies that require time to work. Expansive notions of therapeutic value are imperative for including marginalized patients—especially low-income, disabled, and women patients—in debates on automation and the future of healthcare. Studying how multiple stakeholders value a medical technology provides insight into valuation, objectification, expertise, and other concerns central to science and technology studies. [ABSTRACT FROM AUTHOR]

Abstract Anterior cruciate ligament injuries (ACLi) impact football players substantially leading to performance declines and premature career endings. Emerging evidence suggests that ACLi should be viewed not merely as peripheral injuries but as complex conditions with neurophysiological aspects. The objective of the present study was to compare kicking performance and associated cortical activity between injured and healthy players. Ten reconstructed and 15 healthy players performed a kicking task. Kicking biomechanics were recorded using wearable inertial measurement unit sensors. Cortical activity was captured with a 64-electrode mobile electroencephalography. Multiscale entropy (MSE) analysis of biomechanics revealed increased variability in foot external rotation among injured players. Source-derived event-related spectral perturbations indicated significant differences in posterior alpha and frontal theta oscillations between the two groups. Furthermore, kick-related complexity of these regions as indexed by MSE was reduced in injured players at medium and coarse scales. Our findings suggest sensorimotor changes during kicking in injured players, which may necessitate compensatory strategies involving augmented attention at the cost of processing visuospatial information. This conflict may hinder the integration of task-relevant information across distributed networks. Our study provides preliminary insights into the neurophysiological implications of ACLi within football context and underscores the potential for prospective research.

Abstract The Optimal electrode configuration of Electroencephalograms (EEG) systems for mild cognitive impairment (MCI) detection and monitoring in non-clinical settings, i.e. number of electrodes and the positions of the electrodes, remains to be explored. In the current study, we explored the optimization of electrode configuration for MCI detection. We used a 32-channel EEG device to record the data of 21 MCI patients and 20 cognitively normal elderly (NC) undergoing working memory (WM) tasks. Based on the differential value (MCI group vs. NC group) from the Power Spectral Density (PSD) value of each electrode in θ and α frequency band during WM coding stage, six different electrode configurations were obtained: (1) four electrodes in the occipital lobe (OCL4); (2) three electrodes in the prefrontal lobe (PRL3); (3) four electrodes in the parietal lobe (PLL4), (4) eight electrodes in occipital combined parietal lobe (OPL8), (5) seven electrodes in occipital combined prefrontal lobe (OPL7); and (6) seven electrodes in parietal combined prefrontal lobe (PPL7). A multi-parameter combination-assisted binary logistic regression model was established to distinguish two groups. Receiver operating characteristic (ROC) curves were used to evaluate the MCI diagnostic power of each electrode configuration. The area under curve (AUC) of the ROC of electrode configurations OCL4, PRL3, PLL4, OPL8, OPL7 and PPL7 were 0.765, 0.683, 0.729, 0.83, 0.788, and 0.769, respectively. And the sensitivity of six electrode configurations were 0.962, 0.794, 0.873, 0.943, 0.859, and 0.938, respectively. Among these six configurations, OCL4, i.e. PO3, PO4, PO8, and PO7, had the highest sensitivity 96.2%, which meant that relying solely on these four electrodes of the occipital lobe had the potential to serve as an objective tool for preliminary screening of MCI. The abnormal brain rhythm characteristics of the frontal, parietal, and occipital lobes in the memory encoding stage of MCI provide a new perspective for MCI-WM impairment, which has the potential to be a novel biomarker for the early detection of pathological age-related cognitive decline. Further, a potential four-electrode configuration may be used for a novel detecting and monitoring EEG system of MCI in non-clinical settings.

Abstract Deep language models (DLMs) have exhibited remarkable language understanding and generation capabilities, prompting researchers to explore the similarities between their internal mechanisms and human language cognitive processing. This study investigated the representational similarity (RS) between the abstractive summarization (ABS) models and the human brain and its correlation to the performance of ABS tasks. Specifically, representational similarity analysis (RSA) was used to measure the similarity between the representational patterns (RPs) of the BART, PEGASUS, and T5 models’ hidden layers and the human brain’s language RPs under different spatiotemporal conditions. Layer-wise ablation manipulation, including attention ablation and noise addition was employed to examine the hidden layers’ effect on model performance. The results demonstrate that as the depth of hidden layers increases, the models’ text encoding becomes increasingly similar to the human brain’s language RPs. Manipulating deeper layers leads to more substantial decline in summarization performance compared to shallower layers, highlighting the crucial role of deeper layers in integrating essential information. Notably, the study confirms the hypothesis that the hidden layers exhibiting higher similarity to human brain activity play a more critical role in model performance, with their correlations reaching statistical significance even after controlling for perplexity. These findings deepen our understanding of the cognitive mechanisms underlying language representations in DLMs and their neural correlates, potentially providing insights for optimizing and improving language models by aligning them with the human brain’s language-processing mechanisms.

Abstract Quantifying cognitive potential relies on psychometric measures that do not directly reflect cortical activity. While the relationship between cognitive ability and resting state EEG signal dynamics has been extensively studied in children with below-average cognitive performances, there remains a paucity of research focusing on individuals with normal to above-average cognitive functioning. This study aimed to elucidate the resting EEG dynamics in children aged four to 12 years across normal to above-average cognitive potential. Our findings indicate that signal complexity, as measured by Multiscale Entropy (MSE), was not significantly predictive of the level of cognitive functioning. However, utilizing Directed Phase Lag Index (DPLI) as an effective connectivity measure, we observed consistent patterns of information flow between anterior and posterior regions. Fronto-parietal as well as local connectivity patterns were seen across most of the cognitive functions. Moreover, specific connectivity patterns were obtained for each intellectual quotient index (namely verbal comprehension, visuospatial, fluid reasoning, and processing speed indexes as well as full-scale intellectual quotient). These results underscore the presence of long-range connections and support fronto-parietal theories of cognitive abilities within the resting state brain dynamics of children.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of brain–computer interfaces (BCIs). Multimodal BCIs have been able to gain significant traction given their potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities, including electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), and single-unit activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with a discussion on the available literature, highlighting recent advances, challenges, and future directions for each of these modalities.

Abstract Semantic processing is an essential mechanism in human language comprehension and has profound implications for speech brain-computer interface technologies. Despite recent advances in brain imaging techniques and data analysis algorithms, the mechanisms underlying human brain semantic representations remain a topic of debate, specifically whether this occurs through the activation of selectively separated cortical regions or via a network of distributed and overlapping regions. This study investigates spatiotemporal neural representation during the perception of semantic words related to faces, numbers, and animals using electroencephalography. Source‐level analysis focuses on contrasting neural responses to different semantic categories. Critical intervals used in the source contrast analysis are defined using the peak duration of global field power. Effective connectivity, determined through a causality analysis of brain regions activated for semantic processing, is explored. The findings reveal the necessity of a distributed network of regions for processing specific semantic categories and provide evidence suggesting the existence of a neural substrate for semantic representations.

Persistent motor deficits are highly prevalent among post-stroke survivors, contributing significantly to disability. Despite the prevalence of these deficits, the precise mechanisms underlying motor recovery after stroke remain largely elusive. The exploration of motor system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue of research. Quantitative electroencephalography (qEEG) parameters, including the power ratio index, brain symmetry index, and phase synchrony index, have emerged as potential prognostic markers for overall motor recovery post-stroke. Current evidence suggests a correlation between qEEG parameters and functional motor outcomes in stroke recovery. However, accurately identifying the source activity poses a challenge, prompting the integration of EEG with other neuroimaging modalities, such as functional near-infrared spectroscopy (fNIRS). fNIRS is nowadays widely employed to investigate brain function, revealing disruptions in the functional motor network induced by stroke. Combining these two methods, referred to as integrated fNIRS-EEG, neural activity and hemodynamics signals can be pooled out and offer new types of neurovascular coupling-related features, which may be more accurate than the individual modality alone. By harnessing integrated fNIRS-EEG source localization, brain connectivity analysis could be applied to characterize cortical reorganization associated with stroke, providing valuable insights into the assessment and treatment of post-stroke motor recovery.

Abstract Background Sleep is essential for the recovery of patients with disorders of consciousness (DoC). However, few approaches targeting sleep were applied. Melatonin has been shown to enhance sleep efficiency with virtually no side effects. This study explored melatonin’s benefits for patients with prolonged DoC, as well as the underlying mechanisms involved. Methods A cohort of 46 patients with prolonged DoC were randomly assigned to either the melatonin treatment group or the placebo group. Assessments were conducted using the Coma Recovery Scale-Revised (CRS-R), electroencephalography (EEG), and polysomnography (PSG) before and after the intervention, with follow-up CRS-R evaluations performed 6 months post-treatment. Results Compared to the placebo, melatonin demonstrated a significant improvement in CRS-R scores after a 2-week period in patients with unresponsive wakefulness syndrome (UWS) (F group*time = 6.86, P = 0.032; F group = 4.03, P = 0.045) and this improvement was particularly pronounced in visual scores (F group*time = 7.03, P = 0.030; F group = 4.90, P = 0.027). Moreover, patients with UWS who received melatonin exhibited a higher relative spectral density of the alpha band in the frontal lobe compared to those who received placebo (F time-mel = 4.55, P = 0.033) and benefited for their prognosis after 6 months (Pseudo R 2 = 0.370, F = 12.03, P = 0.034). Conclusions Overall, melatonin intervention seems to have a better response in UWS patients with preserved sleep cycles. These positive effects may not be solely attributed to improvements in the patients’ sleep quality. Trail registration ClinicalTrials.gov: NCT05285124.

Establishing the minimum recording standards for routine and sleep EEG is of great importance for both clinical practice and scientific research, and the joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standard guideline to guide clinical practice and ensure the quality of EEG studies. This article gives an interpretation of the guideline from the aspects of the indications, technical standards, recording duration, sleep deprivation, and activation methods for EEG, in order to provide feasible standards for EEG examination in China and promote the standardization of routine and sleep EEG examination.

Objective To investigate the effect of different effect compartment concentrations of propofol on the ste-reotactic electroencephalography (SEEG) of the left cerebral neocortex and limbic system in adult patients with epilepsy. Methods Fourteen patients undergoing elective electrode removal following robot-guided stereotactic implantation of left intracerebral electrodes in the Department of Epilepsy Surgery at our hospital from January 2021 to December 2022 were selected. Patients were divided into neocortex group and limbic system group according to the location of electrodes in the brain. The power range and percentage of δ, θ, α, β, and γ waves on SEEG at the intraoperative effect compartment concentrations of propofol of 0, 2, 3, 4, and 5 mg/L (concentrations of D1-D5) as well as the modulation index (MI) of phase-amplitude coupling (PAC) in the wave bands of δ-γ, θ-γ, α-γ, and β-γ were recorded and compared between the two groups. Results The power ranges of γ wave on SEEG of the left limbic system and cerebral neocortex were significantly lower at the concentrations of D2-D5 than at the concentration of D1 (F=15.13,20.33,t=4.33-8.72,P

Objective To investigate the clinical and nerve electrophysiological features of patients with propriospinal myoclonus (PSM), and to improve the understanding of this disease. Methods A retrospective analysis was performed for the medical records of five patients with PSM who were treated at Electroencephalogram Monitoring Center of our hospital from July 2019 to September 2021. Their clinical and nerve electrophysiological features were summarized, and they were followed up to observe the efficacy of drug treatment. Results All fivepatients met the diagnostic criteria for PSM, among whom there were three male patients and two female patients, with involuntary myoclonus of the body as the main symptom. The age of onset of PSM ranged from 28 to 50 years. There were three patients with idiopathic PSM and two with secondary PSM. All five patients underwent video electroencephalography (V-EEG) and electromyography examinations, and no abnormal discharges were observed du-ring the ictal period, while only electromyographic artifacts were observed. Myoelectricity mainly originated from the abdomen and the trunk, with a conduction velocity of 1.4-13.7 m/s and a burst duration of 100-1 000 ms. Medications such as clonazepam could effectively reduce myoclonic seizuresin PSM patients. Conclusion Understanding the clinical and nerve electrophysiological features of PSM is the key to its diagnosis. V-EEG is an important basis for the differential diagnosis of PSM, characterized by the absence of abnormal discharges during the ictal period and the presence of myoclonic electromyographic bursts on electromyography. Clonazepam has a good therapeutic effect on the symptoms of PSM.

Abstract Technological innovation has been crucial in the evolution of our lineage, with tool use and production linked to complex cognitive processes. While previous research has examined the cognitive demands of early stone toolmaking, the neurocognitive aspects of early hominin tool use remain largely underexplored. This study relies on electroencephalography to investigate brain activation patterns associated with two distinct early hominin tool-using behaviors: forceful hammerstone percussion, practiced by both humans and non-human primates and linked to the earliest proposed stone tool industries, and precise flake cutting, an exclusive hominin behavior typically associated with the Oldowan. Our results show increased engagement of the frontoparietal regions during both tasks. Furthermore, we observed significantly increased beta power in the frontal and centroparietal areas when manipulating a cutting flake compared to a hammerstone, and increased beta activity over contralateral frontal areas during the aiming (planning) stage of the tool-using process. This original empirical evidence suggests that certain fundamental brain changes during early hominin evolution may be linked to precise stone tool use. These results offer new insights into the complex interplay between technology and human brain evolution and encourage further research on the neurocognitive underpinnings of hominin tool use.

Abstract Migraine is one of the most common neurological disorders in the US. Currently, the diagnosis and management of migraine are based primarily on subjective self-reported measures, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of encephalography (EEG) data at Cleveland Clinic to identify signatures that correlate with migraine. We performed two independent analyses, a prospective analysis (n = 62 subjects) and a retrospective age-matched analysis on a larger cohort (n = 734) obtained from the sleep registry at Cleveland Clinic. In the prospective analysis, no significant difference between migraine and control groups was detected in the mean power spectral density (PSD) of an all-electrodes montage in the frequency range of 1–32 Hz, whereas a significant PSD increase in single occipital electrodes was found at 12 Hz in migraine patients. We then trained machine learning models on the binary classification of migraine versus control using EEG power features, resulting in high accuracies (82–83%) with occipital electrodes’ power at 12 Hz ranking highest in the contribution to the model’s performance. Further retrospective analysis also showed a consistent increase in power from occipital electrodes at 12 and 13 Hz in migraine patients. These results demonstrate distinct and localized changes in brain activity measured by EEG that can potentially serve as biomarkers in the diagnosis and personalized therapy for individuals with migraine.

The diagnosis and treatment of disorder of consciousness (DOC) is still challenging. One of the neural mechanisms of DOC is the destruction of synchronization and connectivity of neural oscillation. This article will review the progress of neural oscillation in the diagnosis and treatment of DOC from three aspects: theoretical basis, application progress, and existing deficiencies of neural oscillations applied to DOC. Neural oscillation is the periodic and synchronous activity of neurons under certain conditions. There are obvious differences in the spectrum characteristics, phase synchronization, and response to neural regulation of neural oscillations among different severities of DOC. The analysis of neural oscillation based on electroencephalography (EEG) reveals the information transmission and processing ability at different levels of consciousness, which is helpful to distinguish different degrees of DOC more accurately and achieve the purpose of DOC wakening treatment by regulating neuro oscillation. Despite there being some problems like insufficient mechanism studies and slow application transformation, neural oscillation has shown great application potential in the diagnosis and treatment of DOC.

Abstract A concern in the field of autism electroencephalography (EEG) biomarker discovery is their lack of reproducibility. In the present study, we considered the problem of learning reproducible associations between multiple features of resting state (RS) neural activity and autism, using EEG data collected during a RS paradigm from 36 to 96 month-old children diagnosed with autism (N = 224) and neurotypical children (N = 69). Specifically, EEG spectral power and functional connectivity features were used as inputs to a regularized generalized linear model trained to predict diagnostic group (autism versus neurotypical). To evaluate our model, we proposed a procedure that quantified both the predictive generalization and reproducibility of learned associations produced by the model. When prioritizing both model predictive performance and reproducibility of associations, a highly reproducible profile of associations emerged. This profile revealed a distinct pattern of increased gamma power and connectivity in occipital and posterior midline regions associated with an autism diagnosis. Conversely, model selection based on predictive performance alone resulted in non-robust associations. Finally, we built a custom machine learning model that further empirically improved robustness of learned associations. Our results highlight the need for model selection criteria that maximize the scientific utility provided by reproducibility instead of predictive performance.

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

Abstract Among patients with epilepsy, 30–40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms.

Abstract Magnetoencephalography/electroencephalography (M/EEG) can provide insights into migraine pathophysiology and help develop clinically valuable biomarkers. To integrate and summarize the existing evidence on changes in brain function in migraine, we performed a systematic review and meta-analysis (PROSPERO CRD42021272622) of resting-state M/EEG findings in migraine. We included 27 studies after searching MEDLINE, Web of Science Core Collection, and EMBASE. Risk of bias was assessed using a modified Newcastle–Ottawa Scale. Semi-quantitative analysis was conducted by vote counting, and meta-analyses of M/EEG differences between people with migraine and healthy participants were performed using random-effects models. In people with migraine during the interictal phase, meta-analysis revealed higher power of brain activity at theta frequencies (3–8 Hz) than in healthy participants. Furthermore, we found evidence for lower alpha and beta connectivity in people with migraine in the interictal phase. No associations between M/EEG features and disease severity were observed. Moreover, some evidence for higher delta and beta power in the premonitory compared to the interictal phase was found. Strongest risk of bias of included studies arose from a lack of controlling for comorbidities and non-automatized or non-blinded M/EEG assessments. These findings can guide future M/EEG studies on migraine pathophysiology and brain-based biomarkers, which should consider comorbidities and aim for standardized, collaborative approaches.

The Turkish Epilepsy Society, the International League Against Epilepsy (ILAE), our close ally, completed the preliminary evaluation of the study range on August 12, 2024. This evaluation included recommendations for updating the role of the “2017 Seizure Classification” (2017 SC) in clinical practice worldwide. Following this study, an update document was presented to us, and we were invited to provide individual opinions by October 16, 2024. In this article, we summarize the pre-2017 stages, the 2017 classification, the findings of the Turkish Epilepsy Society, and the results of an e-mail survey conducted in 2018. This summary aims to enhance the understanding of the subject and revisit the process. Furthermore, we incorporate the forward-looking scientific basis and explanations of the changes made by the ILAE task force, presented in the working group’s own words. The final version of the classification, along with the classifier and descriptor tables, has been included in Turkey directly from the original article without altering the terminology used in the 2017 SC. We must consider what new options we propose and whether they will address the shortcomings of the 2017 SC. Together with colleagues from around the globe, we are committed to determining the future direction of this classification.

Abstract Agreeableness, one of the five personality traits, is associated with socio-cognitive abilities. This study investigates how agreeableness impacts the perception of social interactions, while considering sex that might moderate this effect. Sixty-two young adults, preselected to ensure a wide range of agreeableness scores, underwent EEG recording while viewing images depicting real-world scenes of two people either engaged in a social interaction or acting independently. Behavioral results suggested a trend where higher agreeableness scores predicted better ability to detect social interactions primarily in males. ERP analysis showed that individuals with higher agreeableness exhibited stronger neural differentiation between social and non-social stimuli, observed in both females and males, and in the whole sample. This neural differentiation, occurring early in the processing timeline, was particularly extensive in males, and predictive of their performance. Three independent source analyses, conducted for the whole sample and for each sex, identified the engagement of right fronto-parietal regions for the ERP-agreeableness association. These findings enhance our understanding of how agreeableness shapes the neural mechanisms underlying social interaction detection and emphasize sex as an important factor in this dynamic. They also highlight the need for tailored approaches that consider personality traits and sex in clinical interventions targeting social impairments.

Aim: Headaches are a major cause of presentations to pediatric neurology clinics, the majority being primary. Although diagnosis is mostly based on medical history and physical examination, imaging methods and electroencephalography (EEG) are used for differential diagnosis or identifying accompanying conditions. We evaluated cases of primary headache presenting to a newly established pediatric neurology clinic in July-December 2022 and compared their magnetic resonance imaging (MRI) and EEG findings. Materials and Methods: Individuals presenting with headaches were first classified as primary or secondary headaches, and patients with primary headaches were included as migraine or tension-type headaches (TTH). Two hundred four patients presented but only fifty migraines and 50 TTH patients, who had EEG and MRI, met the study criteria. Results: Greater photophobia, phonophobia, and family histories were present in the migraineurs, while attack frequencies were higher and durations shorter in the TTH group (p=0.025, p=0.001, respectively). Pain was generally throbbing in character in the migraine patients and compression in the TTH cases. No pathology was encountered in the MRIs of 90% of the migraine patients and 94% of the TTH group. While no pathology was detected at EEG in most cases, sharp spike-wave activity was determined in 10% of the migraine patients and in 2% of the TTH group. Conclusion: MRI and EEG are not generally required in the diagnosis of primary headaches once a detailed history and physical examination have been performed. While the majority of brain MRI requests are of no particular diagnostic value, unnecessarily requested EEGs can lead to misdiagnoses. It is crucially important to ensure that patients are closely monitored and that unnecessary requests are avoided.

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a non-invasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

Scoliosis is a three-dimensional spinal deformity characterized by a lateral deviation of at least 10° Cobb, categorized into idiopathic and non-idiopathic forms, caused by identifiable factors like congenital abnormalities, neuromuscular conditions, or genetic syndromes. This case report discusses a 15-year-old girl with growth delay and growth hormone (GH) deficiency who experienced rapid scoliosis progression. Initial evaluations were normal, and electroencephalography (EEG) showed nonspecific alterations, but further assessment revealed a MYH3 gene variant associated with scoliosis, short stature, and distinct facial features. Treatment with a Lyon ARTbrace and tailored exercises stopped curve progression. This case highlights the need for thorough evaluations in atypical AIS cases to uncover potential causes.

Viper snake bites are associated with haemorrhagic qualities and bleeding disorders, including intracerebral haemorrhage. However, idiopathic epilepsy resulting from viper venom is considered a rare condition. Here is a case of a 30-year-old female who developed idiopathic epilepsy following a snake bite within two hours of the incident. Based on her clinical symptoms and laboratory investigations, the patient was immediately treated with Anti-Snake Venom (ASV) and supportive care. She then experienced an idiopathic seizure upon admission, and her Electroencephalography (EEG) showed generalised epileptiform activity. Subsequently, she was treated with anticonvulsant agents. After being discharged, the patient was readmitted one month later for the same condition, experiencing multiple seizure episodes, each lasting about 40 to 50 seconds. Her MRI of the brain was normal. She was then treated with two anticonvulsant agents and discharged with regular follow-up. This case highlights the importance of maintaining a high suspicion for seizures in patients presenting after envenomation. Early diagnosis and aggressive management may help prevent further neuromuscular damage to the patient.

Abstract The multivariate temporal response function (mTRF) is an effective tool for investigating the neural encoding of acoustic and complex linguistic features in natural continuous speech. In this study, we investigated how neural representations of speech features derived from natural stimuli are related to early signs of cognitive decline in older adults, taking into account the effects of hearing. Participants without ( $$n = 25$$ n = 25 ) and with ( $$n = 19$$ n = 19 ) early signs of cognitive decline listened to an audiobook while their electroencephalography responses were recorded. Using the mTRF framework, we modeled the relationship between speech input and neural response via different acoustic, segmented and linguistic encoding models and examined the response functions in terms of encoding accuracy, signal power, peak amplitudes and latencies. Our results showed no significant effect of cognitive decline or hearing ability on the neural encoding of acoustic and linguistic speech features. However, we found a significant interaction between hearing ability and the word-level segmentation model, suggesting that hearing impairment specifically affects encoding accuracy for this model, while other features were not affected by hearing ability. These results suggest that while speech processing markers remain unaffected by cognitive decline and hearing loss per se, neural encoding of word-level segmented speech features in older adults is affected by hearing loss but not by cognitive decline. This study emphasises the effectiveness of mTRF analysis in studying the neural encoding of speech and argues for an extension of research to investigate its clinical impact on hearing loss and cognition.

Autoimmune encephalitis is complex and gradually being recognized. Anti-N-methyl-D-aspartate Receptor (Anti-NMDAR) encephalitis was the most well-known and its unique electroencephalography (EEG) pattern is extreme delta brush (EDB). Anti-Yo encephalitits is far less than common anti-NMDAR encephalitis (anti-NMDARE). A 78-year-old male presented with progressive apathy, hypotension, unsteady gait, and depressed consciousness. EEG revealed an EDB pattern while the serum test was positive for anti-Yo antibodies. The patient then received 10 rounds of plasma exchange, and his blood pressure stability improved. Consequently, urine cytology and abdominal computed tomography revealed atypical cells and linear enhancement in the bladder dome, respectively. However, instead of further pathological confirmation and treatment, the patient’s family requested hospice care. As a result, the patient died of desaturation 7 days later after the withdrawal of ventilatory support. First recognized in 2012, EDB is believed to be specific to NMDARE. However, to date, EDB has not been well described, and no description is available regarding its reactivity. To our knowledge, this is the first case of EDB with anti-Yo encephalitis. Similar to the cases of EDB with anti-NMDARE, our patient did not have satisfied prognosis despite no further investigation and treatment of the possible underlying malignancy. As the prevalence and underlying mechanism of this EEG pattern are unclear, further studies are warranted to identify the potentially similar mechanisms and correlation between anti-NMDAR and anti-Yo encephalitis.

Abstract The auditory steady state response (ASSR) arises when periodic sounds evoke stable responses in auditory networks that reflect the acoustic characteristics of the stimuli, such as the amplitude of the sound envelope. Larger for some stimulus rates than others, the ASSR in the human electroencephalogram (EEG) is notably maximal for sounds modulated in amplitude at 40 Hz. To investigate the local circuit underpinnings of the large ASSR to 40 Hz amplitude-modulated (AM) sounds, we acquired skull EEG and local field potential (LFP) recordings from primary auditory cortex (A1) in the rat during the presentation of 20, 30, 40, 50, and 80 Hz AM tones. 40 Hz AM tones elicited the largest ASSR from the EEG acquired above auditory cortex and the LFP acquired from each cortical layer in A1. The large ASSR in the EEG to 40 Hz AM tones was not due to larger instantaneous amplitude of the signals or to greater phase alignment of the LFP across the cortical layers. Instead, it resulted from decreased latency variability (or enhanced temporal consistency) of the 40 Hz response. Statistical models indicate the EEG signal was best predicted by LFPs in either the most superficial or deep cortical layers, suggesting deep layer coordinators of the ASSR. Overall, our results indicate that the recruitment of non-uniform but more temporally consistent responses across A1 layers underlie the larger ASSR to amplitude-modulated tones at 40 Hz.

Abstract Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants’ degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.