EEG Indices Research Articles

The best indices of anesthesia depth monitored by electroencephalogram in different age groups

Introduction Current electroencephalogram (EEG) indices for monitoring the depth of anesthesia have not been adjusted for age. This study aims to identify the most suitable EEG indices for monitoring anesthesia depth across different age groups. Method In here, we conducted a prospective observational trial to observe whether different EEG features in different age groups can detect anesthesia depth and compare their performance. Results In Group 1, absolute θ had the highest prediction probability (Pk) value (0.78 ± 0.07), while relative γ had the lowest relative coefficient of variation (CV) value in states 1 (0.160), 2 (0.153), 3 (0.214), and 4 (0.144). In Group 2, absolute θ had the highest Pk value (0.81 ± 0.09), and relative γ had the lowest CV value in states 1 (0.159), 2 (0.178), 3 (0.140), and 4 (0.095). In Group 3, absolute δ had the highest Pk value (0.79 ± 0.08), with absolute slow wave having the lowest CV value in states 1 (0.196), 2 (0.258), 3 (0.295), and 4 (0.181). In Group 4, absolute δ had the highest Pk value (0.75 ± 0.10), and absolute slow wave had the lowest CV value in states 1 (0.176), 2 (0.192), 3 (0.185), and 4 (0.144). Conclusions In this study, we found that absolute θ and relative γ are the most suitable choices for young and middle-aged patients, while absolute δ and absolute slow wave are the most suitable choices for young elderly and elderly patients.

Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task.

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers' cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the "fatigued" spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

Assessment of age-related features of the condition of heavy-duty truck drivers based on spectral analysis of EEG-ECG and stress tests during mass occupational examinations

The authors studied the physiological characteristics of drivers of heavy-duty coal mine vehicles based on spectral analysis of EEG-ECG and stress tests, which have prognostic value in professional activity. The study aims to assess the functional condition of heavy-duty truck drivers of different age groups based on a comprehensive spectral analysis of EEG-ECG under occupational examinations. During the survey, the authors have studied the spectral parameters of the EEG‒ECG in 300 male drivers aged 25 to 66 years. Specialists have performed EEG registration when placing electrodes in the 10–20 system. The EEG analysis was performed with a preliminary visual analysis followed by the removal of artifacts. The scientists assessed the level of representation of the main alpha rhythm and its spatial distribution. The ECG registration was in the first lead. The duration of EEG-ECG registration is about 15 minutes in a typical sequence of stress tests: baseline, rhythmic photostimulation, hyperventilation with recovery periods. The spectral analysis of the EEG‒ECG was performed automatically on a Neurotravel device (Italy). The authors found lower alpha and delta activity in the initial EEG spectra in older drivers aged 45–66 years, which contributed to the cumulative effect of stress load. Light stress stimulation significantly reduced alpha and delta activity and increased beta EEG activity in both groups, while in the older group the high-frequency range significantly exceeded the low-frequency range. Simultaneously with the changes in the EEG, stress photostimulation increased the energy of the ECG spectra in all frequency ranges. The authors established the appearance of additional stress indicators during light stimulation in older drivers based on the correlation of EEG‒ECG spectra (alpha/delta) and the absence of such changes in the group of young drivers aged 25–35 years. Moreover, the EEG–ECG correlation coefficients and their critical values increased in groups in increments of 6 years — from 35 to 65 years. Elderly drivers, based on neuro-cardiodynamic indicators, were in a state of chronic stress and the resulting risks to health and professional activity, and the identified physiological mechanisms of stress may be at the heart of ways to prevent it. Limitations. The study is limited to the assessment of EEG and ECG indicators in 300 heavy truck drivers. Ethics. The study was performed using non-invasive methods and approved by the Bioethical Committee of the Research Institute of Complex Problems of Hygiene and Occupational Diseases, Protocol No. 5, § 1 dated 12/26/2018, in accordance with the Helsinki Declaration of the World Medical Association "Ethical Principles of conducting medical research with human participation as a subject" as amended in 2013 and the "Rules of Good Clinical Practice" approved by by Order of the Ministry of Health of the Russian Federation No. 200n dated April 1, 2016. All the examined patients signed a voluntary informed consent to participate in the study.

BrainScan AI: Early Detection of Schizophrenia through EEG Signals

Schizophrenia is a persistent and debilitating mind disorder characterised via signs and symptoms such as delusions, hallucinations, disorganized questioning, and lack of motivation.The traditional method to diagnosing schizophrenia is based on scientific tests and EEG assessments, which might be time Consuming and difficult, even for skilled clinicians. With the upward thrust of deep studying strategies, there's developing potential for automating and improving the accuracy of schizophrenia detection. in this examine, we explore using diverse deep learning and machine Learning fashions, along with artificial Neural Networks (ANN), to categorise EEG indicators for schizophrenia detection. We propose a custom method for processing EEG records and evaluate the performance of different models. many of the models examined, the ANN completed the highest accuracy of 84%, demonstrating the feasibility of deep mastering techniques for early and correct detection of schizophrenia. This approach ought to provide a dependable and efficient opportunity to conventional diagnostic strategies, lowering the complexity and subjectivity concerned in the modern- day tactics Keywords: Schizophrenia detection, EEG signals ,EEG learning,Artificial Neural Networks (ANN), Machine earning.

Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device.

Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. Specifically, studies using medical-grade EEG data have shown that depression can be accurately detected. However, there is a need to expand the range of applications by achieving a score with machine learning using simpler consumer-grade brain wave sensors. At present, a sufficient score has not been achieved.; Methods: To improve the score of depression detection, we quantified various EEG indices to train models such as power spectrum, asymmetry, complexity, and functional connectivity. In addition, feature selection was performed to ensure that the model learns only promising EEG indices for depression detection. The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; Results: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1-128 Hz; Conclusions: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression.

Correlation between physical exercise levels, depressive symptoms, and sleep quality in college students: Evidence from electroencephalography

BackgroundIn recent years, depression has increasingly become a major global public health issue. Among its common physical symptoms, sleep disturbances are prevalent in individuals with depression and are considered a risk factor for the progression of the disorder. Poor sleep quality may be a significant contributor to depression among college students. However, the EEG indicators that are commonly associated with depressive symptoms and sleep quality, as well as the effects of physical exercise on these EEG indicators, remain unclear. PurposeThis study aims to explore the relationship between physical activity levels, depressive symptoms, and sleep quality among college students, based on resting-state electroencephalography (EEG) data in a cross-sectional study. MethodsA total of 342 college students were recruited to assess physical activity levels, depressive symptoms, sleep quality, and EEG data. Pearson correlation coefficients were used to evaluate the relationships among these variables, and the PROCESS macro (Model 4) for SPSS was applied to examine the mediating role of sleep quality in the relationship between physical activity levels and depressive symptoms, with mediation effects tested using the bootstrap method. ResultsA significant difference was observed in the Pittsburgh Sleep Quality Index (PSQI) total scores between students with and without depressive symptoms (T = 9.746, P < 0.001). Students with depressive symptoms showed poorer sleep quality across various dimensions, including sleep duration, sleep onset latency, sleep disturbances, and daytime dysfunction. Depression symptom were positively correlated with Sleep Quality (r = 0.547, P < 0.001), indicating that higher depression symptoms are associated with poorer sleep quality. Correlations between depressive symptoms and EEG power values revealed significant associations with theta and beta2 frequencies in multiple brain regions (P < 0.05). Physical exercise showed a significant negative correlation with Sleep Quality scores (r = −0.158, P = 0.004), and with sleep duration (r = −0.141, P = 0.011) and daytime dysfunction (r = −0.142, P = 0.010). Additionally, physical exercise was negatively correlated with theta band power at F8 (P < 0.05). Mediation analysis demonstrated that physical exercise has a direct effect on depressive symptoms (β = −0.123, 95 % CI = −0.287 to −0.069) and an indirect effect through improved sleep quality (β = −0.074, 95 % CI = −0.089 to −0.016), accounting for 60.16 % and 40.65 % of the total effect, respectively. ConclusionThese findings provide a theoretical basis for developing precise exercise intervention programs to improve depressive symptoms and sleep quality among college students. The results indicate that moderate physical activity can help alleviate depressive symptoms and improve sleep quality, thereby enhancing the overall health of college students.

Neuroergonomic Attention Assessment in Safety-Critical Tasks: EEG Indices and Subjective Metrics Validation in a Novel Task-Embedded Reaction Time Paradigm.

Background/Objectives: This study addresses the gap in methodological guidelines for neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices, including the engagement index (EI) and beta/alpha ratio, alongside subjective ratings. Methods: A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, this study successfully demonstrated the effectiveness of the paradigm. Results: Results indicate that both the beta/alpha ratio and EI are sensitive to changes in attentional demands, with beta/alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. Conclusions: The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to monitor attention fluctuations accurately and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining a high attention level is crucial.

Electroencephalographic features of alcohol use disorders with different decision-making efficiency in risk conditions

In order to identify the neurophysiological mechanisms underlying the violation of decision-making in risk conditions, we conducted a comparative analysis of spectral EEG indicators of patients with alcohol use disorders with different effectiveness of their decision-making in a number of cognitive tasks. As a result of the cluster analysis, two subgroups of patients were identified: with “moderate” and with “pronounced” decision-making deficit, which did not differ in socio–demographic and clinical indicators (p 0.05). The subgroup of patients with a “pronounced” decision-making deficit differed statistically significantly lower values of the spectral power of θ- and α-rhythm in the central (p = 0.018 for θ-rhythm and p = 0.017 for α-rhythm), parietal (p = 0.031 for θ-rhythm and p = 0.014 for α-rhythm), occipital (p = 0.029 for θ-rhythm and p = 0.016 for α-rhythm) and temporal (p = 0.022 on the left and p = 0.043 on the right for α-rhythm) leads compared with patients with “moderate” decision-making deficit. Thus, in a subgroup of patients with a “pronounced” deficit of decision-making, a certain deficit of the brain’s inhibitory systems was noted.

Recommendation for the practice of total intravenous anesthesia.

This Recommendation was developed by the Japanese Society of Intravenous Anesthesia Recommendation Making Working Group (JSIVA-WG) to promote the safe and effective practice of total intravenous anesthesia (TIVA), tailored to the current situation in Japan. It presents a policy validated by the members of JSIVA-WG and a review committee for practical anesthesia management. Anesthesiologists should acquire and maintain the necessary knowledge and skills to be able to administer TIVA properly. A secure venous access is critically important for TIVA. To visualize and understand the pharmacokinetics of intravenous anesthetics, use of real-time pharmacokinetic simulations is strongly recommended. Syringe pumps are essential for the infusion of intravenous anesthetics, which should be prepared according to the rules of each individual anesthesia department, particularly with regard to dilution. Syringes should be clearly labeled with content and drug concentration. Whenmanaging TIVA, particularly with the use of muscle relaxants, monitoring processed electroencephalogram (EEG) is advisable. However, the depth of sedation/anesthesia must be assessed comprehensively using various parameters, rather than simply relying on a single EEG index. TIVA should be swiftly changed to an alternative method that includes inhalation anesthesia if necessary. Use of antagonists at emergence may be associated with re-sedation risk. Casual administration of antagonists and sending patients back to surgical wards without careful observation are not acceptable.

Differences and potential mechanisms of theta oscillation and temporoparietal and temporal-central networks in temporal lobe epilepsy patients with unilateral hippocampal sclerosis

BackgroundThere is a lack of further exploration of the epileptogenic network of specific types of epilepsy, such as unilateral hippocampal sclerosis (HS), and there is an urgent need to find exact evidence to confirm the consistency of its brain network changes.MethodsWe enrolled 22 mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE-HS) patients to compare the differences in brain activity between 22 healthy controls (HCs) and them. Resting-state electroencephalography (EEG) was also measured. Then, we calculated the power spectral density and phase locking values in and between these electrodes.ResultsThe results showed the increased theta power was related to the high severity of epilepsy in the temporal, parietal, and central regions in mTLE-HS patients, and there were positive correlations between theta power in the contralateral temporal region and seizure frequency. Theta power in the ipsilateral parietal lobe is positively correlated with the number of anti-seizure medications (ASMs), but not with the usage of third-generation ASMs. Meanwhile, the temporal lobe of mTLE-HS patients had more connectivity with parietal lobe and central region.ConclusionsTheta power is an important EEG indicator of mTLE-HS, positively correlates with epilepsy severity and seizure frequency, and has network properties that can be observed outside the lesion. Moreover, the usage of third-generation ASMs did not affect the risk of increased theta power. Lastly, the temporoparietal and temporal-central networks are likely to be causative pathways in epilepsy patients with cognitive impairment. This study provides a potential guideline for the treatment of mTLE-HS in clinical practice.

Electroencephalogram-Based Anesthesia Indices Differently React to Modulations of Alpha-Oscillatory Activity.

The electroencephalographic (EEG) provides the anesthesiologist with information regarding the level of anesthesia. Processed EEG indices are available that reflect the level of anesthesia as a single number. Strong oscillatory EEG activity in the alpha-band may be associated with an adequate level of anesthesia and a lower incidence of cognitive sequelae. So far, we do not know how the processed indices would react to changes in the alpha-band activity. Hence, we modulated the alpha-oscillatory activity of intraoperative EEG to assess possible index changes. We performed our analyses based on data from 2 studies. Intraoperative EEG was extracted, and we isolated the alpha-band activity by band-pass filtering (8-12 Hz). We added or subtracted this activity to the original EEG in different steps with different amplifications of the alpha signal. We then replayed these signals to the bispectral index (BIS), the Entropy Module (state entropy [SE]), the CONOX (qCON), and the SEDLine (patient state index [PSI]); and evaluated the alpha-band modulation's impact on the respective index. The indices behaved differently to the modulation. In general, indices decreased with stronger alpha-band activity, but the rate of change was different with SE showing the strongest change (9% per step) and PSI and BIS (<5% per step) showing the weakest change. A simple regression analysis revealed a decrease of 0.02 to 0.09 index points with increasing alpha amplification. While the alpha-band in the intraoperative EEG seems to carry information regarding the quality of anesthesia, changes in the alpha-band activity do neither strongly nor uniformly influence processed EEG indices. Hence, to assess alpha-oscillatory activity's strength, the user needs to focus on the raw EEG or its spectral representation also displayed on the monitoring systems.

Electroencephalographic Measures of Delirium in the Perioperative Setting: A Systematic Review.

Postoperative delirium (POD) is frequent in older adults and is associated with adverse cognitive and functional outcomes. In the last several decades, there has been an increased interest in exploring tools that easily allow the early recognition of patients at risk of developing POD. The electroencephalogram (EEG) is a widely available tool used to understand delirium pathophysiology, and its use in the perioperative setting has grown exponentially, particularly to predict and detect POD. We performed a systematic review to investigate the use of EEG in the pre-, intra-, and postoperative settings. We identified 371 studies, and 56 met the inclusion criteria. A range of techniques was used to obtain EEG data, from limited 1-4 channel setups to complex 256-channel systems. Power spectra were often measured preoperatively, yet the outcomes were inconsistent. During surgery, the emphasis was primarily on burst suppression (BS) metrics and power spectra, with a link between the frequency and timing of BS, and POD. The EEG patterns observed in POD aligned with those noted in delirium in different contexts, suggesting a reduction in EEG activity. Further research is required to investigate preoperative EEG indicators that may predict susceptibility to delirium.

Emotional and Cognitive "Route" in Decision-Making Process: The Relationship between Executive Functions, Psychophysiological Correlates, Decisional Styles, and Personality.

Studies on decision-making have classically focused exclusively on its cognitive component. Recent research has shown that a further essential component of decisional processes is the emotional one. Indeed, the emotional route in decision-making plays a crucial role, especially in situations characterized by ambiguity, uncertainty, and risk. Despite that, individual differences concerning such components and their associations with individual traits, decisional styles, and psychophysiological profiles are still understudied. This pilot study aimed at investigating the relationship between individual propensity toward using an emotional or cognitive information-processing route in decision-making, EEG and autonomic correlates of the decisional performance as collected via wearable non-invasive devices, and individual personality and decisional traits. Participants completed a novel task based on realistic decisional scenarios while their physiological activity (EEG and autonomic indices) was monitored. Self-report questionnaires were used to collect data on personality traits, individual differences, and decisional styles. Data analyses highlighted two main findings. Firstly, different personality traits and decisional styles showed significant and specific correlations, with an individual propensity toward either emotional or cognitive information processing for decision-making. Secondly, task-related EEG and autonomic measures presented a specific and distinct correlation pattern with different decisional styles, maximization traits, and personality traits, suggesting different latent profiles.

Electroencephalogram Alpha Oscillations in Stroke Recovery: Insights into Neural Mechanisms from Combined Transcranial Direct Current Stimulation and Mirror Therapy in Relation to Activities of Daily Life.

The goal of stroke rehabilitation is to establish a robust protocol for patients to live independently in community. Firstly, we examined the impact of 3 hybridized transcranial direct current stimulation (tDCS)-mirror therapy interventions on activities of daily life (ADL) in stroke patients. Secondly, we explored the underlying therapeutic mechanisms with theory-driven electroencephalography (EEG) indexes in the alpha band. This was achieved by identifying the unique contributions of alpha power in motor production to ADL in relation to the premotor cortex (PMC), primary cortex (M1), and Sham tDCS with mirror therapy. The results showed that, although post-intervention ADL improvement was comparable among the three tDCS groups, one of the EEG indexes differentiated the interventions. Neural-behavioral correlation analyses revealed that different types of ADL improvements consistently corresponded with alpha power in the temporal lobe exclusively in the PMC tDCS group (all rs > 0.39). By contrast, alterations in alpha power in the central-frontal region were found to vary, with ADL primarily in the M1 tDCS group (r = -0.6 or 0.7), with the benefit depending on the complexity of the ADL. In conclusion, this research suggested two potential therapeutic mechanisms and demonstrated the additive benefits of introducing theory-driven neural indexes in explaining ADL.

OTHR-11. TITLE: ELECTROGRAPHIC SEIZURES AND EEG BACKGROUND FEATURES IN PEDIATRIC NEURO-ONCOLOGY PATIENTS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT

Abstract BACKGROUND Electrographic seizures (ES) are common in critically ill children with the incidence of ES ranging from 10-50%. Continuous EEG (cEEG) is frequently obtained in children with brain tumors due to encephalopathy or concern for seizures. Despite this, little is known about the incidence of ES, EEG background features, or the impact of EEG on clinical management in this population. METHODS This is a prospective single center study of critically ill children with brain tumors admitted to the pediatric intensive care unit (PICU) from July 2021 to July 2023. Patients with new or known primary brain tumors and cEEG completed during their admission were included. We excluded patients with planned admissions for EEG. Clinical variables included age, sex, oncologic diagnosis, tumor grade and location, indication for EEG, EEG features, and changes in management. RESULTS Thirty-six patients were identified who met the inclusion criteria. 21 patients were female. The median age was 11.0 years (IQR 6.6– 15.4 years). The most common indication for EEG was encephalopathy in 42% followed by clinical seizure in 31%. ES were captured in 19% (7/36) with the majority being subclinical; in patients with acute encephalopathy, ES were captured in 20% (3/15). The EEG background was abnormal in 97% of cases. The most common EEG background was slow disorganized with background asymmetry seen in in 74%. EEG background features met criteria for the ictal-interictal continuum in 14%. A higher rate of ES was seen in patients with disseminated tumors (p=0.03) and severe encephalopathy on EEG (p=0.006). EEG led to a change in management in 31% of patients. CONCLUSIONS ES are common in critically ill children with brain tumors and would go undetected without cEEG. EEG findings often led to a change in clinical management and is a vital tool for the evaluation of this population.

The N270 as an index of consumer commodity color preference in the S1-S2 paradigm.

Affective decision-making is a prominent topic in consumer psychology research, with its core assumption being that consumers tend to purchase brands and commodities they like. However, the reasons behind why we develop emotional responses of liking or disliking toward certain commodities, as well as what the underlying neural mechanisms are, remain largely unknown. This study utilized the S1-S2 paradigm in an experiment wherein S1 presented 12 types of commodities and S2 displayed 48 distinct colored squares. Participants were instructed to assess whether they "Like" or "Dislike" the commodity in S1, which was colored with the S2 color. Electroencephalogram (EEG) recordings were obtained during the reaction process and subsequently analyzed to examine the components of S2-induced event-related potentials (ERPs). The analysis revealed that S2 elicited a significant N270 in the prefrontal scalp area under both the "Like" and "Dislike" conditions. Interestingly, the amplitude of the N270 was significantly higher during the "Dislike" condition compared to the "Like" condition. The N270 component was shown to reflect the conflict in physical attributes between S1 and S2, as well as the conflict between commodity and color. This highlights the potential utility of this component as an objective EEG indicator of consumer commodity color preferences in future marketing research.

Exploring the influence of subjective thermal perception on electroencephalogram characteristics based on prior thermal experience

Achieving thermal comfort for occupants in a dynamic environment that reflects their previous thermal experiences is essential for enhancing satisfaction. Current models do not fully consider these dynamic elements, thus limiting advancements in occupants' thermal comfort. This study aims to delineate the characteristics of electroencephalogram (EEG) responses aligned with subjective thermal perception in various thermal environment scenarios, inclusive of past thermal experiences. It focuses on identifying EEG indices, brain regions, and temporal markers that can distinguish between different thermal perceptions. The study involves sixteen male participants who undergo three thermal experience scenarios: predicted mean vote (PMV) 0 → PMV +2, PMV +2 → PMV –2, and PMV -2 → PMV +2. Our results highlight the sensitivity of the F3 channel in the left frontal lobe to thermal environments. The study identifies a pivotal time frame of 6 min after the experiment's start as significant for differentiating thermal perception groups. Additionally, it reveals that the EEG indices reacting to thermal changes vary depending on the participants' previous thermal experiences. These findings indicate that physiological signals, particularly EEG responses, can serve as effective biomarkers for distinguishing thermal perceptions in diverse thermal experience scenarios. This research emphasizes the need to integrate a variety of thermal environment scenarios, considering previous thermal experiences, into personal comfort models for more effective and tailored thermal comfort solutions.

The Neurocritical Care Examination and Workup.

This article provides an overview of the evaluation of patients in neurocritical care settings and a structured approach to recognizing and localizing acute neurologic emergencies, performing a focused examination, and pursuing workup to identify critical findings requiring urgent management. After identifying and stabilizing imminent threats to survival, including respiratory and hemodynamic compromise, the initial differential diagnosis for patients in neurocritical care is built on a focused history and clinical examination, always keeping in mind critical "must-not-miss" pathologies. A key priority is to identify processes warranting time-sensitive therapeutic interventions, including signs of elevated intracranial pressure and herniation, acute neurovascular emergencies, clinical or subclinical seizures, infections of the central nervous system, spinal cord compression, and acute neuromuscular respiratory failure. Prompt neuroimaging to identify structural abnormalities should be obtained, complemented by laboratory findings to assess for underlying systemic causes. The indication for EEG and lumbar puncture should be considered early based on clinical suspicion. In neurocritical care, the initial evaluation is often fast paced, requiring assessment and management to happen in parallel. History, clinical examination, and workup should be obtained while considering therapeutic implications and the need for lifesaving interventions.

Predicting brain edema and outcomes after thrombectomy in stroke: Frontal delta/alpha ratio as an optimal quantitative EEG index

ObjectiveWe aimed to determine whether quantitative electroencephalography (QEEG) measures have predictive value for cerebral edema (CED) and clinical outcomes in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion who underwent mechanical thrombectomy (MT). MethodsA total of 105 patients with AIS in the anterior circulation were enrolled in this prospective study. The occurrence and severity of CED were assessed through computed tomography conducted 24 h after MT. Clinical outcomes were evaluated based on early neurological deterioration (END) and 3-month functional status, as measured by the modified Rankin scale (mRS). Electroencephalography (EEG) recordings were performed 24 h after MT, and QEEG indices were calculated from the standard 16 electrodes and 2 frontal channels (F3-C3, F4-C4). The delta/alpha ratio (DAR), the (delta + theta) / (alpha + beta) ratio (DTABR), and relative delta power were averaged over all electrodes (global) and the F3-C3 and F4-C4 channels (frontal). The predictive effect and value of QEEG indices for CED and clinical outcomes were assessed using ordinal and logistic regression models, as well as receiver operating characteristic (ROC) curves. ResultsSignificantly, both global and frontal DAR were found to be associated with the severity of CED, END, and poor functional outcomes at 90 days, while global and frontal DTABR and relative delta power were not associated with outcomes. In ROC analysis, the best predictive effect was observed in frontal DAR, with an area under the curve of approximately 0.80. It exhibited approximately 75% sensitivity and 71% specificity for radiological and clinical outcomes when a threshold of 3.3 was used. ConclusionsQEEG techniques may be considered an efficient bedside monitoring method for assessing treatment efficacy, identifying patients at higher risk of severe CED and END, and predicting long-term functional outcomes. SignificanceQEEG can help identify patients at risk of severe neurological complications that can impact long-term functional recovery in AIS patients who underwent MT.

The influence of wakefulness fluctuations on brain networks involved in centrotemporal spike occurrence

ObjectiveDrowsiness has been implicated in the modulation of centro-temporal spikes (CTS) in Self-limited epilepsy with Centro-Temporal Spikes (SeLECTS). Here, we explore this relationship and whether fluctuations in wakefulness influence the brain networks involved in CTS generation. MethodsFunctional MRI (fMRI) and electroencephalography (EEG) was simultaneously acquired in 25 SeLECTS. A multispectral EEG index quantified drowsiness (‘EWI’: EEG Wakefulness Index). EEG (Pearson Correlation, Cross Correlation, Trend Estimation, Granger Causality) and fMRI (PPI: psychophysiological interactions) analytic approaches were adopted to explore respectively: (a) the relationship between EWI and changes in CTS frequency and (b) the functional connectivity of the networks involved in CTS generation and wakefulness oscillations. EEG analyses were repeated on a sample of routine EEG from the same patient’s cohort. ResultsNo correlation was found between EWI fluctuations and CTS density during the EEG-fMRI recordings, while they showed an anticorrelated trend when drowsiness was followed by proper sleep in routine EEG traces. According to PPI findings, EWI fluctuations modulate the connectivity between the brain networks engaged by CTS and the left frontal operculum. ConclusionsWhile CTS frequency per se seems unrelated to drowsiness, wakefulness oscillations modulate the connectivity between CTS generators and key regions of the language circuitry, a cognitive function often impaired in SeLECTS. SignificanceThis work advances our understanding of (a) interaction between CTS occurrence and vigilance fluctuations and (b) possible mechanisms responsible for language disruption in SeLECTS.