Search

As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Background: Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored., Methods: Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed., Findings: Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions., Conclusion: These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations., (© 2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

Equitable access to care and management is a priority for patients with epilepsy and may vary depending on each country's healthcare system. As this issue has not been specifically addressed in France, we conducted a retrospective study to identify discriminating factors in access to surgery at a French tertiary epilepsy center. Initially, we examined factors previously identified in other countries as influential in surgery access, including age at diagnosis, affected side, gender, years of education, socio-professional categories, and density of general practitioners in the residential area, in 293 consecutive French-native patients with refractory medial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS). Subsequently, we conducted a case-control study comparing patients born in France with 22 patients born abroad to specifically explore migratory status. The analysis revealed that the only three factors statistically influencing the delay between the onset of epilepsy and entry into video-EEG were early age at onset (associated with a longer delay), pensioner status (associated with a longer delay), and student status (associated with a shorter delay). Migratory status, gender, and socio-economic level (indirectly reflected by the level of education and socio-professional category) were not found to be discriminatory factors in access to video-EEG. Discrepancies between our study and foreign studies may be attributed to differences in healthcare systems and medical coverage among countries. Efforts in France to improve access to surgery should focus on enhancing communication among practitioners to promptly refer any MTLE-HS patient to an epilepsy surgery center, regardless of their age., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition ( EMD ) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions ( IMF ), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy.

Nowadays, electroencephalogram (EEG) signals are the major part of recognizing emotions as the signals contain more brain-related information. Several traditional recognition methodologies have been developed, but still, exist with certain challenges that degrade and mislead the recognition process. Some models are facing issues based on insignificant features, poor generalization capability, and computational burden. To alleviate such issues, an effective emotion recognition model is proposed using a heuristic-aided transformer-based approach. Initially, the input EEG signals are gathered and decomposed via the 5-level Discrete Wavelet Transform (DWT). Then, the decomposed signals are fed to the feature extraction process, where the feature extraction is carried out by using Logistic Regression (LR) approach. From the decomposed signals, the Logistic Regression (LR) is used for getting the noteworthy features and then, the same decomposed signals are fed to the optimal weighted feature selection, which is carried out using Adaptive Bald Eagle Search Optimization (ABESO) algorithm. Subsequently, the hybrid weighted feature selection approach is introduced for getting the weighted features, where the weight is optimized with the adoption of the ABESO algorithm. At last, the selected hybrid weighted features are sent to the recognition phase, where the Optimized Block Recurrent Transformer (OBRT), where performance gets enhanced by tuning the parameter with the developed ABESO algorithm. The accuracy and precision rate of the designed approach are attained as 96% and 94%. The performance of the model is assessed and the findings have shown that better performance that makes the system more feasible to recognize human emotions.

Objective: We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work., Methods: This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model., Results: After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979)., Conclusion: A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge., Clinical Implication: This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

An Auditory Steady-State Response (ASSR) is a valuable tool for determining auditory thresholds in individuals who are either unable or unwilling to cooperate with conventional behavioral testing methods. This study proposes a sequential test technique for automatic detection of ASSRs, incorporating a non-detection stopping criterion. The electrophysiological thresholds of a normal hearing volunteer were established using data collected from multichannel EEG signals. The detection probabilities and critical values were obtained via Monte Carlo simulations. Remarkably, application of the non-detection stopping criterion resulted in a 60% reduction in exam time in the absence of a response. These findings clearly demonstrate the significant potential of the sequential test in enhancing the performance of automatic audiometry.

Introduction: Prognostication of outcome in severe stroke patients necessitating invasive mechanical ventilation poses significant challenges. The objective of this study was to assess the prognostic significance and prevalence of early electroencephalogram (EEG) abnormalities in adult stroke patients receiving mechanical ventilation., Methods: This study is a pre-planned ancillary investigation within the prospective multicenter SPICE cohort study (2017-2019), conducted in 33 intensive care units (ICUs) in the Paris area, France. We included adult stroke patients requiring invasive mechanical ventilation, who underwent at least one intermittent EEG examination during their ICU stay. The primary endpoint was the functional neurological outcome at one year, determined using the modified Rankin scale (mRS), and dichotomized as unfavorable (mRS 4-6, indicating severe disability or death) or favorable (mRS 0-3). Multivariable regression analyses were employed to identify EEG abnormalities associated with functional outcomes., Results: Of the 364 patients enrolled in the SPICE study, 153 patients (49 ischemic strokes, 52 intracranial hemorrhages, and 52 subarachnoid hemorrhages) underwent at least one EEG at a median time of 4 (interquartile range 2-7) days post-stroke. Rates of diffuse slowing (70% vs. 63%, p = 0.37), focal slowing (38% vs. 32%, p = 0.15), periodic discharges (2.3% vs. 3.7%, p = 0.9), and electrographic seizures (4.5% vs. 3.7%, p = 0.4) were comparable between patients with unfavorable and favorable outcomes. Following adjustment for potential confounders, an unreactive EEG background to auditory and pain stimulations (OR 6.02, 95% CI 2.27-15.99) was independently associated with unfavorable outcomes. An unreactive EEG predicted unfavorable outcome with a specificity of 48% (95% CI 40-56), sensitivity of 79% (95% CI 72-85), and positive predictive value (PPV) of 74% (95% CI 67-81). Conversely, a benign EEG (defined as continuous and reactive background activity without seizure, periodic discharges, triphasic waves, or burst suppression) predicted favorable outcome with a specificity of 89% (95% CI 84-94), and a sensitivity of 37% (95% CI 30-45)., Conclusion: The absence of EEG reactivity independently predicts unfavorable outcomes at one year in severe stroke patients requiring mechanical ventilation in the ICU, although its prognostic value remains limited. Conversely, a benign EEG pattern was associated with a favorable outcome., (© 2024. The Author(s).)

We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Objective: To assess, in adults with acute consciousness impairment, the impact of latency between hospital admission and EEG recording start, and their outcome., Methods: We reviewed data of the CERTA trial (NCT03129438) and explored correlations between EEG recording latency and mortality, Cerebral Performance Categories (CPC), and modified Rankin Scale (mRS) at 6 months, considering other variables, using uni- and multivariable analyses., Results: In univariable analysis of 364 adults, median latency between admission and EEG recordings was comparable between surviving (61.1 h; IQR: 24.3-137.7) and deceased patients (57.5 h; IQR: 22.3-141.1); p = 0.727. This did not change after adjusting for potential confounders, such as lower Glasgow Coma Score on enrolment (p < 0.001) and seizure or status epilepticus detection (p < 0.001). There was neither any correlation between EEG latency and mRS (rho 0.087, p 0.236), nor with CPC (rho = 0.027, p = 0.603)., Conclusion: This analysis shows no correlation between delays of EEG recordings and mortality or functional outcomes at 6 months in critically ill adults., Significance: These findings might suggest that in critically ill adults mortality correlates with underlying brain injury rather than EEG delay., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease., Competing Interests: The authors have declared that no competing interests exist.

Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Siyuan Zang et al.)

The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the past decades. Recently, deep learning-based detection methods which automatically learn features from the EEG signals have attracted much attention. However, with deep learning-based detection methods, different input formats of EEG signals will lead to different detection performances. In this paper, we propose a deep learning-based epileptic seizure detection method with hybrid input formats of EEG signals, i.e., original EEG, Fourier transform of EEG, short-time Fourier transform of EEG, and wavelet transform of EEG. Convolutional neural networks (CNNs) are designed for extracting latent features from these inputs. A feature fusion mechanism is applied to integrate the learned features to generate a more stable syncretic feature for seizure detection. The experimental results show that our proposed hybrid method is effective to improve the seizure detection performance in few-shot scenarios., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yayan Pan et al.)

Methods: We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS., Result: The best result is that the model's accuracy of predicting BLS on the test set has an overall upward trend, eventually reaching more than 90%. The overall error shows a gradual decrease and eventually approaches zero. The worst result is that the model's accuracy of predicting BIS on the test set has an overall upward trend. The accuracy rate is relatively stable without major fluctuations, but the final accuracy rate is above 70%., Conclusion: The prediction of BIS indicators by the deep learning method CNN algorithm shows good results in statistics., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Yachao Wang et al.)

To analyze the application value of artificial intelligence model based on Visual Geometry Group- (VGG-) 16 combined with quantitative electroencephalography (QEEG) in cerebral small vessel disease (CSVD) with cognitive impairment, 72 patients with CSVD complicated by cognitive impairment were selected as the research subjects. As per Diagnostic and Statistical Manual (5th Edition), they were divided into the vascular dementia (VD) group of 34 cases and vascular cognitive impairment with no dementia (VCIND) group of 38 cases. The two groups were analyzed for the clinical information, neuropsychological test results, and monitoring results of QEEG based on intelligent algorithms for more than 2 hours. The accuracy rate of VGG was 84.27% and Kappa value was 0.7, while that of modified VGG (nVGG) was 88.76% and Kappa value was 0.78. The improved VGG algorithm obviously had higher accuracy. The test results found that the QEEG identified 8 normal, 19 mild, 10 moderate, and 0 severe cases in the VCIND group, while in the VD group, the corresponding numbers were 4, 13, 11, and 7; in the VCIND group, 7 cases had the normal QEEG, 11 cases had background changes, 9 cases had abnormal waves, and 11 cases had in both background changes and abnormal waves, and in the VD group, the corresponding numbers were 5, 2, 5, and 22, respectively; in the VCIND group, QEEG of 18 patients had no abnormal waves, QEEG of 11 patients had a few abnormal waves, and QEEG of 9 patients had many abnormal waves, and QEEG of 0 people had a large number of abnormal waves, and in the VD group, the corresponding numbers were 7, 6, 12, and 9. The above data were statistically different between the two groups ( P < 0.05). Hence, QEEG based on intelligent algorithms can make a good assessment of CSVD with cognitive impairment, which had good clinical application value., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Hengya Zhu et al.)

Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to "pops" in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Milind Natu et al.)

Background: Neuromonitoring is the use of continuous measures of brain physiology to detect clinically important events in real-time. Neuromonitoring devices can be invasive or non-invasive and are typically used on patients with acute brain injury or at high risk for brain injury. The goal of this study was to characterize neuromonitoring infrastructure and practices in North American pediatric intensive care units (PICUs)., Methods: An electronic, web-based survey was distributed to 70 North American institutions participating in the Pediatric Neurocritical Care Research Group. Questions related to the clinical use of neuromonitoring devices, integrative multimodality neuromonitoring capabilities, and neuromonitoring infrastructure were included. Survey results were presented using descriptive statistics., Results: The survey was completed by faculty at 74% (52 of 70) of institutions. All 52 institutions measure intracranial pressure and have electroencephalography capability, whereas 87% (45 of 52) use near-infrared spectroscopy and 40% (21/52) use transcranial Doppler. Individual patient monitoring decisions were driven by institutional protocols and collaboration between critical care, neurology, and neurosurgery attendings. Reported device utilization varied by brain injury etiology. Only 15% (eight of 52) of institutions utilized a multimodality neuromonitoring platform to integrate and synchronize data from multiple devices. A database of neuromonitoring patients was maintained at 35% (18 of 52) of institutions. Funding for neuromonitoring programs was variable with contributions from hospitals (19%, 10 of 52), private donations (12%, six of 52), and research funds (12%, six of 52), although 73% (40 of 52) have no dedicated funds., Conclusions: Neuromonitoring indications, devices, and infrastructure vary by institution in North American pediatric critical care units. Noninvasive modalities were utilized more liberally, although not uniformly, than invasive monitoring. Further studies are needed to standardize the acquisition, interpretation, and reporting of clinical neuromonitoring data, and to determine whether neuromonitoring systems impact neurological outcomes., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The outdoor light environment significantly affects aspects of public psychological and physiological health. This study conducted experiments to quantify the effects of the light environment on visitor light comfort in urban park pedestrian space. Nine sets of lighting conditions with different average horizontal illuminance (2 lx, 6 lx, 10 lx) and colour temperatures (5600 K, 4300 K, 3000 K) were established virtual reality scenarios. Subjective light comfort was evaluated, and electroencephalogram (EEG) was measured on 18 subjects to comprehensively study the effects of different light environments on human light comfort. The results of the comprehensive evaluation showed that colour temperature had a very significant impact on subjective light comfort, with warm light being generally more favourable than cool light in enhancing human subjective light comfort. The results of the EEG analysis show that the average horizontal illuminance is an important factor in the level of physiological fatigue, and that physiological fatigue can be maintained in a superior state at an appropriate level of illuminance. Based on the results of both subjective and objective factors, a comprehensive analysis was carried out to propose a range of average horizontal illuminance (4.08 lx, 6.99 lx) and a range of colour temperature (3126 K, 4498 K) for the comprehensive light comfort zone in urban park pedestrian space., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2021 Jun Zhang and Wenhan Dai.)

Objectives: The purpose of this study was to examine critical care continuous electroencephalography (cEEG) utilization and downstream anti-seizure treatment patterns, their association with outcomes, and generate hypotheses for larger comparative effectiveness studies of cEEG-guided interventions., Methods: Single-center retrospective study of critically ill patients (n = 14,523, age ≥18 years). Exposure defined as ≥24 h of cEEG and subsequent anti-seizure medication (ASM) escalation, with or without concomitant anesthetic. Exposure window was the first 7 days of admission. Primary outcome was in-hospital mortality. Multivariable analysis was performed using penalized logistic regression., Results: One thousand and seventy-three patients underwent ≥24 h of cEEG within 7 days of admission. After adjusting for disease severity, ≥24 h of cEEG followed by ASM escalation in patients not on anesthetics (n = 239) was associated with lower in-hospital mortality (OR 0.76 [0.53-1.07]), though the finding did not reach significance. ASM escalation with concomitant anesthetic use (n = 484) showed higher odds for mortality (OR 1.41 [1.03-1.94]). In the seizures/status epilepticus subgroup, post cEEG ASM escalation without anesthetics showed lower odds for mortality (OR 0.43 [0.23-0.74]). Within the same subgroup, ASM escalation with concomitant anesthetic use showed higher odds for mortality (OR 1.34 [0.92-1.91]) though not significant., Interpretation: Based on our findings we propose the following hypotheses for larger comparative effectiveness studies investigating the direct causal effect of cEEG-guided treatment on outcomes: (1) cEEG-guided ASM escalation may improve outcomes in critically ill patients with seizures; (2) cEEG-guided treatment with combination of ASMs and anesthetics may not improve outcomes in all critically ill patients., (© 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the EEG data. Thus, the adaptability of EEG classification methods has become significant. This study proposed a deep network model for autonomous learning and classification of EEG signals, which could self-adaptively classify EEG signals with different sampling frequencies and lengths. The artificial design feature extraction methods could not obtain stable classification results when analyzing EEG data with different sampling frequencies. However, the proposed depth network model showed considerably better universality and classification accuracy, particularly for EEG signals with short length, which was validated by two datasets., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Tingxi Wen et al.)

Motor imagery modulates specific neural oscillations like actual movement does. Representatively, suppression of the alpha power (e.g., event-related desynchronization [ERD]) is the typical pattern of motor imagery in the motor cortex. However, in addition to this amplitude-based feature, the coupling across frequencies includes important information about the brain functions and the existence of such complex information has been reported in various invasive studies. Yet, the interaction across multiple frequencies during motor imagery processing is still unclear and has not been widely studied, particularly concerning the non-invasive signals. In this study, we provide empirical evidence of the comodulation between the phase of alpha rhythm and the amplitude of high gamma rhythm during the motor imagery process. We used electroencephalography (EEG) in our investigation during the imagination of left- or right-hand movement recorded from 52 healthy subjects, and quantified the ERD of alpha and phase-amplitude coupling (PAC) which is a relative change of modulation index to the base line period (before the cue). As a result, we found that the coupling between the phase of alpha (8-12 Hz) and the amplitude of high gamma (70-120 Hz) and this PAC decreases during motor imagery and then rebounds to the baseline like alpha ERD (r = 0.29 to 0.42). This correlation between PAC and ERD was particularly stronger in the ipsilateral area. In addition, trials that demonstrated higher alpha power during the ready period (before the cue) showed a larger ERD during motor imagery and similarly, trials with higher modulation index during the ready period yielded a greater decrease in PAC during imagery. In the classification analysis, we found that the effective phase frequency that showed better decoding accuracy in left and right-hand imagery, varied across subjects. Motivated by result, we proposed a weighted cross-frequency coupling (WCFC) method that extracts the maximal discriminative feature by combining band power and CFC. In the evaluation, WCFC with only two electrodes yielded a performance comparable to the conventional algorithm with 64 electrodes in classifying left and right-hand motor imagery. These results indicate that the phase-amplitude frequency plays an important role in motor imagery, and that optimizing this frequency ranges is crucial for extracting information features to decode the motor imagery types., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Bo Pan and Wei Zheng.)

A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI's poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world., Competing Interests: The authors have declared that no competing interests exist.

Background: In intensive care, monitoring the depth of anesthesia during surgical procedures is a key element in the success of the medical operation and postoperative recovery. However, despite the development of anesthesia thanks to technological and pharmacological advances, its side effects such as underdose or overdose of hypnotics remain a major problem. Observation and monitoring must combine clinical observations (loss of consciousness and reactivity) with tools for real-time measurement of changes in the depth of anesthesia. Methodology . In this work, we will develop a noninvasive method for calculating, monitoring, and controlling the depth of general anesthesia during surgery. The objective is to reduce the effects of pharmacological usage of hypnotics and to ensure better quality recovery. Thanks to the overall activity of sets of neurons in the brain, we have developed a BIS technique based on bispectral analysis of the electroencephalographic signal EEG. Discussion . By collecting the electrical voltages from the brain, we distinguish light sleep from deep sleep according to the values of the BIS indicator (ranging from 0 : sleep to 100 : wake) and also control it by acting on the dosage of propofol and sevoflurane. We showed that the BIS value must be maintained during the operation and the anesthesia at a value greater than 60., Conclusion: This study showed that the BIS technology led to an optimization of the anesthetic management, the adequacy of the hypnotic dosage, and a better postoperative recovery., Competing Interests: The author declares that they have no conflicts of interest., (Copyright © 2021 Lamia Bouafif.)

The human brain tracks amplitude fluctuations of both speech and music, which reflects acoustic processing in addition to the encoding of higher-order features and one's cognitive state. Comparing neural tracking of speech and music envelopes can elucidate stimulus-general mechanisms, but direct comparisons are confounded by differences in their envelope spectra. Here, we use a novel method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening. We expected to see music reconstruction match speech in a narrow range of frequencies, but instead we found that speech was reconstructed better than music for all frequencies we examined. Additionally, models trained on all stimulus types performed as well or better than the stimulus-specific models at higher modulation frequencies, suggesting a common neural mechanism for tracking speech and music. However, speech envelope tracking at low frequencies, below 1 Hz, was associated with increased weighting over parietal channels, which was not present for the other stimuli. Our results highlight the importance of low-frequency speech tracking and suggest an origin from speech-specific processing in the brain., Competing Interests: The authors have declared that no competing interests exist.

The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real-life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time- and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave-one subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention ( ) and 5.45% for focused attention ( ). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% ( ). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.

Abstract: This study aimed to investigate evidence of gray matter brain lesions in multiple sclerosis (MS) patients by evaluating the resting state alpha rhythm of brain electrical activity.The study included 50 patients diagnosed with MS recruited from the MS clinic with 50 age and gender-matched control participants. The study investigated parameters of posterior dominant rhythm (PDR) in the electroencephalography (EEG) recordings including wave frequency and amplitude. Functional disability among the patients was evaluated according to the expanded disability status scale. Univariate statistical analysis was completed using one-way analysis of variance and t test with a P value of less than .05 to indicate statistical significance.Patients with MS had significantly lower PDR frequency and amplitude values compared to the controls (P value < .01) and 34% of the MS patients had a PDR frequency of less than 8.5 Hz. The PDR frequency was negatively associated with the level of functional disability among the patients (P value <.001) and 4% of the patients had abnormal epileptiform discharges.Background slowing of resting alpha rhythms and epileptiform discharges are suggestive of gray matter degeneration and may help in the prediction and follow-up of cortical damage and functional disabilities among MS patients. Therefore, electroencephalography monitoring of the PDR spectrum may serve as an alternative or complementary tool with other imaging techniques to detect and monitor cerebral cortical lesions., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

People with Alzheimer's disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies., Competing Interests: The authors have declared that no competing interests exist.

Objective: Children with infantile spasms may develop Lennox-Gastaut syndrome. The diagnostic criteria for Lennox-Gastaut syndrome are vague, and many experts use varying combinations of the following criteria for diagnosis: paroxysmal fast activity on electroencephalography (EEG), slow spike and wave on EEG, developmental delay, multiple seizure types, and nocturnal tonic seizures. Our objective was to determine the prevalence of Lennox-Gastaut syndrome in a high-risk cohort of children with a history of infantile spasms and the characteristics of infantile spasms that were associated with the diagnosis of Lennox-Gastaut syndrome., Methods: Children with infantile spasms who were diagnosed and treated at Children's Hospital Colorado between 2012 and 2018 were included. Lennox-Gastaut syndrome was defined as having 3 of 5 of the following characteristics: paroxysmal fast activity, slow spike and wave, current developmental delay, multiple seizure types, or tonic seizures. Descriptive statistics were performed using median and interquartile range. Univariable analysis was performed with Pearson chi-square, Fisher exact, or the Kruskal-Wallis test., Results: Ninety-seven children met inclusion criteria, and 36% (35/97) met criteria for Lennox-Gastaut syndrome. Developmental delay and history of seizures prior to the onset of infantile spasms were identified as risk factors for the development of Lennox-Gastaut syndrome ( P = .003) as was poor response to first treatment for spasms ( P = .004). Children with an unknown etiology of infantile spasms were less likely to develop Lennox-Gastaut syndrome ( P = .019). Eighty percent (28/35) of the children who met Lennox-Gastaut syndrome criteria lacked a documented diagnosis., Conclusions: Thirty-six percent of children with infantile spasms met criteria for Lennox-Gastaut syndrome. Risk factors for development of Lennox-Gastaut syndrome were developmental delay and seizures prior to the onset of infantile spasms and poor response to first treatment for infantile spasms. Children with an unknown etiology of infantile spasms were less likely to develop Lennox-Gastaut syndrome. Eighty percent of the children who met our criteria were not given a documented diagnosis of Lennox-Gastaut syndrome, which highlights the fact that many children may not be receiving a diagnosis of Lennox-Gastaut syndrome. We recommend establishing clear guidelines for the diagnosis of Lennox-Gastaut syndrome to ensure that the diagnosis is being made accurately.

A retrospective cohort analysis was performed on 79 consecutive patients between 6 months and 5 years admitted to a tertiary hospital with a diagnosis of complex febrile seizures requiring mechanical ventilation from 2011 to 2017 to determine the utility of infectious and neurologic diagnostics. Intubation was used as a proxy for severity of illness. The overall intensive care unit stay was short (95% intubated <24 hours, 88% admitted <3 days). No life-threatening infections were identified, and none required surgical interventions. Electroencephalogram (EEG) was obtained on 43%, 26% of which were abnormal. Sixty-six percent of patients were discharged on rescue benzodiazepine and 20% with maintenance antiseizure medications. Duration of follow-up averaged 4 years (range 1 month to 9 years); 8 patients (10%) were subsequently diagnosed with epilepsy. Our findings suggest that extensive diagnostic evaluations may not be necessary for children with complex febrile seizures requiring mechanical ventilation although the role of EEG is less understood.

Finding effective and objective biomarkers to inform the diagnosis of schizophrenia is of great importance yet remains challenging. Relatively little work has been conducted on multi-biological data for the diagnosis of schizophrenia. In this cross-sectional study, we extracted multiple features from three types of biological data, including gut microbiota data, blood data, and electroencephalogram data. Then, an integrated framework of machine learning consisting of five classifiers, three feature selection algorithms, and four cross validation methods was used to discriminate patients with schizophrenia from healthy controls. Our results show that the support vector machine classifier without feature selection using the input features of multi-biological data achieved the best performance, with an accuracy of 91.7% and an AUC of 96.5% (p < 0.05). These results indicate that multi-biological data showed better discriminative capacity for patients with schizophrenia than single biological data. The top 5% discriminative features selected from the optimal model include the gut microbiota features (Lactobacillus, Haemophilus, and Prevotella), the blood features (superoxide dismutase level, monocyte-lymphocyte ratio, and neutrophil count), and the electroencephalogram features (nodal local efficiency, nodal efficiency, and nodal shortest path length in the temporal and frontal-parietal brain areas). The proposed integrated framework may be helpful for understanding the pathophysiology of schizophrenia and developing biomarkers for schizophrenia using multi-biological data., (© 2021. The Author(s).)

Background: The distribution of pediatric-onset morphea and site-based likelihood for extracutaneous complications has not been well characterized., Objective: To characterize the lesional distribution of pediatric-onset morphea and to determine the sites with the highest association of extracutaneous manifestations., Methods: A retrospective cross-sectional study was performed. Using clinical photographs, morphea lesions were mapped onto body diagrams using customized software., Results: A total of 823 patients with 2522 lesions were included. Lesions were more frequent on the superior (vs inferior) anterior aspect of the head and extensor (vs flexor) extremities. Linear morphea lesions were more likely on the head and neck, whereas plaque and generalized morphea lesions were more likely on the trunk. Musculoskeletal complications were more likely with lesions on the extensor (vs flexor) extremity (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.2-3.4), whereas neurologic manifestations were more likely with lesions on the anterior (vs posterior) (OR, 2.8; 95% CI, 1.7-4.6) and superior (vs inferior) aspect of the head (OR, 2.3; 95% CI, 1.6-3.4)., Limitations: Retrospective nature and the inclusion of only patients with clinical photographs., Conclusion: The distribution of pediatric-onset morphea is not random and varies with body site and within individual body sites. The risk stratification of extracutaneous manifestations by body site may inform decisions about screening for extracutaneous manifestations, although prospective studies are needed., Competing Interests: Conflict of interest None disclosed., (Copyright © 2021 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.)

In recent decades reported findings regarding gender differences in reading achievement, cognitive abilities and maturation process in boys and girls are conflicting. As reading is one of the most important processes in the maturation of an individual, the aim of the study was to better understand gender differences between primary school students. The study evaluates differences in Heart Rate Variability (HRV), Electroencephalography (EEG), Electrodermal Activities (EDA) and eye movement of participants during the reading task. Taking into account that colour may affect reading skills, in that it affects the emotional and physiological state of the body, the research attempts to provide a better understanding of gender differences in reading through examining the effect of colour, as applied to reading content. The physiological responses of 50 children (25 boys and 25 girls) to 12 different background and overlay colours of reading content were measured and summarised during the reading process. Our findings show that boys have shorter reading duration scores and a longer Saccade Count, Saccade Duration Total, and Saccade Duration Average when reading on a coloured background, especially purple, which could be caused by their motivation and by the type of reading task. Also, the boys had higher values for the Delta band and the Whole Range of EEG measurements in comparison to the girls when reading on coloured backgrounds, which could reflect the faster maturation of the girls. Regarding EDA measurements we did not find systematic differences between groups either on white or on coloured/overlay background. We found the most significant differences arose in the HRV parameters, namely (SDNN (ms), STD HR (beats/min), RMSSD (ms), NN50 (beats), pNN50 (%), CVRR) when children read the text on coloured/overlay backgrounds, where the girls showed systematically higher values on HRV measurements in comparison to the boys, mostly with yellow, red, and orange overlay colours., Competing Interests: The authors have declared that no competing interests exist.

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals., Competing Interests: The authors have declared that no competing interests exist.

Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable solution remains unavailable. Here, we report an accurate and broadly applicable data-driven algorithm for dimensionality reduction. The algorithm, which we named 'feature-augmented embedding machine' (FEM), first learns the structure of the data and the inherent characteristics of the data components (such as central tendency and dispersion), denoises the data, increases the separation of the components, and then projects the data onto a lower number of dimensions. We show that the technique is effective at revealing the underlying dominant trends in datasets of protein expression and single-cell RNA sequencing, computed tomography, electroencephalography and wearable physiological sensors.

As one of the key issues in the field of emotional computing, emotion recognition has rich application scenarios and important research value. However, the single biometric recognition in the actual scene has the problem of low accuracy of emotion recognition classification due to its own limitations. In response to this problem, this paper combines deep neural networks to propose a deep learning-based expression-EEG bimodal fusion emotion recognition method. This method is based on the improved VGG-FACE network model to realize the rapid extraction of facial expression features and shorten the training time of the network model. The wavelet soft threshold algorithm is used to remove artifacts from EEG signals to extract high-quality EEG signal features. Then, based on the long- and short-term memory network models and the decision fusion method, the model is built and trained using the signal feature data extracted under the expression-EEG bimodality to realize the final bimodal fusion emotion classification and identification research. Finally, the proposed method is verified based on the MAHNOB-HCI data set. Experimental results show that the proposed model can achieve a high recognition accuracy of 0.89, which can increase the accuracy of 8.51% compared with the traditional LSTM model. In terms of the running time of the identification method, the proposed method can effectively be shortened by about 20 s compared with the traditional method., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Yu Lu et al.)

Due to the complexity of human emotions, there are some similarities between different emotion features. The existing emotion recognition method has the problems of difficulty of character extraction and low accuracy, so the bidirectional LSTM and attention mechanism based on the expression EEG multimodal emotion recognition method are proposed. Firstly, facial expression features are extracted based on the bilinear convolution network (BCN), and EEG signals are transformed into three groups of frequency band image sequences, and BCN is used to fuse the image features to obtain the multimodal emotion features of expression EEG. Then, through the LSTM with the attention mechanism, important data is extracted in the process of timing modeling, which effectively avoids the randomness or blindness of sampling methods. Finally, a feature fusion network with a three-layer bidirectional LSTM structure is designed to fuse the expression and EEG features, which is helpful to improve the accuracy of emotion recognition. On the MAHNOB-HCI and DEAP datasets, the proposed method is tested based on the MATLAB simulation platform. Experimental results show that the attention mechanism can enhance the visual effect of the image, and compared with other methods, the proposed method can extract emotion features from expressions and EEG signals more effectively, and the accuracy of emotion recognition is higher., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Yifeng Zhao and Deyun Chen.)

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k -nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k -nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Morteza Amini et al.)

Understanding the connection between different stimuli and the brain response represents a complex research area. However, the use of mathematical models for this purpose is relatively unexplored. The present study investigates the effects of three different auditory stimuli on cerebral biopotentials by means of mathematical functions. The effects of acoustic stimuli (S1, S2, and S3) on cerebral activity were evaluated by electroencephalographic (EEG) recording on 21 subjects for 20 minutes of stimulation, with a 5-minute period of silence before and after stimulation. For the construction of the mathematical models used for the study of the EEG rhythms, we used the Box-Jenkins methodology. Characteristic mathematical models were obtained for the main frequency bands and were expressed by 2 constant functions, 8 first-degree functions, a second-degree function, a fourth-degree function, 6 recursive functions, and 4 periodic functions. The values obtained for the variance estimator are low, demonstrating that the obtained models are correct. The resulting mathematical models allow us to objectively compare the EEG response to the three stimuli, both between the stimuli itself and between each stimulus and the period before stimulation., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Marius Georgescu et al.)

Background: Intensive care unit (ICU) physicians have extended the minimum alveolar concentration (MAC) to deliver and monitor long-term volatile sedation in critically ill patients. There is limited evidence of MAC's reliability in controlling sedation depth in this setting. We hypothesized that sedation depth, measured by the electroencephalography (EEG)-derived Narcotrend-Index (burst-suppression N&#95;Index 0-awake N&#95;Index 100), might drift downward over time despite constant MAC values., Methods: This prospective single-centre randomized clinical study was conducted at a University Hospital Surgical Intensive Care Unit and included consecutive, postoperative ICU patients fulfilling the inclusion criteria. Patients were randomly assigned to receive uninterrupted inhalational sedation with isoflurane, sevoflurane, or desflurane. The end-expiratory concentration of the anaesthetics and the EEG-derived index were measured continuously in time-stamped pairs. Sedation depth was also monitored using Richmond-Agitation-Sedation-Scale (RASS). The paired t-test and linear models (bootstrapped or multilevel) have been employed to analyze MAC, N&#95;Index and RASS across the three groups., Results: Thirty patients were recruited (female/male: 10/20, age 64 ± 11, Simplified Acute Physiology Score II 30 ± 10). In the first 24 h, 21.208 pairs of data points (N&#95;Index and MAC) were recorded. The median MAC of 0.58 ± 0.06 remained stable over the sedation time in all three groups. The t-test indicated in the isoflurane and sevoflurane groups a significant drop in RASS and EEG-derived N&#95;Index in the first versus last two sedation hours. We applied a multilevel linear model on the entire longitudinal data, nested per patient, which produced the formula N&#95;Index = 43 - 0.7·h (R 2  = 0.76), showing a strong negative correlation between sedation's duration and the N&#95;Index. Bootstrapped linear models applied for each sedation group produced: N&#95;Index of 43-0.9, 45-0.8, and 43-0.4·h for isoflurane, sevoflurane, and desflurane, respectively. The regression coefficient for desflurane was almost half of those for isoflurane and sevoflurane, indicating a less pronounced time-effect in this group., Conclusions: Maintaining constant MAC does not guarantee stable sedation depth. Thus, the patients necessitate frequent clinical assessments or, when unfeasible, continuous EEG monitoring. The differences across different volatile anaesthetics regarding their time-dependent negative drift requires further exploration., Trial Registration: NCT03860129.

Mild, blast-induced traumatic brain injury (mbTBI) is a common combat brain injury characterized by typically normal neuroimaging findings, with unpredictable future cognitive recovery. Traditional methods of electroencephalography (EEG) analysis (e.g., spectral analysis) have not been successful in detecting the degree of cognitive and functional impairment in mbTBI. We therefore collected resting state EEG (5 minutes, 64 leads) from twelve patients with a history of mbTBI, along with repeat neuropsychological testing (D-KEFS Tower test) to compare two new methods for analyzing EEG (multifractal detrended fluctuation analysis (MF-DFA) and information transfer modeling (ITM)) with spectral analysis. For MF-DFA, we extracted relevant parameters from the resultant multifractal spectrum from all leads and compared with traditional power by frequency band for spectral analysis. For ITM, because the number of parameters from each lead far exceeded the number of subjects, we utilized a reduced set of 10 leads which were compared with spectral analysis. We utilized separate 30 second EEG segments for training and testing statistical models based upon regression tree analysis. ITM and MF-DFA models both generally had improved accuracy at correlating with relevant measures of cognitive performance as compared to spectral analytic models ITM and MF-DFA both merit additional research as analytic tools for EEG and cognition in TBI., Competing Interests: The authors have no relevant financial interests at present., (Copyright © 2021 Todd Zorick et al.)

Objective: To assess the impact of ongoing COVID-19 pandemic on epilepsy care in India., Methods: We conducted a three-part survey comprising neurologists, people with epilepsy (PWE), and 11 specialized epilepsy centers across India. We sent two separate online survey questionnaires to Indian neurologists and PWE to assess the epilepsy practice, seizures control, and access to care during the COVID-19 pandemic. We collected and compared the data concerning the number of PWE cared for and epilepsy procedures performed during the 6 months periods preceding and following COVID-19 lockdown from epilepsy centers., Results: The survey was completed by 453 neurologists and 325 PWE. One third of the neurologist reported >50 % decline in outdoor visits by PWE and EEG recordings. The cumulative data from 11 centers showed 65-70 % decline in the number of outdoor patients, video-EEG monitoring, and epilepsy surgery. Working in a hospital admitting COVID-19 patients and use of teleconsultation correlated with this decline. Half of PWE had postponed their planned outpatient visits and EEG. Less than 10 % of PWE missed their antiseizure medicines (ASM) or had seizures due to the nonavailability of ASM. Seizure control remained unchanged or improved in 92 % PWE. Half of the neurologists started using teleconsultation during the pandemic. Only 4% of PWE were afflicted with COVID-19 infection., Conclusions: Despite significant decline in the number of PWE visiting hospitals, their seizure control and access to ASMs were not affected during the COVID-19 pandemic in India. Risk of COVID-19 infection in PWE is similar to general population., (Copyright © 2021 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms., Competing Interests: The authors have declared that no competing interests exist.

To study the yield of prolonged ambulatory electroencephalogram (aEEG). A retrospective chart review of all patients who underwent aEEG studies between 2013 and 2017 was performed. Reasons for aEEG were classified into five categories: detection of interictal epileptiform discharges (IEDs), capturing clinical events, detection of unrecognized seizures, monitoring IEDs during treatment, and unclassifiable. Ambulatory EEG reports were reviewed to evaluate whether the study answered the clinical question. A total of 1,264 patients were included. Forty studies were excluded for incomplete data and 234 for being a repeat study. The average number of recording days was 1.57 ± 0.73. Based on initial clinical evaluation, patients carried the following presumptive diagnosis: 61% epilepsy, 11% single unprovoked or acute symptomatic seizure and 28% non-epileptic paroxysmal events (PEs). Overall, focal IEDs were seen in 16.1% of studies, generalized IEDs in 10.8%, focal seizures in 4.1%, and generalized seizures in 1.9%. The most frequent reason for ordering aEEG was to detect IEDs for diagnostic purposes (48.1%). For this indication, additional information was provided by the aEEG in 19.1% of cases (58.6% focal IEDs, 33.5% generalized IEDs, 7.9% seizures without IEDs). Ambulatory EEG was ordered with the intent to capture and characterize clinical events in 18.9%, mostly in patients who reported daily or weekly events. In these, aEEG captured either epileptic seizures or PEs in 102 (42.7%) of the studies (83.3% PEs, 16.7% epileptic seizures). Ambulatory EEG was ordered to evaluate unrecognized seizures in 17.8% of patients, and electrographic seizures were identified in 13.3% of these studies. The yield of aEEG varies based on the indication for the study. Ambulatory EEG can be a useful tool for recording IEDs in the outpatient setting and in a select group of patients to capture clinical events or unrecognized seizures.

Objective: To characterize photoparoxysmal EEG response (PPR) using a standardized protocol of intermittent photic stimulation (IPS) and standardized definitions for PPR, classified into six types., Methods: Using the SCORE system (Standardized Computer-Based Organized Reporting of EEG) we prospectively built a large database of standardized EEG annotations. In this study, we extracted the features related to PPR from the structured dataset consisting of 10,671 EEG recordings with IPS, from 7,188 patients., Results: The standardized IPS protocol elicited PPR in 375 recordings (3.5%), in 288 patients (4%), with a preponderance among young (11-20 years) and female patients (67%). PPR was persistent in patients with multiple recordings. The most frequent type of PPR was activation of preexisting epileptogenic area (58%), followed by generalized-PPR limited to the stimulus train (22%). We could not find any recording with self-sustained posterior response. Seizures were elicited in 27% of patients with PPR, most often myoclonic seizures and absences, in patients with self-sustained generalized PPR., Conclusions: The most common type of PPR was accentuation of preexisting epileptogenic area. Self-sustained posterior response could not be documented. Self-sustained generalized-PPR had the highest association with seizures., Significance: Using standardized stimulation protocol and definitions for PPR types, IPS provides high diagnostic yield., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Background: Extra virgin olive oil (EVOO) constitutes a natural compound with high protection over cognitive function that could positively alter brain dynamics and the mixture of within and between-frequency connectivity., Objective: The balance of cross-frequency coupling over within-frequency coupling can build a nonlinearity index (NI) that encapsulates the over-excitation of information flow between brain areas and across experimental time. The present study investigated for the very first time how the Greek High Phenolic Early Harvest Extra Virgin Olive Oil (HP-EH-EVOO) versus Moderate Phenolic (MP-EVOO) and Mediterranean Diet (MeDi) intervention in people with mild cognitive impairment (MCI) could affect their spontaneous EEG dynamic connectivity., Methods: Forty-three subjects (14 in MeDi, 16 in MP-EVOO, and 13 in HP-EH-EVOO) followed an EEG resting-state recording session (eyes-open and closed) before and after the treatment. Following our dominant coupling mode model, we built a dynamic integrated dynamic functional connectivity graph that tabulates the functional strength and the dominant coupling mode model of every pair of brain areas., Results: Signal spectrum within 1-13 Hz and theta/beta ratio have decreased in the HP-EH-EVOO group in the eyes-open condition. The intervention improved the FIDoCM across groups and conditions but was more prominent in the HP-EH-EVOO group (p < 0.001). Finally, we revealed a significant higher post-intervention reduction of NI (ΔNITotal and α) for the HP-EH-EVOO compared to the MP-EVOO and MeDi groups (p < 0.0001)., Conclusion: Long-term intervention with HP-EH-EVOO reduced the over-excitation of information flow in spontaneous brain activity and altered the signal spectrum of EEG rhythms.

Objectives: Charles Bonnet Syndrome (CBS) is a rare clinical condition which has been defined as complex visual hallucinations (CVH) due to visual loss. This study investigated differences in the EEG power spectral density (PSD) and magnitude-squared coherences between patients with eye disease and hallucinations (VH+), and the control subjects with eye disease without hallucinations (VH-)., Methods: 19 scalp channels EEG was recorded in four VH+ (CBS) and four VH- subjects during an eyes-closed resting condition. Artefact-free epochs were analyzed to obtain PSD values in the delta, theta, alpha1, alpha2, beta1, beta2 and gamma frequency bands. Coherence values were calculated through inter-hemispheric and intra-hemispheric electrodes pairs of interest. All subjects were performed with neuropsychological and behavioral assessments to evaluate cognitive functions., Results: The VH + group had increase PSD in theta, beta2 and gamma bands in central, parietal and occipital (O2) areas. The synchronicity was altered particularly in parietal and frontal-parietal regions especially at theta and alpha1 respectively., Conclusions: The aberrant activity in occipital and parietal regions suggest the mechanism of CBS. This is a major electrophysiological study of understanding CBS and visual hallucinations., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Visual short-term memory (VSTM) enables humans to form a stable and coherent representation of the external world. However, the nature and temporal dynamics of the neural representations in VSTM that support this stability are barely understood. Here we combined human intracranial electroencephalography (iEEG) recordings with analyses using deep neural networks and semantic models to probe the representational format and temporal dynamics of information in VSTM. We found clear evidence that VSTM maintenance occurred in two distinct representational formats which originated from different encoding periods. The first format derived from an early encoding period (250 to 770 ms) corresponded to higher-order visual representations. The second format originated from a late encoding period (1,000 to 1,980 ms) and contained abstract semantic representations. These representational formats were overall stable during maintenance, with no consistent transformation across time. Nevertheless, maintenance of both representational formats showed substantial arrhythmic fluctuations, i.e., waxing and waning in irregular intervals. The increases of the maintained representational formats were specific to the phases of hippocampal low-frequency activity. Our results demonstrate that human VSTM simultaneously maintains representations at different levels of processing, from higher-order visual information to abstract semantic representations, which are stably maintained via coupling to hippocampal low-frequency activity., Competing Interests: The authors declare no competing interest.