Search

Compared to functional magnetic resonance imaging (fMRI), source localization of a scalp-recorded electroencephalogram (EEG) provides higher temporal resolution and frequency synchronization to better understand the potential neurophysiological origins of disrupted functional connectivity (FC) in major depressive disorder (MDD). The present study aimed to investigate EEG-sourced measures to examine the FC in drug-free patients with MDD. Resting-state 32-channel EEG were recorded in 84 drug-free patients with MDD and 143 healthy controls, and the cortical source signals were estimated. Exact low-resolution brain electromagnetic tomography (eLORETA) was used to compute the intracortical activity from regions within the default mode network (DMN) and frontoparietal network (PFN). Lagged phase synchronization was used as a measure of functional connectivity. Compared with control subjects, the MDD group showed greater within-DMN alpha 1 and 2 bands and within-FPN alpha 1, 2, and beta 3 bands. Furthermore, the MDD group showed hyperconnectivity between the DMN and the FPN in the alpha 1 and 2 bands. Finally, higher levels of anhedonia were associated with higher between-network DMN and FPN connectivity in the alpha-1 band. Due to the inherent limitations of eLORETA with predefined seeds, we could not exclude connectivity between regions of interest (ROIs), which may be related to the activity from regions adjacent to the ROIs. The present findings support the importance of phase-lagged functional dysconnectivity in the neurophysiological mechanisms underlying MDD. Exploring the potential of these patterns as surrogates for treatment responses may advance targeted interventions for depression. • Resting-state EEG were recorded in 84 drug-free patients with MDD and 143 healthy controls. • Drug-free patients with MDD have altered EEG functional connectivity. • The MDD group showed greater within-DMN and within-FPN functional connectivity. • MDD group showed hyperconnectivity between the DMN and the FPN. • Anhedonia severity was positively correlated with between-network DMN and FPN connectivity. [ABSTRACT FROM AUTHOR]

In the domain of medical science, emotion recognition based on electroencephalogram (EEG) has been widely used in emotion computing. Despite the prevalence of deep learning in EEG signals analysis, standard convolutional and recurrent neural networks fall short in effectively processing EEG data due to their inherent limitations in capturing global dependencies and addressing the non-linear and unstable characteristics of EEG signals. We propose a dual transfer learning method based on 3D Convolutional Neural Networks (3D-CNN) with a Vision Transformer (ViT) to enhance emotion recognition. This paper aims to utilize 3D-CNN effectively to capture the spatial characteristics of EEG signals and reduce data covariance, extracting shallow features. Additionally, ViT is incorporated to improve the model’s ability to capture long-range dependencies, facilitating deep feature extraction. The methodology involves a two-stage process: initially, the front end of a pre-trained 3D-CNN is employed as a shallow feature extractor to mitigate EEG data covariance and transformer biases, focusing on low-level feature detection. The subsequent stage utilizes ViT as a deep feature extractor, adept at modeling the global aspects of EEG signals and employing attention mechanisms for precise classification. We also present an innovative algorithm for data mapping in transfer learning, ensuring consistent feature representation across both spatio-temporal dimensions. This approach significantly improves global feature processing and long-range dependency detection, with the integration of color channels augmenting the model’s sensitivity to signal variations. In a 10-fold cross-validation experiment on the DEAP, experimental results demonstrate that the proposed method achieves classification accuracies of 92.44 % and 92.85 % for the valence and arousal dimensions, and the accuracies of four-class classification across valence and arousal are HVHA: 88.01 % , HVLA: 88.27 % , LVHA: 90.89 % , LVLA: 78.84 % . Similarly, it achieves an accuracy of 98.69 % on the SEED. Overall, this methodology not only holds substantial potential in advancing emotion recognition tasks but also contributes to the broader field of affective computing. [ABSTRACT FROM AUTHOR]

Phenylalanine (PA) levels below 360 µmol/L do not require treatment; however, cognitive deficits have been observed in patients with elevated PA levels, necessitating a safe upper limit for treatment and therapeutic objectives. The main purpose of this study is to evaluate the correlation between developmental assessments (Denver Developmental Screening Test-II [DDST-II] and Ankara Developmental Screening Inventory [ADSI]) and electroencephalogram (EEG) findings with blood PA levels and genotypic data in non-phenylketonuria mild Hyperphenylalaninemia (HPA) patients, to re-evaluate their treatment status based on potential adverse outcomes. This study encompassed 40 patients aged 1–5 years diagnosed with HPA and not on treatment, identified through initial blood PA levels, and monitored for a minimum of 1 year on an unrestricted diet. Data on demographics, serum PA levels during presentation and follow-up, and genetic mutations were retrieved from hospital records. Patients were categorized into two groups as well-controlled (120–240 µmol/L) and at-risk (240–360 µmol/L) based on average PA levels. Sleep-activated EEGs and developmental assessments using the DDST-II and ADSI were conducted to compare outcomes with PA levels and genetic findings. Developmental delays in the DDST-II were observed across language, gross motor, fine motor, and personal-social domains, predominantly in males. No significant difference in delays was noted between the well-controlled and at-risk groups based on PA levels. The ADSI revealed delays in similar developmental areas, with fine motor skills being particularly prominently affected in the at-risk group. Only a well-controlled patient showed abnormal EEG results deemed unrelated to HPA. Conclusion: Our findings indicate that children with untreated PA levels above 240 µmol/L are particularly susceptible to fine motor skill impairments, suggesting a need to reassess the PA level thresholds for initiating treatment. This study highlights the potential requirement for amending current guidelines to ensure early and appropriate intervention in non-PKU mild HPA patients, thereby mitigating the risk of developmental delays. What is Known: • It is known that phenylalanine levels between 120 and 360 μmol/L typically do not require intervention in non-PKU mild HPA patients, but outcomes for levels near this threshold remain unclear. What is New: • Children with PA levels exceeding 240 µmol/L are at a higher risk of fine motor skill impairment, requiring a reassessment of safe PA levels to prevent developmental delays. • In addition, the Denver Developmental Screening Test II reveals developmental delays in multiple areas in children with non-PKU mild HPA, particularly in males, highlighting the need for gender-specific monitoring and intervention strategies. [ABSTRACT FROM AUTHOR]

Mind wandering can cause workers to overlook safety hazards and delay making accurate operational decisions, ultimately raising the potential for accidents. However, there is relatively little research on the physiological characteristics of drilling workers during mind wandering. The aim of this investigation was to tackle the constraints of previous studies and to establish a more comprehensive theoretical framework and practical guidance for safety management. To this end, the phenomenon of workload on mind wandering among drillers during the drilling process was investigated in depth. It focused on drilling site workers, using SART paradigm tasks and EEG devices to track cognitive states under various loads, exploring how they affect mind wandering and EEG mechanisms. Fifty workers participated, observing drilling images to judge accidents. Results showed workload influenced cognitive processes such as mind wandering occurrence, reaction time, accuracy, and brain connectivity. High workload increased reaction time, decreased accuracy, raised mind wandering frequency, altered theta, beta, and gamma waves, and reduced cerebral synchronisation and engagement. Workload affected employees’ mind wandering, sensations, focus, and work status, with a positive correlation between workload and mind wandering, potentially harming work performance and safety. Analyzing EEG data helps identify mind wandering and develop intervention measures. In depth research on these features not only helps identify employee mind wandering, but also promotes the development of more precise and personalized intervention measures. [ABSTRACT FROM AUTHOR]

Food aroma molecules have the potential to influence people's emotions through the olfactory pathway and are anticipated to emerge as a new method for regulating emotional states, owing to their simplicity and high acceptance. Current research on food aroma predominantly centres on the physicochemical properties and formation mechanisms of aroma components, neglecting the effects of aroma molecules in emotional regulation. Moreover, the evaluation of pleasantness, a pivotal dimension of emotions, lacks objective assessment methods. In this study, sensory assessments of pleasantness for 12 aroma compounds were gathered from 45 subjects, and their correlation with the brain's activity responses in the left frontal‐temporal lobe (LFT) and right frontal‐temporal lobe (RFT) using electroencephalogram (EEG) signals was analysed. The results revealed a close relationship between brain activity in the LFT and the perception of aroma pleasantness. Furthermore, a substantial correlation was observed between the α, β and γ frequency bands in the LFT and the subjective pleasantness scores. These findings demonstrate that the LFT plays a critical role in evaluating the pleasantness of aroma molecules, and that changes in the power of the α, β and γ bands serve as important evaluation indicators. Consequently, this method offers a new objective means for assessing pleasantness to find higher pleasantness aroma molecules and the emotional regulation of food aroma. [ABSTRACT FROM AUTHOR]

Objective: To explore the relationship between the high frequency (HF) heart rate variability (HRV) and electroencephalogram (EEG) delta band power in women with irritable bowel syndrome (IBS) versus healthy control women. Materials and Methods: Twenty women with IBS and twenty healthy controls were studied over three consecutive nights using polysomnography in a sleep laboratory. To avoid the first night effect, only second-night data were analyzed. Power spectral analysis was applied to HRV and EEG recordings. The linear system coherence/phase analysis assessed the relationship between normalized HF power of HRV and normalized delta band power of EEG during the first four NREM-REM sleep cycles. Results: Women with IBS exhibited a significantly higher percentage of NREM sleep, higher normalized HF, lower normalized low frequency (LF) and decreased LF/HF ratio of HRV in the first four NREM-REM sleep cycles compared to controls. Additionally, their normalized delta band power was significantly lower in these sleep cycles and over the whole night. The phase shift between HF and delta band power was significantly longer in the IBS group. While the coherence between normalized HF and normalized delta band power was lower in the IBS group, the difference was not statistically significant. Conclusions: The coherence/phase analysis showed a dysregulated interaction between autonomic and central nervous systems in women with IBS, manifested by increased lag time between cardiac and EEG delta band power compared to healthy controls. Whether this dysregulation contributes to the pathophysiology of IBS remains to be determined. [ABSTRACT FROM AUTHOR]

Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies. Since the domain shift cannot be completely removed, target EEG samples located near the edge of clusters are also susceptible to misclassification. Methods: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. Finally, the entropy minimization (EM) regularization is further integrated to adjust the classifier to pass through the low-density region between clusters. The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects. Results: The effectiveness of the ADFR framework was evaluated on two public motor imagery (MI)-based EEG datasets: BCI Competition III dataset 4a and BCI Competition IV dataset 2a. In terms of average accuracy, ADFR achieved improvements of 3.0% and 2.1%, respectively, over the state-of-the-art methods on these datasets. Conclusions: The promising results highlight the effectiveness of the ADFR algorithm for EEG decoding and show its potential for practical applications. [ABSTRACT FROM AUTHOR]

Introduction: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms. Methods: Our main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation. Results: Among all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications. Discussion: Our research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation. [ABSTRACT FROM AUTHOR]

Summary We aimed to establish the heritability of polysomnography measures through the analysis of home polysomnography recordings from 648 participants in the Baependi Heart Study, a rural, family‐based, genetically admixed cohort based in the southeast of Brazil. Sleep polysomnography staging variables were computed, and narrow‐sense heritability values were derived. The heritability (h2) of polysomnography total sleep time was 0.18 ± 0.08 (p = 0.007). Including age and sex did not change the heritability estimates of the model. Wake after sleep onset did not show significant heritability (h2 = 0.02 ± 0.07, p = 0.39). The unadjusted model for N1 resulted in a heritability estimate of 0.22 ± 0.10 (p < 0.003), and of 0.26 ± 0.10 (p = 0.003) in the adjusted model. Time spent in N2 had an unadjusted heritability of 0.18 ± 0.09 (p = 0.01) and adjusted h2 of 0.22 ± 0.10 (p = 0.007). The heritability of the total time spent in N3 was 0.35 ± 0.09 (p < 0.001) in the unadjusted and 0.38 ± 0.09 (p < 0.001) when sex, age and age*age were considered in the model. By contrast, no variance in the total time spent in rapid eye movement could be significantly attributed to genetic variance. In terms of the heritability of the apnea–hypopnea index, 17% of its variance could be attributed to genetic factors (0.17 ± 0.08, p&#x02009;=&#x02009;0.02). This is the first report of heritability of electroencephalographic‐derived sleep parameters from a larger population sample, and the first one performed in a population with a majority above 25&#x02009;years of age. Our findings indicate the potential feasibility of future genome‐wide association studies of non‐rapid eye movement sleep stages in pooled population samples. [ABSTRACT FROM AUTHOR]

Chronic sedentary behavior can have a negative impact on the executive function (EF) of young people. While physical activity (PA) has been shown to improve this phenomenon, the effects of different types of PA on EF vary. In this study, we compared the effects of moderate-intensity continuous training (MICT) (60–70% HRmax, 30 min), body weight training (BWT) (2 sets tabata, 20 min), and mind-body exercise (MBE) (2 sets Yang style shadowboxing, 20 min) on EF in 59 sedentary youth (n = 59, age = 20.36 ± 1.78, BMI = 24.91 ± 1.82, P>0.05) to identify the optimal dose of PA for improving EF. Metrics related to the EF task paradigm included stop signal, electroencephalogram (EEG), event-related potential (ERP), P300, N200, error-related negativity (ERN), and error positivity (Pe). error positivity (Pe), and β-wave in frontal lobe; training monitoring, including heart rate (HR), rating of perceived exertion (RPE), feeling scale (FS), and dual-mode model (DMM); load assessment, including Edward's TRIMP (TRIMP) and session-RPE (s-RPE). The study results indicate that BWT significantly improved accuracy in terms of EF (F = 16.84, P = 0.0381) and was comparable to MICT in terms of shortening reaction time (F = 58.03, P = 0.0217; F = 75.49, P = 0.0178). Regarding ERP, BWT reduced the amplitude values of N200 compared to ERN (F = 44.35, P = 0.0351; F = 48.68, P = 0.0317), increased P300 compared to Pe (F = 97.72, P<0.01; F = 29.56, P = 0.0189), and shortened P300 latency (F = 1.84, P = 0.0406). In contrast, MICT was only effective for P300 with Pe (F = 66.59, P = 0.0194; F = 21.04, P = 0.0342) and shortened N200 latency (F = 27.29, P = 0.0411). The increase in total amplitude and β-oscillation in terms of EEG was proportional to the exercise intensity, with the difference between MICT and BWT being present at 5–20 Hz, and MBE at 10–15 Hz. Regarding training load, the order of HR, RPE, TRIMP, and s-RPE was BWT > MICT > MBE (F = 202.69; F = 114.69; F = 114.69; P = 0.0342). The latency of N200 was also shortened (F = 27.29, P = 0.0411). The results showed that PA improves EF in sedentary youth, although BWT works best, it leads to a decrease in motor perception. Initially, MICT was scheduled alongside MBE and later replaced with BWT. This may help establish an exercise habit while improving EF. [ABSTRACT FROM AUTHOR]

Summary Traditionally categorized as a uniform sleep phase, rapid eye movement sleep exhibits substantial heterogeneity with its phasic and tonic constituents showing marked differences regarding many characteristics. Here, we investigate how tonic and phasic states differ with respect to aperiodic neural activity, a marker of arousal and sleep. Rapid eye movement sleep heterogeneity was assessed using either binary phasic‐tonic (n = 97) or continuous (in 60/97 participants) approach. Slopes of the aperiodic power component were measured in the low (2–30 Hz, n = 97) and high (30–48 Hz, n = 60/97) frequency bands with the Irregularly Resampled Auto‐Spectral Analysis applied on electroencephalography. Rapid eye movement amplitudes were quantified with the YASA applied on electrooculography (n = 60/97). The binary approach revealed that the phasic state is characterized by steeper low‐band slopes with small effect sizes and some topographical heterogeneity over datasets. High‐band aperiodic slopes were flatter in the phasic versus tonic state with medium‐to‐large effect sizes over all areas in both datasets. The continuous approach confirmed these findings. The temporal analysis within rapid eye movement episodes revealed that aperiodic activity preceding or following EM events did not cross‐correlate with eye movement amplitudes. This study demonstrates that aperiodic slopes can serve as a reliable marker able to differentiate between phasic and tonic constituents of rapid eye movement sleep and reflect phasic rapid eye movement event intensity. However, rapid eye movement events could not be predicted by preceding aperiodic activity and vice versa, at least not with scalp electroencephalography. [ABSTRACT FROM AUTHOR]

Background: Local anesthetic systemic toxicity (LAST) is a rare but potentially life-threatening complication. Under general anesthesia, neurological signs are often masked, delaying diagnosis and increasing the risk of sudden cardiovascular collapse. Therefore, early detection methods are critically needed. Case presentation: A 48-year-old male patient (height: 182 cm, weight: 98 kg) underwent resection of a mediastinal goiter. He received 10 mL of 4% lidocaine for topical airway anesthesia and 20 mL of 1% lidocaine with 1:100,000 epinephrine for chest wall anesthesia. Thirty minutes after airway anesthesia, continuous theta waves appeared on the frontal electroencephalogram (EEG), which were enhanced following chest wall anesthesia. These waves transitioned into a repeating pattern and evolved into sharp periodic discharges. After administering 150 mL of 20% lipid emulsion, the EEG normalized. Conclusions: This case highlights that EEG monitoring during general anesthesia may facilitate the early detection of LAST and provide real-time feedback on treatment efficacy. [ABSTRACT FROM AUTHOR]

Dynamic uncertainty of the relationship among brain regions is an important limiting factor in electroencephalography (EEG)-based emotion recognition. This uncertainty stems from individual differences and emotional volatility, which needs further in-depth study. In this paper, we propose a new emotion recognition method, which is named graph convolutional neural network with spatio-temporal modeling and long short-term memory (STLGCNN). The proposed method aims to address the instability of emotion intensity and underutilization of EEG biotopological information. The method consists of an attention module, a bi-directional long short-term memory network (BiLSTM), a graph convolutional neural network (GCNN) and a long short-term memory module (LSTM). The attention mechanism is utilized to reveal correlations between different time periods and to reduce emotional temporal volatility. The BiLSTM is employed to learn spatio-temporal features. Then, the GCNN learns the biotopological information of multi-channel EEG signals and extracts effective graph domain features. These features are then fed into the LSTM to integrate the graph-domain information and extract valid temporal information. To verify the effectiveness of the STLGCNN method, we conducted experiments on the DEAP and SEED datasets. The average accuracies on the two datasets are 93.95 and 96.78%, respectively. The results show that the STLGCNN method has better performance than existing methods. [ABSTRACT FROM AUTHOR]

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

This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain–computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing traditional machine learning with deep learning and emphasizing the role of brain lateralization in imagined speech for effective recognition and classification. This study discusses the challenges of generalizability and scalability in imagined speech recognition, focusing on subject-independent approaches and multiclass scalability. Performance benchmarking across various datasets and methodologies revealed varied classification accuracies, reflecting the complexity and variability of EEG signals. This review concludes that challenges remain despite progress, particularly in classifying directional words. Future research directions include improved signal processing techniques, advanced neural network architectures, and more personalized, adaptive BCI systems. This review is critical for future efforts to develop practical communication tools for individuals with speech and motor impairments using EEG-based BCIs. [ABSTRACT FROM AUTHOR]

Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep's impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming. To address this challenge, automated brain monitoring has emerged as a crucial solution for cost-effective and efficient EEG data analysis. A critical component of sleep analysis is detecting transitions between wakefulness and sleep states. These transitions offer valuable insights into sleep quality and quantity, essential for diagnosing sleep disorders, designing effective interventions, enhancing overall health and well-being, and studying sleep's effects on cognitive function, mood, and physical performance. This study presents a novel EEG feature extraction pipeline for the accurate classification of various wake and sleep stages. We propose a noise-robust model-based Kalman filtering (KF) approach to track changes in a time-varying auto-regressive model (TVAR) applied to EEG data during different wake and sleep stages. Our approach involves extracting features, including instantaneous frequency and instantaneous power from EEG, and implementing a two-step classifier for sleep staging. The first step classifies data into wake, REM, and non-REM categories, while the second step further classifies non-REM data into N1, N2, and N3 stages. Evaluation on the extended Sleep-EDF dataset (Sleep-EDFx), with 153 EEG recordings from 78 subjects, demonstrated compelling results with classifiers including Logistic Regression, Support Vector Machines, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The best performance was achieved with the LGBM and XGBoost classifiers, yielding an overall accuracy of over 77%, a macro-averaged F1 score of 0.69, and a Cohen's kappa of 0.68, highlighting the efficacy of the proposed method with a remarkably compact and interpretable feature set. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Electroencephalographic seizures (ES) are common in neonates with hypoxic–ischemic encephalopathy (HIE), but identification with continuous electroencephalographic (EEG) monitoring (CEEG) is resource‐intensive. We aimed to develop an ES prediction model.Using a prospective observational study of 260 neonates with HIE undergoing CEEG, we identified clinical and EEG risk factors for ES, evaluated model performance with area under the receiver operating characteristic curve (AUROC), and calculated test characteristics emphasizing high sensitivity. We assessed ES incidence and timing in neonates subdivided by ES risk group (low, moderate, high) as determined by EEG risk factors.ES occurred in 32% (83/260) of neonates. Performing CEEG for only 24 h would fail to identify the 7% (17/260) of neonates with later onset ES (20% of all neonates experiencing ES). Identifying 90% or 95% of neonates with ES would require CEEG for 63 or 74 h, respectively. The optimal model included continuity and epileptiform discharges, both assessed in the initial 1 h of CEEG. It yielded an AUROC of .80, and at a cutoff that emphasized sensitivity, had sensitivity of 94%, specificity of 45%, positive predictive value of 44%, and negative predictive value of 95%. The model would avoid CEEG beyond 1 h in 32% (84/260) of neonates, but 6% (5/83) of neonates with ES would not have ES identified. ES incidence was significantly different (p < .01) across ES risk groups (6% low, 40% moderate, and 83% high). Only ~6 h of CEEG would identify all neonates with ES in the low‐risk group, whereas 75 and 63 h of CEEG would be required to identify 95% of neonates with ES in the moderate‐risk and high‐risk groups, respectively.Among neonates with HIE, a model employing two EEG variables from a 1‐h screening EEG and stratifying neonates into low‐, moderate‐, and high‐risk groups could enable evidence‐based strategies for targeted CEEG use. [ABSTRACT FROM AUTHOR]

Background: Stroke remains a leading cause of disability globally and movement impairment is the most common complication in stroke patients. Resting-state electroencephalography (EEG) microstate analysis is a non-invasive approach of whole-brain imaging based on the spatiotemporal pattern of the entire cerebral cortex. The present study aims to investigate microstate alterations in stroke patients. Methods: Resting-state EEG data collected from 24 stroke patients and 19 healthy controls matched by age and gender were subjected to microstate analysis. For four classic microstates labeled as class A, B, C and D, their temporal characteristics (duration, occurrence and coverage) and transition probabilities (TP) were extracted and compared between the two groups. Furthermore, we explored their correlations with clinical outcomes including the Fugl-Meyer assessment (FMA) and the action research arm test (ARAT) scores in stroke patients. Finally, we analyzed the relationship between the temporal characteristics and spectral power in frequency bands. False discovery rate (FDR) method was applied for correction of multiple comparisons. Results: Microstate analysis revealed that the stroke group had lower occurrence of microstate A which was regarded as the sensorimotor network (SMN) compared with the control group (p = 0.003, adjusted p = 0.036, t = -2.959). The TP from microstate A to microstate D had a significant positive correlation with the Fugl-Meyer assessment of lower extremity (FMA-LE) scores (p = 0.049, r = 0.406), but this finding did not survive FDR adjustment (adjusted p = 0.432). Additionally, the occurrence and the coverage of microstate B were negatively correlated with the power of delta band in the stroke group, which did not pass adjustment (p = 0.033, adjusted p = 0.790, r = -0.436; p = 0.026, adjusted p = 0.790, r = -0.454, respectively). Conclusions: Our results confirm the abnormal temporal dynamics of brain activity in stroke patients. The study provides further electrophysiological evidence for understanding the mechanism of brain motor functional reorganization after stroke. [ABSTRACT FROM AUTHOR]

Background: Sleep deprivation (SD) can impair an individual's alertness, which is the basis of attention and the mechanism behind continuous information processing. However, research concerning the effects of total sleep deprivation (TSD) on alertness networks is inadequate. In this study, we investigate the cognitive neural mechanism of alertness processing after TSD. Methods: Twenty-four college students volunteered to participate in the study. The resting-state electroencephalogram (EEG) data were collected under two conditions (rested wakefulness [RW], and TSD). We employed isolated effective coherence (iCoh) analysis and functional independent component analysis (fICA) to explore the effects of TSD on participants' alertness network. Results: This study found the existence of two types of effective connectivity after TSD, as demonstrated by iCoh: from the left cuneus to the right middle frontal gyrus in the β3 and γ bands, and from the left angular gyrus to the left insula in the δ, θ, α, β1, β3, and γ bands. Furthermore, Pearson correlation analysis showed that increased effective connectivity between all the bands had a positive correlation with increases in the response time in the psychomotor vigilance task (PVT). Finally, fICA revealed that the neural oscillations of the cuneus in the α2 bands increased, and of the angular gyrus in the α and β1 bands decreased in TSD. Conclusions: TSD impairs the alertness function among individuals. Increased effective connectivity from the cuneus to the middle frontal gyrus may represent overloads on the alertness network, resulting in participants strengthening top-down control of the attention system. Moreover, enhanced effective connectivity from the angular gyrus to the insula may indicate a special perception strategy in which individuals focus on salient and crucial environmental information while ignoring inessential stimuli to reduce the heavy burden on the alertness network. [ABSTRACT FROM AUTHOR]

Background: Sleep, a process physiologically vital for mental health, faces disruptions in various sleep disorders linked to metabolic and neurodegenerative risks. Zizyphus seed (Zizy) has long been recognized for its diverse pharmacological attributes, including analgesic, sedative, insomnia, and anxiety alleviation. Objectives: In this study, the sleep-prolonging effects of Zizy extract (100, 200 mg/kg), along with their characterizing compounds jujuboside A (JuA) (5, 10 mg/kg), were evaluated in a mouse model under a pentobarbital-induced sleep. Additionally, the efficacy of Zizy extract was examined on caffeine-induced insomnia in mice. Methods: To confirm the efficacy of Zizy extract on the structure and quality of sleep, an electroencephalogram (EEG) analysis of rats was performed using the MATLAB algorithm. Additionally, Western blot analysis and measurement of intracellular chloride influx were performed to confirm whether these effects acted through the gamma-aminobutyric acid (GABA)ergic system. Administration of Zizy extract showed no effect on the locomotor performance of mice, but the extract and their characteristic compounds significantly prolonged sleep duration in comparison to the pentobarbital alone group in the pentobarbital-induced sleep mouse model. Furthermore, this extract alleviated caffeine-induced insomnia in mice. Results: The administration of Zizy extract extended non-rapid eye movement sleep (NREMS) duration without inducing significant changes in the brain wave frequency. Zizy extract regulated the expression of GABAA receptor subunits and GAD65/67 in specific brain regions (frontal cortex, hippocampus, and hypothalamus). JuA increased intracellular chloride influx in human SH-SY5Y cells, and it was reduced by GABAA receptor antagonists. These results suggest that the sleep-maintaining effects of Zizy extract may entail GABAergic regulation. In summary, Zizy extract demonstrated sleep-prolonging properties, improved insomnia, and regulated sleep architecture through GABAergic system modulation. Conclusions: These findings suggest that Zizy extract has potential as a therapeutic agent for stress-related neuropsychiatric conditions such as insomnia. [ABSTRACT FROM AUTHOR]

Introduction: The ASME (stands for Auditory Stream segregation Multiclass ERP) paradigm is proposed and used for an auditory brain-computer interface (BCI). In this paradigm, a sequence of sounds that are perceived as multiple auditory streams are presented simultaneously, and each stream is an oddball sequence. The users are requested to focus selectively on deviant stimuli in one of the streams, and the target of the user attention is detected by decoding event-related potentials (ERPs). To achieve multiclass ASME BCI, the number of streams must be increased. However, increasing the number of streams is not easy because of a person's limited audible frequency range. One method to achieve multiclass ASME with a limited number of streams is to increase the target stimuli in a single stream. Methods: Two approaches for the ASME paradigm, ASME-4stream (four streams with a single target stimulus in each stream) and ASME-2stream (two streams with two target stimuli in each stream) were investigated. Fifteen healthy subjects with no neurological disorders participated in this study. An electroencephalogram was acquired, and ERPs were analyzed. The binary classification and BCI simulation (detecting the target class of the trial out of four) were conducted with the help of linear discriminant analysis, and its performance was evaluated offline. Its usability and workload were also evaluated using a questionnaire. Results: Discriminative ERPs were elicited in both paradigms. The average accuracies of the BCI simulations were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). In the ASME-2stream paradigm, the latency and the amplitude of P300 were shorter and larger, the average binary classification accuracy was higher, and the average weighted workload was smaller. Discussion: Both four-class ASME paradigms achieved a sufficiently high accuracy (over 80%). The shorter latency and larger amplitude of P300 and the smaller workload indicated that subjects could perform the task confidently and had high usability in ASME-2stream compared to ASME-4stream paradigm. A paradigm with multiple target stimuli in a single stream could create a multiclass ASME BCI with limited streams while maintaining task difficulty. These findings expand the potential for an ASME BCI multiclass extension, offering practical auditory BCI choices for users. [ABSTRACT FROM AUTHOR]

Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the affected individual's social functionality and quality of life. This study aims to integrate cognitive dynamic attributes of the brain into seizure prediction, evaluating the effectiveness of various characterization perspectives for seizure prediction, while delving into the impact of varying fragment lengths on the performance of each characterization. We adopted microstate analysis to extract the dynamic properties of cognitive states, calculated the EEG-based and microstate-based features to characterize nonlinear attributes, and assessed the power values across different frequency bands to represent the spectral information of the EEG. Based on the aforementioned characteristics, the predictor achieved a sensitivity of 93.82% on the private FH-ZJU seizure dataset and 93.22% on the Siena Scalp EEG dataset. The study outperforms state-of-the-art works in terms of sensitivity metrics in seizure prediction, indicating that it is crucial to incorporate cognitive dynamic attributes of the brain in seizure prediction. [ABSTRACT FROM AUTHOR]

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

In this study, the agricultural work stress induced by the noise and vibration of some agricultural machinery was analyzed through electroencephalogram (EEG) measurements. The values of spectral edge frequency (SEF) 95%, relative gamma power (RGP), and EEG-based working index (EWI), utilized as stress indicators, were derived by analyzing the EEG data collected. The EEG analysis revealed that agricultural work stress manifested when participants engaged in agricultural tasks following a period of rest. Additionally, the right prefrontal cortex was identified where the values of SEF95% and RGP increased concurrently with the rise in noise (61.42–88.39 dBA) and vibration (0.332–1.598 m/s2). This study's results are expected to be utilized as foundational data to determine the agricultural work stress felt by farmers during work through EEG analysis in response to changes in noise and vibration. [ABSTRACT FROM AUTHOR]

Motor imagery (MI)-based brain computer interfaces (BCIs) frequently use convolutional neural networks (CNNs) to analyse electroencephalography (EEG) signals. In this study, we proposed a novel methodology that includes an innovative preprocessing step and a new model for MI EEG classification. In the preprocessing, we use common average reference (CAR) filtering and Laplace filtering of EEG signals. The CAR filter eliminates the overall noise and Laplace filter removes the neighbouring electrode noise. Additionally, a sliding window method is used to increases the number of small-time segments which prevents overfitting. Next, the time segments are converted into spectrograms using the short-time Fourier transform (STFT). Further, the concatenated spectrogram images of mu and beta bands are processed using a CNN model with self-attention. The proposed model uses both local and global information to effectively extract features The EEG signals obtained from BCI competition IV dataset-2a are divided into 80:20 ratio for training and testing. Moreover, the ablation study highlights the importance of the combination of CAR and Laplace filters. The classification results obtained using proposed methodology shows advancement as compared to state-of-the-art methods. Finally, the proposed CNN model learning and feature distribution are visualized with the gradient weight class activation map. [ABSTRACT FROM AUTHOR]

One of the most crucial use of hands in daily life is grasping. Sometimes people with neuromuscular disorders become incapable of moving their hands. This article proposes a grasp motor imagery identification approach based on multivariate fast iterative filtering (MFIF). The proposed methodology involves the selection of relevant electroencephalogram (EEG) channels based on the neurophysiology of the brain. The selected EEG channels have been decomposed into five components using MFIF. Information potential based features are extracted from the decomposed EEG components. The extracted features are smoothed using a moving average filter. The smoothed features are classified using the k-nearest neighbors classifier. The cross-subject classification accuracy, precision, and F1-score of 98.25%, 98.31%, and 98.24%, respectively, is obtained. While the average classification accuracy, precision and F1-score for multiple subjects is 98.43%, 98.62%, and 98.41%, respectively. The proposed methodology can be used for the development of a low cost EEG based grasp identification system. [ABSTRACT FROM AUTHOR]

Background: Resting-state electroencephalography (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI). We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in patients with TBI. Methods: This is a retrospective study of comatose patients with TBI who were admitted to a trauma center (October 2013 to January 2022). Demographics, basic clinical data, imaging characteristics, and EEGs were collected. We calculated the following using 10-min rsEEGs: power spectral density, permutation entropy (complexity measure), weighted symbolic mutual information (wSMI, global information sharing measure), Kolmogorov complexity (Kolcom, complexity measure), and heart-evoked potentials (the averaged EEG signal relative to the corresponding QRS complex on electrocardiography). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, and rsEEG data via a support vector machine. Results: We studied 113 of 134 (84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50 years, p = 0.01). Patients who recovered also had higher Kolcom (U = 1688, p = 0.01), increased beta power (U = 1,652 p = 0.003) with higher variability across channels (U = 1534, p = 0.034) and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04), and higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = 0.026; U = 1639, p = 0.024) than those who did not recover. The area under the receiver operating characteristic curve for rsEEG was higher than that for clinical data (using age, motor response, pupil reactivity) and higher than that for the Marshall computed tomography classification (0.69 vs. 0.66 vs. 0.56, respectively; p < 0.001). Conclusions: We describe the rsEEG signature in recovery of consciousness prior to discharge in comatose patients with TBI. rsEEG measures performed modestly better than the clinical and imaging data in predicting recovery. [ABSTRACT FROM AUTHOR]

Sleep is a prominent physiological state observed across the animal kingdom. Yet, for some animals, our ability to identify sleep can be masked by behaviors otherwise associated with being awake, such as for some sharks that must swim continuously to push oxygenated seawater over their gills to breathe. We know that sleep in buccal pumping sharks with clear rest/activity cycles, such as draughtsboard sharks (Cephaloscyllium isabellum, Bonnaterre, 1788), manifests as a behavioral shutdown, postural relaxation, reduced responsiveness, and a lowered metabolic rate. However, these features of sleep do not lend themselves well to animals that swim nonstop. In addition to video and accelerometry recordings, we tried to explore the electrophysiological correlates of sleep in draughtsboard sharks using electroencephalography (EEG), electromyography, and electrooculography, while monitoring brain temperature. The seven channels of EEG activity had a surprising level of (apparent) instability when animals were swimming, but also when sleeping. The amount of stable EEG signals was too low for replication within‐ and across individuals. Eye movements were not measurable, owing to instability of the reference electrode. Based on an established behavioral characterization of sleep in draughtsboard sharks, we offer the original finding that muscle tone was strongest during active wakefulness, lower in quietly awake sharks, and lowest in sleeping sharks. We also offer several critical suggestions on how to improve techniques for characterizing sleep electrophysiology in future studies on elasmobranchs, particularly for those that swim continuously. Ultimately, these approaches will provide important insights into the evolutionary confluence of behaviors typically associated with wakefulness and sleep. Research Highlights: Some fishes need to swim to breathe, which makes it difficult to identify sleep because the animal is continuously moving. Here, we present relaxed muscle tone as an electrophysiological characteristic of sleep in draughtsboard sharks. [ABSTRACT FROM AUTHOR]

Background: Studies show that the two peak heights of electroencephalographic bicoherence (pBIC-high, pBIC-low) decrease after incision and are restored by fentanyl administration. We investigated whether pBICs are good indicators for adequacy of analgesia during surgery. Methods: After local ethical committee approval, we enrolled 50 patients (27–65 years, ASA-PS I or II) who were scheduled elective surgery. Besides standard anesthesia monitors, to assess pBICs, we used a BIS monitor and freeware Bispectrum Analyzer for A2000. Fentanyl 5 µg/kg was completely administered before incision, and anesthesia was maintained with sevoflurane. After skin incision, when the peak of pBIC-high or pBIC-low decreased by 10% in absolute value (named LT10-high and LT10-low groups in order) or when either peak decreased to below 20% (BL20-high and BL20-low groups), an additional 1 g/kg of fentanyl was administered to examine its effect on the peak that showed a decrease. Results: The mean values and standard deviation for pBIC-high 5 min before fentanyl administration, at the time of fentanyl administration, and 5 min after fentanyl administration for LT10-high group were 39.8% (10.9%), 26.9% (10.5%), and 35.7% (12.5%). And those for pBIC-low for LT10-low group were 39.5% (6.0%), 26.8% (6.4%) and 35.0% (7.0%). Those for pBIC-high for BL20-high group were 26.3% (5.6%), 16.5% (2.6%), and 25.7% (7.0%). And those for pBIC-low for BL20-low group were 26.7% (4.8%), 17.4% (1.8%) and 26.9% (5.7%), respectively. Meanwhile, at these trigger points, hemodynamic parameters didn't show significant changes. Conclusion: Superior to standard anesthesia monitoring, pBICs are better indicators of analgesia during surgery. Trial registry: Clinical trial Number and registry URL: UMIN ID: UMIN000042843 https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr%5fview.cgi?recptno = R000048907 [ABSTRACT FROM AUTHOR]

Background: Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications. Methods: This paper proposes a patient-independent seizure prediction model, named MFCC-CNN, to improve the generalization ability. MFCC-CNN model introduces Mel-Frequency Cepstrum Coefficients (MFCC) features and Linear Predictive Cepstral Coefficients (LPCC) features concentrated in the low frequency region, which contains more detailed information. Convolutional neural network (CNN) is used to construct a seizure prediction model. Results: Experimental results showed that the proposed model obtained accuracy of 96 % , sensitivity of 92 % , specificity of 84 % and F1-score of 85 % for 24 cases in CNHB-MIT dataset. The overall performance of MFCC-CNN model is better than the other models. Conclusion: MFCC-CNN model does not need to be specifically customized for different patients. As a patient-independent seizure prediction model, it has good generalization ability. [ABSTRACT FROM AUTHOR]

A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed for seizure detection on long-term EEG. The CNN-Reformer consists of two main parts: the Data Reshaping (DR) module and the Efficient Attention and Concentration (EAC) module. This framework reduces network parameters while retaining effective feature extraction of multi-channel EEGs, thereby improving model computational efficiency and real-time performance. Initially, the raw EEG signals undergo Discrete Wavelet Transform (DWT) for signal filtering, and then fed into the DR module for data compression and reshaping while preserving local features. Subsequently, these local features are sent to the EAC module to extract global features and perform categorization. Post-processing involving sliding window averaging, thresholding, and collar techniques is further deployed to reduce the false detection rate (FDR) and improve detection performance. On the CHB-MIT scalp EEG dataset, our method achieves an average sensitivity of 97.57%, accuracy of 98.09%, and specificity of 98.11% at segment-based level, and a sensitivity of 96.81%, along with FDR of 0.27/h, and latency of 17.81 s at the event-based level. On the SH-SDU dataset we collected, our method yielded segment-based sensitivity of 94.51%, specificity of 92.83%, and accuracy of 92.81%, along with event-based sensitivity of 94.11%. The average testing time for 1 h of multi-channel EEG signals is 1.92 s. The excellent results and fast computational speed of the CNN-Reformer model demonstrate its potential for efficient seizure detection. [ABSTRACT FROM AUTHOR]

Background: Neonatal seizures are diagnostically challenging and predominantly electrographic-only. Multichannel video continuous electroencephalography (cEEG) is the gold standard investigation, however, out-of-hours access to neurophysiology support can be limited. Automated seizure detection algorithms (SDAs) are designed to detect changes in EEG data, translated into user-friendly seizure probability trends. The aim of this study was to evaluate the diagnostic accuracy of the Persyst neonatal SDA in an intensive care setting. Methods: Single-centre retrospective service evaluation study in neonates undergoing cEEG during intensive care admission to Great Ormond Street Hospital (GOSH) between May 2019 and December 2022. Neonates with <44 weeks corrected gestational age, who had a cEEG recording duration >60 minutes, whilst inpatient in intensive care, were included in the study. One-hour cEEG clips were created for all cases (seizures detected) and controls (seizure-free) and analysed by the Persyst neonatal SDA. Expert neurophysiology reports of the cEEG recordings were used as the gold standard for diagnostic comparison. A receiver operating characteristic (ROC) curve was created using the highest seizure probability in each recording. Optimal seizure probability thresholds for sensitivity and specificity were identified. Results: Eligibility screening produced 49 cases, and 49 seizure-free controls. Seizure prevalence within those patients eligible for the study, was approximately 19% with 35% mortality. The most common case seizure aetiology was hypoxic ischaemic injury (35%) followed by inborn errors of metabolism (18%). The ROC area under the curve was 0.94 with optimal probability thresholds 0.4 and 0.6. Applying a threshold of 0.6, produced 80% sensitivity and 98% specificity. Conclusions: The Persyst neonatal SDA demonstrates high diagnostic accuracy in identifying neonatal seizures; comparable to the accuracy of the standard Persyst SDA in adult populations, other neonatal SDAs, and amplitude integrated EEG (aEEG). Overdiagnosis of seizures is a risk, particularly from cEEG recording artefact. To fully examine its clinical utility, further investigation of the Persyst neonatal SDA's accuracy is required, as well as confirming the optimal seizure probability thresholds in a larger patient cohort. [ABSTRACT FROM AUTHOR]