Investigating neuromodulatory effect of transauricular vagus nerve stimulation on resting-state electroencephalography.

Purpose: The purpose of this study was to investigate the neuromodulatory effects of transauricular vagus nerve stimulation (taVNS) and determine optimal taVNS duration to induce the meaningful neuromodulatroty effects using resting-state electroencephalography (EEG). Method: Fifteen participants participated in this study and taVNS was applied to the cymba conchae for a duration of 40 min. Resting-state EEG was measured before and during taVNS application. EEG power spectral density (PSD) and brain network indices (clustering coefficient and path length) were calculated across five frequency bands (delta, theta, alpha, beta and gamma), respectively, to assess the neuromodulatory effect of taVNS. Moreover, we divided the whole brain region into the five regions of interest (frontal, central, left temporal, right temporal, and occipital) to confirm the neuromodulation effect on each specific brain region. Result: Our results demonstrated a significant increase in EEG frequency powers across all five frequency bands during taVNS. Furthermore, significant changes in network indices were observed in the theta and gamma bands compared to the pre-taVNS measurements. These effects were particularly pronounced after approximately 10 min of stimulation, with a more dominant impact observed after approximately 20-30 min of taVNS application. Conclusion: The findings of this study indicate that taVNS can effectively modulate the brain activity, thereby exerting significant effects on brain characteristics. Moreover, taVNS duration of approximately 20-30 min was considered appropriate for inducing a stable and efficient neuromodulatory effects. Consequently, these findings have the potential to contribute to research aimed at enhancing cognitive and motor functions through the modulation of EEG using taVNS. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-024-00361-8.

Understanding of Consciousness in Absence Seizures: A Literature Review.

Absence seizures are classically associated with behavioral arrest and transient deficits in consciousness, yet substantial variability exists in the severity of the impairment. Despite several decades of research on the topic, the pathophysiology of absence seizures and the mechanisms underlying behavioral impairment remain unclear. Several rationales have been proposed including widespread cortical deactivation, reduced perception of external stimuli, and transient suspension of the default mode network, among others. This review aims to summarize the current knowledge on the neural correlates of impaired consciousness in absence seizures. We review evidence from studies using animal models of absence epilepsy, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, and single photon emission computed tomography.

Parsing evoked and induced gamma response differences in Autism: A visual evoked potential study.

OBJECTIVE: Electroencephalography (EEG) measures of visual evoked potentials (VEPs) provide a targeted approach for investigating neural circuit dynamics. This study separately analyses phase-locked (evoked) and non-phase-locked (induced) gamma responses within the VEP to comprehensively investigate circuit differences in autism. METHODS: We analyzed VEP data from 237 autistic and 114 typically developing (TD) children aged 6-11, collected through the Autism Biomarkers Consortium for Clinical Trials (ABC-CT). Evoked and induced gamma (30-90 Hz) responses were separately quantified using a wavelet-based time-frequency analysis, and group differences were evaluated using a permutation-based clustering procedure. RESULTS: Autistic children exhibited reduced evoked gamma power but increased induced gamma power compared to TD peers. Group differences in induced responses showed the most prominent effect size and remained statistically significant after excluding outliers. CONCLUSIONS: Our study corroborates recent research indicating diminished evoked gamma responses in children with autism. Additionally, we observed a pronounced increase in induced power. Building upon existing ABC-CT findings, these results highlight the potential to detect variations in gamma-related neural activity, despite the absence of significant group differences in time-domain VEP components. SIGNIFICANCE: The contrasting patterns of decreased evoked and increased induced gamma activity in autistic children suggest that a combination of different EEG metrics may provide a clearer characterization of autism-related circuitry than individual markers alone.

Electroencephalographic insights into the pathophysiological mechanisms of emergence delirium in children and corresponding clinical treatment strategies.

Emergence delirium is a common postoperative complication in patients undergoing general anesthesia, especially in children. In severe cases, it can cause unnecessary self-harm, affect postoperative recovery, lead to parental dissatisfaction, and increase medical costs. With the widespread use of inhalation anesthetic drugs (such as sevoflurane and desflurane), the incidence of emergence delirium in children is gradually increasing; however, its pathogenesis in children is complex and unclear. Several studies have shown that age, pain, and anesthetic drugs are strongly associated with the occurrence of emergence delirium. Alterations in central neurophysiology are essential intermediate processes in the development of emergence delirium. Compared to adults, the pediatric nervous system is not fully developed; therefore, the pediatric electroencephalogram may vary slightly by age. Moreover, pain and anesthetic drugs can cause changes in the excitability of the central nervous system, resulting in electroencephalographic changes. In this paper, we review the pathogenesis of and prevention strategies for emergence delirium in children from the perspective of brain electrophysiology-especially for commonly used pharmacological treatments-to provide the basis for understanding the development of emergence delirium as well as its prevention and treatment, and to suggest future research direction.

Investigation of the acute impact of rosemary consumption on brain activity in healthy volunteers.

OBJECTIVES: Rosmarinus officinalis L. (rosemary) is a fragrant plant of the mint family, broadly known as a nourishment flavoring agent; it is additionally utilized in conventional people cures for its anti-inflammatory, diuretic, and antibacterial properties. Intense cognitive impacts from devouring plant-based flavonoids can be measured with electroencephalography (EEG), which records unconstrained brain movement. Brain activity can be evaluated amid independent states or whereas performing attentional assignments. This study aimed to determine the impact of rosemary consumption on cognitive consequences. METHODS: Twenty volunteers took part in the study. EEG was taken for each volunteer twice, before drinking rosemary extract and around one hour after drinking it. EEG information was recorded with a Micromed recording framework inspecting rate of 512 Hz. EEG signals were prepared to be utilized in EEGLAB, an open-source toolbox within the MATLAB environment. The information obtained after the EEG recording was compared with the preliminary EEG information. RESULTS: The signal's power spectral density in theta, delta, and beta frequency bands modestly increased in males and females. Even though there was a significant increase in power at the alpha frequency band in both sexes, this increment was not specific channel-wise. DISCUSSION: The obtained data are consistent with the expected results and similar studies conducted, suggesting that the consumption of rosemary is beneficial for cognitive function in the short term. It is anticipated that forthcoming long-term studies will support the existing data.

A neural signature for brain compensation in stroke with EEG and TMS: Insights from the DEFINE cohort study.

OBJECTIVE: This study aimed to explore the relationships between potential neurophysiological biomarkers and upper limb motor function recovery in stroke patients, specifically focusing on combining two neurophysiological markers: electroencephalography (EEG) and transcranial magnetic stimulation (TMS). METHODS: This cross-sectional study analyzed neurophysiological, clinical, and demographical data from 102 stroke patients from the DEFINE cohort. We searched for correlations of EEG and TMS measurements combined to build a prediction model for upper limb motor functionality, assessed by five outcomes, across five assessments: Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Finger Tapping Test (FTT), Nine-Hole Peg Test (9HPT), and Pinch Strength Test (PST). RESULTS: Our multivariate models agreed on a specific neural signature: higher EEG Theta/Alpha ratio in the frontal region of the lesioned hemisphere is associated with poorer motor outcomes, while increased MEP amplitude in the non-lesioned hemisphere correlates with improved motor function. These relationships are held across all five motor assessments, suggesting the potential of these neurophysiological measures as recovery biomarkers. CONCLUSION: Our findings indicate a potential neural signature of brain compensation in which lower frequencies of EEG power are increased in the lesioned hemisphere, and lower corticospinal excitability is also increased in the non-lesioned hemisphere. We discuss the meaning of these findings in the context of motor recovery in stroke.

Successful Diagnosis of Neuroleptic Malignant Syndrome in an Unconscious Patient Using Amplitude-Integrated Electroencephalography: A Case Report.

Neuroleptic malignant syndrome (NMS) is a rare but life-threatening medical condition often characterized by altered consciousness and clinical features resembling seizures. This case report presents a unique and successful diagnosis of NMS in an unconscious patient with an unknown medical history. We demonstrate the potential utility of amplitude-integrated electroencephalography (aEEG) as a valuable tool for the differential diagnosis of seizure-like medical conditions, including NMS. The application of aEEG allowed for early diagnosis and prompt initiation of appropriate treatment, potentially contributing to improved patient outcomes.

Evaluating the Impact of Point-of-Care Electroencephalography on Length of Stay in the Intensive Care Unit: Subanalysis of the SAFER-EEG Trial.

BACKGROUND: Electroencephalography (EEG) is needed to diagnose nonconvulsive seizures. Prolonged nonconvulsive seizures are associated with neuronal injuries and deleterious clinical outcomes. However, it is uncertain whether the rapid identification of these seizures using point-of-care EEG (POC-EEG) can have a positive impact on clinical outcomes. METHODS: In a retrospective subanalysis of the recently completed multicenter Seizure Assessment and Forecasting with Efficient Rapid-EEG (SAFER-EEG) trial, we compared intensive care unit (ICU) length of stay (LOS), unfavorable functional outcome (modified Rankin Scale score ≥ 4), and time to EEG between adult patients receiving a US Food and Drug Administration-cleared POC-EEG (Ceribell, Inc.) and those receiving conventional EEG (conv-EEG). Patient records from January 2018 to June 2022 at three different academic centers were reviewed, focusing on EEG timing and clinical outcomes. Propensity score matching was applied using key clinical covariates to control for confounders. Medians and interquartile ranges (IQRs) were calculated for descriptive statistics. Nonparametric tests (Mann-Whitney U-test) were used for the continuous variables, and the χ2 test was used for the proportions. RESULTS: A total of 283 ICU patients (62 conv-EEG, 221 POC-EEG) were included. The two populations were matched using demographic and clinical characteristics. We found that the ICU LOS was significantly shorter in the POC-EEG cohort compared to the conv-EEG cohort (3.9 [IQR 1.9-8.8] vs. 8.0 [IQR 3.0-16.0] days, p = 0.003). Moreover, modified Rankin Scale functional outcomes were also different between the two EEG cohorts (p = 0.047). CONCLUSIONS: This study reveals a significant association between early POC-EEG detection of nonconvulsive seizures and decreased ICU LOS. The POC-EEG differed from conv-EEG, demonstrating better functional outcomes compared with the latter in a matched analysis. These findings corroborate previous research advocating the benefit of early diagnosis of nonconvulsive seizure. The causal relationship between the type of EEG and metrics of interest, such as ICU LOS and functional/clinical outcomes, needs to be confirmed in future prospective randomized studies.

Clinical and electroencephalographic correlates of carbamazepine-associated hiccups in epileptic patients.

Carbamazepine, a commonly prescribed antiepileptic drug, is known to induce hiccups in a subset of epileptic patients. Although relatively uncommon, can have significant clinical implications. This comprehensive review delves into the clinical and electroencephalographic correlates of carbamazepine-associated hiccups, aiming to enhance understanding and management of this neurological side effect. The authors' review synthesizes qualitative epidemiological data, revealing that carbamazepine-induced hiccups occur in a subset of patients receiving the medication, with reported incidence rates ranging from 2.5 to 40%. Despite its relatively low prevalence, hiccups pose substantial challenges for patients and healthcare providers. Complications associated with carbamazepine-induced hiccups include disruption of sleep, impaired social functioning, and decreased quality of life, underscoring the clinical significance of this side effect. Effective management strategies can be implemented through a multidisciplinary approach, including collaboration among neurologists, pharmacists, and other healthcare professionals. These may include dose adjustments, medication discontinuation, and adjunctive therapies such as diaphragmatic breathing exercises or acupuncture. Additionally, close monitoring for adverse effects and timely intervention are essential to mitigate the impact of hiccups on patient well-being. Essentially, carbamazepine-induced hiccups represent a clinically relevant phenomenon that warrants attention in the management of epilepsy. By recognizing the clinical manifestations, understanding the underlying pathophysiology, and implementing evidence-based management strategies, healthcare providers can optimize patient care and improve outcomes in this patient population.

Relationship between continuous EEG monitoring findings and prognostic factors in patients with status epilepticus.

BACKGROUND AND PURPOSE: Little information is available regarding the use of continuous electroencephalography (cEEG) monitoring findings to predict the prognosis of patients with status epilepticus, which could aid in prognostication. This study investigated the relationship between cEEG monitoring findings and various prognostic indicators in patients with status epilepticus. METHODS: We reviewed the clinical profiles and cEEG monitoring data of 28 patients with status epilepticus over a ten-year period. Patient demographics, etiology, EEG features, duration of hospital stay, number of antiseizure medications, and outcome measures were analyzed. Functional outcomes were assessed using the modified Rankin Scale (mRS), which evaluates the degree of daily living impairment and dependence on others resulting from neurological injury. RESULTS: Patients exhibiting electrographic status epilepticus (ESE) demonstrated significantly longer duration of status epilepticus (77.75 ± 58.25 vs. 39.86 ± 29.81 h, p = 0.024) and total length of hospital stay (13.00 ± 6.14 vs. 8.14 ± 5.66 days, p = 0.038) when compared to those with ictal-interictal continuum (IIC). Individuals who displayed any increase in modified Rankin Scale (mRS) score between their premorbid state and discharge also had significantly longer duration of status epilepticus (74.09 ± 34.94 vs. 51.56 ± 54.25 h, p = 0.041) and total length of hospital stay (15.89 ± 6.05 vs. 8.05 ± 4.80 days, p = 0.004) when compared to those who showed no difference. The most prevalent etiology of status epilepticus in our study was chronic structural brain lesions. CONCLUSIONS: This suggests that ESE may serve as a predictor of prolonged duration of status epilepticus and increased hospitalization among patients with status epilepticus.

Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning.

Purpose: This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data. Methods: We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution. Results: We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals. Conclusion: The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.

Contributions of brain regions to machine learning-based classifications of attention deficit hyperactivity disorder (ADHD) utilizing EEG signals.

OBJECTIVE: The study presented focuses on the creation of a machine learning (ML) model that uses electrophysiological (EEG) data to identify kids with attention deficit hyperactivity disorder (ADHD) from healthy controls. The EEG signals are acquired during cognitive tasks to distinguish children with ADHD from their counterparts. METHODOLOGY: The EEG data recorded in cognitive exercises was filtered using low pass Bessel filter and notch filters to remove artifacts, by the data set owners. To identify unique EEG patterns, we used many well-known classifiers, including Naïve Bayes (NB), Random Forest, Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost and Linear Discriminant Analysis (LDA), to identify distinct EEG patterns. Input features comprised EEG data from nineteen channels, individually and in combination. FINDINGS: Study indicates that EEG-based categorization can differentiate between individuals with ADHD and healthy individuals with accuracy of 84%. The RF classifier achieved a maximum accuracy of 0.84 when particular region combinations were used. Evaluation of classification performance utilizing hemisphere-specific EEG data yielded promising outcomes, particularly in the right hemisphere channels. NOVELTY: The study goes beyond traditional methodologies by investigating the effect of regional data on categorization results. The contributions of various brain regions to these classifications are being extensively researched. Understanding the role of different brain regions in ADHD can lead to better diagnosis and treatment options for individuals with ADHD. The study of categorization ability, utilizing EEG data specific to each hemisphere, particularly channels in the right hemisphere region, provides further granularity to the findings.

State of the art of brain function detection technologies in robot-assisted lower limb rehabilitation.

BACKGROUND: With an aging population, the prevalence of neurological disorders is increasing, leading to a rise in lower limb movement disorders and, in turn, a growing need for rehabilitation training. Previous neuroimaging studies have shown a growing scientific interest in the study of brain mechanisms in robot-assisted lower limb rehabilitation (RALLR). OBJECTIVE: This review aimed to determine differences in neural activity patterns during different RALLR tasks and the impact on neurofunctional plasticity. METHODS: Sixty-five articles in the field of RALLR were selected and tested using three brain function detection technologies (BFDT). RESULTS: Most studies have focused on changes in activity in various regions of the cerebral cortex during different lower limb rehabilitation tasks, but have also increasingly focused on functional changes in other cortical and deep subcortical structures. Our analysis also revealed a neglect of certain task types. CONCLUSION: We identify and discuss future research directions that may contribute to a clear understanding of neural functional plasticity under different RALLR tasks.

Problematic Gaming and Gambling: A Systematic Review of Task-Specific EEG Protocols.

Even though gaming and gambling bear similar problematic behavioral aspects, there are no recognizable neurophysiological biomarkers or features characterizing and/or distinguishing these conditions. A systematic review of the literature with a focus on methods was performed in PubMed, Scopus, Web of Science (Web of Science Core Collection), EBSCOhost Research Databases (APA PsycINFO; APA PsycArticles; OpenDissertations; ERIC) databases. Following search terms were used to search the databases: ERP, "event related potential*", EP, "evoked potential*", SS, "steady state", EEG, electroencephal*; gam*. Data about the participants (total number, gender, age), main aim of the study and information about the experimental setup (experimental task description, stimuli used, ERPs measured (latency windows and placement of the electrodes), process evaluated) was extracted. A total of 24 studies were revised (problematic gaming - 16, pathological gambling - 8). The experimental protocols could be grouped into 3 main target domains (Cue-reactivity, General Information processing and Reward Processes & Risk Assessment). Sample-related limitations (small sample sizes, gender differences, differences between the groups regarding potential confounding variables) and heterogeneity regarding the experimental tasks, implementation and interpretation reviewed. Gambling-related research is highly focused on the investigation of the reward-related processes, whereas gaming-related research is mostly focused on the altered aspects of more general information processing. A vast heterogeneity regarding the ERP experimental paradigms being used and lack of clear guidelines and standardized procedures prevents identification of measures capable to reliably discriminate or characterize the population susceptible to addictive behavior or being able to diagnose and monitor these disorders.

Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.

This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training from EEG recordings. The proposed model is a ten-layered CNN that contains a max pooling layer, a Global Average Pooling layer, four convolution layers, two dropout layers for overfitting prevention, and two fully connected layers. The efficiency of the suggested method was assessed using the ten-fold-cross validation technique and the EEG records of 14 healthy subjects and 14 SZ patients. The obtained mean accuracy score was 99.18 %. To confirm the high mean accuracy attained, we tested the model on unseen data with a near-perfect accuracy score (almost 100 %). In addition, the results we obtained outperform numerous other comparable works.

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.

Clinical Applicability and Safety of Conventional Frame-Based Stereotactic Techniques for Stereoelectroencephalography.

Objective: Stereoelectroencephalography (SEEG) is increasingly being recognized as an important invasive modality for presurgical evaluation of epilepsy. This study focuses on the clinical and technical considerations of SEEG investigations when using conventional frame-based stereotaxy, drawing on institutional experience and a comprehensive review of relevant literature. Methods: This retrospective observational study encompassed the surgical implantation of 201 SEEG electrodes in 16 epilepsy patients using a frame-based stereotactic instrument at a single tertiary-level center. We provide detailed descriptions of the operative procedures and technical nuances for bilateral and multiple SEEG insertions, along with several illustrative cases. Additionally, we present a literature review on the technical aspects of the SEEG procedure, discussing its clinical implications and potential risks. Results: Frame-based SEEG electrode placements were successfully performed through sagittal arc application, with the majority (81.2%) of cases being bilateral and involving up to 18 electrodes in a single operation. The median skin-to-skin operation time was 162 minutes (interquartile range [IQR], 145-200), with a median of 13 minutes (IQR, 12-15) per electrode placement for time efficiency. There were two occurrences (1.0%) of electrode misplacement and one instance (0.5%) of a postoperative complication, which manifested as a delayed intraparenchymal hemorrhage. Following SEEG investigation, 11 patients proceeded with surgical intervention, resulting in favorable seizure outcomes for nine (81.8%) and complete remission for eight cases (72.7%). Conclusion: Conventional frame-based stereotactic techniques remain a reliable and effective option for bilateral and multiple SEEG electrode placements. While SEEG is a suitable approach for selected patients who are strong candidates for epilepsy surgery, it is important to remain vigilant concerning the potential risks of electrode misplacement and hemorrhagic complications.

EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.

Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model's overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments.

Conductive Hydrogel Tapes for Tripolar EEG: A Promising Solution to Paste-Related Challenges.

Electroencephalography (EEG) remains pivotal in neuroscience for its non-invasive exploration of brain activity, yet traditional electrodes are plagued with artifacts and the application of conductive paste poses practical challenges. Tripolar concentric ring electrode (TCRE) sensors used for EEG (tEEG) attenuate artifacts automatically, improving the signal quality. Hydrogel tapes offer a promising alternative to conductive paste, providing mess-free application and reliable electrode-skin contact in locations without hair. Since the electrodes of the TCRE sensors are only 1.0 mm apart, the impedance of the skin-to-electrode impedance-matching medium is critical. This study evaluates four hydrogel tapes' efficacies in EEG electrode application, comparing impedance and alpha wave characteristics. Healthy adult participants underwent tEEG recordings using different tapes. The results highlight varying impedances and successful alpha wave detection despite increased tape-induced impedance. MATLAB's EEGLab facilitated signal processing. This study underscores hydrogel tapes' potential as a convenient and effective alternative to traditional paste, enriching tEEG research methodologies. Two of the conductive hydrogel tapes had significantly higher alpha wave power than the other tapes, but were never significantly lower.

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.