Unsupervised heterogeneous domain adaptation for EEG classification.

$Objective.$ Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification. $Approach.$ In this article, we propose a novel model named Informative Representation Fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e., independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the Maximum Mean Discrepancy is utilized to align the distributions of the source and target domains based on the fused features. $Main~results.$ Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model. $Significance.$ This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what's more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.

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.

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.

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.

Beta-Band power modulation in the human amygdala differentiates between go/no-go responses in an arm reaching task.

Introduction Traditionally known for its involvement in emotional processing, the amygdala's involvement in motor control remains relatively unexplored, with sparse investigation into the neural mechanisms governing amygdaloid motor movement and inhibition. Objective This study aimed to characterize the amygdaloid beta-band (13-30 Hz) power between "Go" and "No-go" trials of an arm reaching task. Methods Ten participants with drug-resistant epilepsy implanted with stereoelectroencephalographic (SEEG) electrodes in the amygdala were enrolled in this study. SEEG data was recorded throughout discrete phases of a Direct Reach Go/No-go task, during which participants reached a touchscreen monitor or withheld movement based on a colored cue. Multitaper power analysis along with Wilcoxon signed-rank and Yates-corrected Z tests were used to assess significant modulations of beta power between the Response and Fixation (baseline) phases in the "Go" and "No-go" conditions. Results In the "Go" condition, nine out of the ten participants showed a significant decrease in relative beta-band power during the Response phase (p ≤ 0.0499). In the "No-go" condition, eight out of the ten participants presented a statistically significant increase in relative beta-band power during the Response phase (p ≤ 0.0494). Four out of the eight participants with electrodes in the contralateral hemisphere and seven out of the eight participants with electrodes in the ipsilateral hemisphere presented significant modulation in beta-band power in both the "Go" and "No-go" conditions. At the group level, no significant differences were found between the contralateral and ipsilateral sides or between genders. Conclusion This study reports beta-band power modulation in the human amygdala during voluntary movement in the setting of motor execution and inhibition. This finding supplements prior research in various brain regions associating beta-band power with motor control. The distinct beta-power modulation observed between these response conditions suggests involvement of amygdaloid oscillations in differentiating between motor inhibition and execution.

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.

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.

Spatiotemporal alterations in the brain oscillations of Arctic explorers.

BACKGROUND: The limited understanding of the physiology and psychology of polar expedition explorers has prompted concern over the potential cognitive impairments caused by exposure to extreme environmental conditions. Prior research has demonstrated that such stressors can negatively impact cognitive function, sleep quality, and behavioral outcomes. Nevertheless, the impact of the polar environment on neuronal activity remains largely unknown. METHODS: In this study, we aimed to investigate spatiotemporal alterations in brain oscillations of 13 individuals (age range: 22 - 48 years) who participated in an Arctic expedition. We utilized electroencephalography (EEG) to record cortical activity before and during the Arctic journey, and employed standardized low resolution brain electromagnetic tomography to localize changes in alpha, beta, theta, and gamma activity. RESULTS: Our results reveal a significant increase in the power of theta oscillations in specific regions of the Arctic, which differed significantly from pre-expedition measurements. Furthermore, microstate analysis demonstrated a significant reduction in the duration of microstates (MS) D and alterations in the local synchrony of the frontoparietal network. CONCLUSION: Overall, these findings provide novel insights into the neural mechanisms underlying adaptation to extreme environments. These findings have implications for understanding the cognitive consequences of polar exploration and may inform strategies to mitigate potential neurological risks associated with such endeavors. Further research is warranted to elucidate the long-term effects of Arctic exposure on brain function.

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.

Obedience to authority reduces cognitive conflict before an action.

How obeying orders impacts moral decision-making remains an open question, despite its significant societal implications. The goal of this study was to determine if cognitive conflict, indexed by mid-frontal theta activity observed before an action, is influenced by the context of obedience. Participants came in pairs and were assigned roles as either agent or victim. Those in the agent role could either decide freely or follow the experimenter's instructions to administer (or refrain from administering) a mildly painful electric shock to the victim in exchange for a small monetary reward. Mid-frontal theta activity was recorded before the agent made their keypress. Results indicated that mid-frontal theta activity was reduced when participants obeyed the experimenter's orders compared to when they acted of their own volition, even though the outcomes of the actions were similar. This finding suggests that obeying orders diminishes cognitive conflict preceding moral decisions that could harm another person. This study sheds light on a potential mechanism explaining how obedience can blurr morality and lessen our natural aversion to harming others.

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.

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.

Facing infant cuteness: How nurturing care motivation and oxytocin system gene methylation are associated with responses to baby schema features.

Baby schema features are a specific set of physical features-including chubby cheeks, large, low-set eyes, and a large, round head-that have evolutionary adaptive value in their ability to trigger nurturant care. In this study among nulliparous women (N = 81; M age = 23.60, SD = 0.44), we examined how sensitivity to these baby schema features differs based on individual variations in nurturant care motivation and oxytocin system gene methylation. We integrated subjective ratings with measures of facial expressions and electroencephalography (EEG) in response to infant faces that were manipulated to contain more or less pronounced baby schema features. Linear mixed effects analyses demonstrated that infants with more pronounced baby schema features were rated as cuter and participants indicated greater motivation to take care of them. Furthermore, infants with more pronounced baby schema features elicited stronger smiling responses and enhanced P2 and LPP amplitudes compared to infants with less pronounced baby schema features. Importantly, individual differences significantly predicted baby schema effects. Specifically, women with low OXTR methylation and high nurturance motivation showed enhanced differentiation in automatic neurophysiological responses to infants with high and low levels of baby schema features. These findings highlight the importance of considering individual differences in continued research to further understand the complexities of sensitivity to child cues, including facial features, which will improve our understanding of the intricate neurobiological system that forms the basis of caregiving behavior.

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.

State of the art and future directions for measuring event-related potentials during cycling exercise: a systematic review.

Intro: Electroencephalography (EEG) is a technique for measuring brain activity that is widely used in neuroscience research. Event-related potentials (ERPs) in the EEG make it possible to study sensory and cognitive processes in the brain. Previous reports have shown that aerobic exercise can have an impact on components of ERPs such as amplitude and latency. However, they focused on the measurement of ERPs after exercise. Objectives: The aim of this systematic review was to investigate the feasibility of measuring ERPs during cycling, and to assess the impact of cycling on ERPs during cycling. Methods: We followed the PRISMA guidelines for new systematic reviews. To be eligible, studies had to include healthy adults and measure ERPs during cycling. All articles were found using Google Scholar and by searching references. Data extracted from the studies included: objectives of ERP studies, ERP paradigm, EEG system, study population data, exercise characteristics (duration, intensity, pedaling cadence), and ERP and behavioral outcomes. The Cochrane Risk of Bias 2 tool was used to assess study bias. Results: Twenty studies were selected. The effect of cycling on ERPs was mainly based on a comparison of P3 wave amplitude between cycling and resting states, using an attentional task. The ERP paradigm most often used was the auditory oddball task. Exercise characteristics and study methods varied considerably. Discussion: It is possible to measure ERPs during cycling under conditions that are likely to introduce more artifacts, including a 3-h athletic exercise session and cycling outdoors. Secondly, no assessment of the effect of cycling on ERPs was possible, because the methods differed too widely between studies. In addition, the theories proposed to explain the results sometimes seemed to contradict each other. Although most studies reported significant results, the direction of the effects was inconsistent. Finally, we suggest some areas for improvement for future studies on the subject.

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.

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.