A multi-center, randomized, double-blind, sham-stimulation controlled study of transcranial magnetic stimulation with precision navigation for the treatment of multiple system atrophy.

BACKGROUND: Multiple system atrophy (MSA) is recognized as an atypical Parkinsonian syndrome, distinguished by a more rapid progression than that observed in Parkinson's disease. Unfortunately, the prognosis for MSA remains poor, with a notable absence of globally recognized effective treatments. Although preliminary studies suggest that transcranial magnetic stimulation (TMS) could potentially alleviate clinical symptoms in MSA patients, there is a significant gap in the literature regarding the optimal stimulation parameters. Furthermore, the field lacks consensus due to the paucity of robust, large-scale, multicenter trials. METHODS: This investigation is a multi-center, randomized, double-blind, sham-controlled trial. We aim to enroll 96 individuals diagnosed with MSA, categorized into Parkinsonian type (MSA-P) and cerebellar type (MSA-C) according to their predominant clinical features. Participants will be randomly allocated in a 1:1 ratio to either the TMS or sham stimulation group. Utilizing advanced navigation techniques, we will ensure precise targeting for the intervention, applying theta burst stimulation (TBS). To assess the efficacy of TBS on both motor and non-motor functions, a comprehensive evaluation will be conducted using internationally recognized clinical scales and gait analysis. To objectively assess changes in brain connectivity, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) will be employed as sensitive indicators before and after the intervention. DISCUSSION: The primary aim of this study is to ascertain whether TBS can alleviate both motor and non-motor symptoms in patients with MSA. Additionally, a critical component of our research involves elucidating the underlying mechanisms through which TBS exerts its potential therapeutic effects. ETHICS AND DISSEMINATION: All study protocols have been reviewed and approved by the First Affiliated Medical Ethics Committee of the Air Force Military Medical University (KY20232118-F-1). TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300072658. Registered on 20 June 2023.

Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. The authors believe the processes can detect various neurodevelopmental problems in children utilizing EEG signals.

Improving working memory by electrical stimulation and cross-frequency coupling.

Working memory (WM) is essential for the temporary storage and processing of information required for complex cognitive tasks and relies on neuronal theta and gamma oscillations. Given the limited capacity of WM, researchers have investigated various methods to improve it, including transcranial alternating current stimulation (tACS), which modulates brain activity at specific frequencies. One particularly promising approach is theta-gamma peak-coupled-tACS (TGCp-tACS), which simulates the natural interaction between theta and gamma oscillations that occurs during cognitive control in the brain. The aim of this study was to improve WM in healthy young adults with TGCp-tACS, focusing on both behavioral and neurophysiological outcomes. Thirty-one participants completed five WM tasks under both sham and verum stimulation conditions. Electroencephalography (EEG) recordings before and after stimulation showed that TGCp-tACS increased power spectral density (PSD) in the high-gamma region at the stimulation site, while PSD decreased in the theta and delta regions throughout the cortex. From a behavioral perspective, although no significant changes were observed in most tasks, there was a significant improvement in accuracy in the 14-item Sternberg task, indicating an improvement in phonological WM. In conclusion, TGCp-tACS has the potential to promote and improve the phonological component of WM. To fully realize the cognitive benefits, further research is needed to refine the stimulation parameters and account for individual differences, such as baseline cognitive status and hormonal factors.

Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment.

Introduction: In operational environments, human interaction and cooperation between individuals are critical to efficiency and safety. These states are influenced by individuals' cognitive and emotional states. Human factor research aims to objectively quantify these states to prevent human error and maintain constant performances, particularly in high-risk settings such as aviation, where human error and performance account for a significant portion of accidents. Methods: Thus, this study aimed to evaluate and validate two novel methods for assessing the degree of cooperation among professional pilots engaged in real-flight simulation tasks. In addition, the study aimed to assess the ability of the proposed metrics to differentiate between the expertise levels of operating crews based on their levels of cooperation. Eight crews were involved in the experiments, consisting of four crews of Unexperienced pilots and four crews of Experienced pilots. An expert trainer, simulating air traffic management communication on one side and acting as a subject matter expert on the other, provided external evaluations of the pilots' mental states during the simulation. The two novel approaches introduced in this study were formulated based on circular correlation and mutual information techniques. Results and discussion: The findings demonstrated the possibility of quantifying cooperation levels among pilots during realistic flight simulations. In addition, cooperation time is found to be significantly higher (p < 0.05) among Experienced pilots compared to Unexperienced ones. Furthermore, these preliminary results exhibited significant correlations (p < 0.05) with subjective and behavioral measures collected every 30 s during the task, confirming their reliability.

E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks.

The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .

Pre-anesthetic brain network metrics as predictors of individual propofol sensitivity.

BACKGROUND AND OBJECTIVE: Numerous factors, including demographic characteristics, have been implicated in modulating individual sensitivity to propofol; however, substantial inter-individual differences persist even after accounting for these factors. This study thus aimed to explore whether pre-anesthesia brain functional network metrics correlate with an individual's sensitivity to propofol. METHODS: A total of 54 subjects, including 30 patients and 24 healthy volunteers, were enrolled. Propofol was administered via a target-controlled infusion device, and anesthesia depth was monitored using a bispectral index monitor. Sensitivity to propofol was quantified using the induction time, measured from infusion onset to the bispectral index, which reached 60. Brain functional network metrics indicative of functional integration and segregation, centrality, and network resilience were computed from pre-anesthetic 60-channel EEG recordings. Linear regression analysis and machine learning predictive models were applied to evaluate the contribution of pre-anesthesia network metrics in predicting individual sensitivity to propofol. RESULTS: Our analysis results revealed that subjects could be categorized into high- or low-sensitivity groups based on their induction time. Individuals with low sensitivity exhibited a greater network degree, clustering coefficient, global efficiency, and betweenness centrality, along with reduced modularity and assortativity coefficient in the alpha band. Furthermore, alpha band network metrics were significantly correlated with individual induction time. Leveraging these network metrics as features enabled the classification of individuals into high- or low-sensitivity groups with an accuracy of 94%. CONCLUSIONS: Using a clinically relevant endpoint that signifies the level of anesthesia suitable for surgical procedures, this study underscored the robust correlation between pre-anesthesia alpha-band network metrics and individual sensitivity to propofol in a cohort that included both patients and healthy volunteers. Our findings offer preliminary insights into the potential utility of pre-anesthetic brain status assessment to predict propofol sensitivity on an individual basis, which may help to develop a more accurate personalized anesthesia plan.

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis. METHODS: Twenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups. RESULTS: Among 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA. CONCLUSIONS: The application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA.

Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.

Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.

Beta tACS of varying intensities differentially affect resting-state and movement-related M1-M1 connectivity.

Due to the interconnected nature of the brain, changes in one region are likely to affect other structurally and functionally connected regions. Emerging evidence indicates that single-site transcranial alternating current stimulation (tACS) can modulate functional connectivity between stimulated and interconnected unstimulated brain regions. However, our understanding of the network response to tACS is incomplete. Here, we investigated the effect of beta tACS of different intensities on phase-based connectivity between the left and right primary motor cortices in 21 healthy young adults (13 female; mean age 24.30 ± 4.84 years). Participants underwent four sessions of 20 min of 20 Hz tACS of varying intensities (sham, 0.5 mA, 1.0 mA, or 1.5 mA) applied to the left primary motor cortex at rest. We recorded resting-state and event-related electroencephalography (EEG) before and after tACS, analyzing changes in sensorimotor beta (13-30 Hz) imaginary coherence (ImCoh), an index of functional connectivity. Event-related EEG captured movement-related beta activity as participants performed self-paced button presses using their right index finger. For resting-state connectivity, we observed intensity-dependent changes in beta ImCoh: sham and 0.5 mA stimulation resulted in an increase in beta ImCoh, while 1.0 mA and 1.5 mA stimulation decreased beta ImCoh. For event-related connectivity, 1.5 mA stimulation decreased broadband ImCoh (4-90 Hz) during movement execution. None of the other stimulation intensities significantly modulated event-related ImCoh during movement preparation, execution, or termination. Interestingly, changes in ImCoh during movement preparation following 1.0 mA and 1.5 mA stimulation were significantly associated with participants' pre-tACS peak beta frequency, suggesting that the alignment of stimulation frequency and peak beta frequency affected the extent of neuromodulation. Collectively, these results suggest that beta tACS applied to a single site influences connectivity within the motor network in a manner that depends on the intensity and frequency of stimulation. These findings have significant implications for both research and clinical applications.

A comprehensive prediction model of drug-refractory epilepsy based on combined clinical-EEG microstate features.

Background: Epilepsy is a chronic neurological disorder characterized by recurrent seizures that significantly impact patients' quality of life. Identifying predictors is crucial for early intervention. Objective: Electroencephalography (EEG) microstates effectively describe the resting state activity of the human brain using multichannel EEG. This study aims to develop a comprehensive prediction model that integrates clinical features with EEG microstates to predict drug-refractory epilepsy (DRE). Design: Retrospective study. Methods: This study encompassed 226 patients with epilepsy treated at the epilepsy center of a tertiary hospital between October 2020 and May 2023. Patients were categorized into DRE and non-DRE groups. All patients were randomly divided into training and testing sets. Lasso regression combined with Stepglm [both] algorithms was used to screen independent risk factors for DRE. These risk factors were used to construct models to predict the DRE. Three models were constructed: a clinical feature model, an EEG microstate model, and a comprehensive prediction model (combining clinical-EEG microstates). A series of evaluation methods was used to validate the accuracy and reliability of the prediction models. Finally, these models were visualized for display. Results: In the training and testing sets, the comprehensive prediction model achieved the highest area under the curve values, registering 0.99 and 0.969, respectively. It was significantly superior to other models in terms of the C-index, with scores of 0.990 and 0.969, respectively. Additionally, the model recorded the lowest Brier scores of 0.034 and 0.071, respectively, and the calibration curve demonstrated good consistency between the predicted probabilities and observed outcomes. Decision curve analysis revealed that the model provided significant clinical net benefit across the threshold range, underscoring its strong clinical applicability. We visualized the comprehensive prediction model by developing a nomogram and established a user-friendly website to enable easy application of this model (https://fydxh.shinyapps.io/CE_model_of_DRE/). Conclusion: A comprehensive prediction model for DRE was developed, showing excellent discrimination and calibration in both the training and testing sets. This model provided an intuitive approach for assessing the risk of developing DRE in patients with epilepsy.

ComBat models for harmonization of resting-state EEG features in multisite studies.

OBJECTIVE: Pooling multisite resting-state electroencephalography (rsEEG) datasets may introduce bias due to batch effects (i.e., cross-site differences in the rsEEG related to scanner/sample characteristics). The Combining Batches (ComBat) models, introduced for microarray expression and adapted for neuroimaging, can control for batch effects while preserving the variability of biological covariates. We aim to evaluate four ComBat harmonization methods in a pooled sample from five independent rsEEG datasets of young and old adults. METHODS: RsEEG signals (n = 374) were automatically preprocessed. Oscillatory and aperiodic rsEEG features were extracted in sensor space. Features were harmonized using neuroCombat (standard ComBat used in neuroimaging), neuroHarmonize (variant with nonlinear adjustment of covariates), OPNested-GMM (variant based on Gaussian Mixture Models to fit bimodal feature distributions), and HarmonizR (variant based on resampling to handle missing feature values). Relationships between rsEEG features and age were explored before and after harmonizing batch effects. RESULTS: Batch effects were identified in rsEEG features. All ComBat methods reduced batch effects and features' dispersion; HarmonizR and OPNested-GMM ComBat achieved the greatest performance. Harmonized Beta power, individual Alpha peak frequency, Aperiodic exponent, and offset in posterior electrodes showed significant relations with age. All ComBat models maintained the direction of observed relationships while increasing the effect size. CONCLUSIONS: ComBat models, particularly HarmonizeR and OPNested-GMM ComBat, effectively control for batch effects in rsEEG spectral features. SIGNIFICANCE: This workflow can be used in multisite studies to harmonize batch effects in sensor-space rsEEG spectral features while preserving biological associations.

Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan.

BACKGROUND AND OBJECTIVE: Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived connectivity possesses the potential to differentiate brain functions between mindfulness naïve and mindfulness experienced, where such quantitative differentiation could benefit telediagnosis for mental health. However, there is no prior guidance in model selection targeting on the mindfulness-experience prediction. Here we hypothesized that the EEG effective connectivity could reach a good prediction performance in mindfulness experiences with brain interpretability. METHODS: We aimed at probing direct Directed Transfer Function (dDTF) to classify the participants' history of mindfulness-based stress reduction (MBSR), and aimed at optimizing the prediction accuracy by comparing multiple machine learning (ML) algorithms. Targeting the gamma-band effective connectivity, we evaluated the EEG-based prediction of the mindfulness experiences across 7 machine learning (ML) algorithms and 3 sessions (i.e., resting, focus-breathing, and body-scan). RESULTS: The support vector machine and naïve Bayes classifiers exhibited significant accuracies above the chance level across all three sessions, and the decision tree algorithm reached the highest prediction accuracy of 91.7 % with the resting state, compared to the classification accuracies with the other two mindful states. We further conducted the analysis on essential EEG channels to preserve the classification accuracy, revealing that preserving just four channels (F7, F8, T7, and P7) out of 19 yielded the accuracy of 83.3 %. Delving into the contribution of connectivity features, specific connectivity features predominantly located in the frontal lobe contributed more to classifier construction, which aligned well with the existing mindfulness literature. CONCLUSION: In the present study, we initiated a milestone of developing an EEG-based classifier to detect a person's mindfulness experience objectively. The prediction accuracy of the decision tree was optimal to differentiate the mindfulness experiences using the local resting-state EEG data. The suggested algorithm and key channels on the mindfulness-experience prediction may provide guidance for predicting mindfulness experiences using the EEG-based classification embedded in future wearable neurofeedback systems or plausible digital therapeutics.

Altered cortical functional networks in Wilson's disease: A resting-state electroencephalogram study.

The neuropsychiatric symptoms are common in Wilson's disease (WD) patients. However, it remains unclear about the associated functional brain networks. In this study, source localization-based functional connectivity analysis of close-eye resting-state electroencephalography (EEG) were implemented to assess the characteristics of functional networks in 17 WD patients with neurological involvements and 17 healthy controls (HCs). The weighted phase-lag index (wPLI) was subsequently calculated in source space across five different frequency bands and the resulting connectivity matrix was transformed into a weighted graph whose structure was measured by five graphical analysis indicators, which were finally correlated with clinical scores. Compared to HCs, WD patients revealed disconnected sub-networks in delta, theta and alpha bands. Moreover, WD patients exhibited significantly reduced global clustering coefficients and small-worldness in all five frequency bands. In WD group, the severity of neurological symptoms and structural brain abnormalities were significantly correlated with disrupted functional networks. In conclusion, our study demonstrated that functional network deficits in WD can reflect the severity of their neurological symptoms and structural brain abnormalities. Resting-state EEG may be used as a marker of brain injury in WD.

"Infantgram?" recruitment of infants to a clinical sleep study via social media.

Study Objectives: This study aimed to outline the strategy and outcomes of a study team in recruiting participants for an infant sleep study via social media during the COVID-19 pandemic, to assess the feasibility of recruitment via social media, and to quantitatively and qualitatively explore parental satisfaction and perceptions of recruitment via social media. Methods: The assessing sleep in infants with early-onset atopic dermatitis by longitudinal evaluation (SPINDLE) study recruited infants with and without atopic dermatitis for a longitudinal study assessing sleep. Infants were recruited via social media and their parents were interviewed to explore their experience of recruitment via social media. Results: In total, 57 controls and 33 cases were recruited. Of the 45 controls recruited via social media, 43 (95.6%) were recruited via Instagram and 2 (4.4%) were recruited via Twitter. Of the seven cases recruited via social media, 6 (85.7%) were recruited via Facebook (via sharing of Instagram posts by third parties on Facebook) and 1 (14.3%) was recruited via Instagram. All (100%, n = 28) mothers recruited via social media who completed the full study were satisfied with this approach to recruitment. Specific reasons why mothers reported engaging following exposure to the social media posts included the benefit of additional health checks for their baby, the benefit to scientific advancement, and the opportunity for a stimulating outing following the COVID-19 lockdowns. Conclusions: Our experience highlights parents' acceptance of recruitment via social media, the optimization of time and financial resources, and the benefit of using internet-based recruitment during a pandemic.

Neural correlates of trait anxiety in sensory processing and distractor filtering.

Evidence suggests that trait anxiety relates to cognitive processing and behavior. However, the relationships between trait anxiety and sensory processing, goal-directed performance and sensorimotor function are unclear, particularly in a multimodal context. This study used electroencephalography to evaluate whether trait anxiety influenced visual and tactile event-related potentials (ERPs), as well as behavioral distractor cost, in a bimodal sensorimotor task. Twenty-nine healthy young adults completed the State-Trait Anxiety Inventory. Participants were directed to focus on either tactile or visual stimuli while disregarding the other modality, responding to target stimulus amplitude with a proportional grip. Previous research suggests that somatosensory N70 and visual P2 ERPs serve as markers of attentional relevance, with attention also impacting the visual P3 ERP. It was hypothesized that trait anxiety would modulate the ERPs susceptible to attentional modulation (tactile N70, visual P2 and P3) and not affect behavioral performance. Trait anxiety showed a large, significant interaction with attention for visual P3 latency in response to unimodal visual stimuli, with a positive relationship between P3 latencies and trait anxiety when attending toward the stimulus and negative when attending away. A large, positive main effect of trait anxiety on visual N1 amplitude for bimodal stimuli was also detected. As predicted, trait anxiety related to ERPs but not behavioral distractor cost. These findings suggest that trait anxiety modulates visual but not somatosensory processing correlates based on attention. The absence of overt behavioral performance effects suggests compensatory mechanisms may offset underlying differences in sensory processing.

The value of electroencephalography features in the prognostic evaluation of large hemispheric infarction patients at different time intervals.

BACKGROUND: Large Hemispheric Infarction (LHI) is a devastating disease with high mortality. This study aimed to use electroencephalography (EEG) to evaluate the death risk of LHI patients and identify suitable evaluation time. METHODS: This study retrospectively collected clinical and EEG data from 73 LHI patients, dividing them into death and survival group at discharge. EEG data was classified as 1-5 days and 6-14 days after onset according to the time intervals of cerebral edema. Regression and receiver operator characteristic curve (ROC) analysis were applied to explore the impact of temporal changes in various EEG and clinical features on death. RESULTS: The areas under ROC curve (AUC) of death prediction for non-α frequency on non-infarct side at 6-14 days after onset was significantly higher than that at 1-5 days (p = 0.004). And there was no significant difference between the AUC of seizure activity for death prediction at 1-5 days and 6-14 days (p = 0.418). Multivariate regression analysis revealed that non-α frequency on non-infarct side and seizure activity at 6-14 days after onset were the independent risk factors for the death of LHI patients. Additionally, above two EEG features significantly improved the death predictive efficacy of clinical features in LHI patients with the integrated discrimination improvement index (IDI) of 0.174 (p = 0.015) and the net reclassification improvement (NRI) of 1.314 (p<0.001). CONCLUSIONS: Non-α frequency on non-infarct side and seizure activity were reliable indicators for death prediction. 6-14 days after onset was the better time window for death evaluation of LHI patients through EEG.

A sustainable artificial-intelligence-augmented digital care pathway for epilepsy: Automating seizure tracking based on electroencephalogram data using artificial intelligence.

Objective: Scalp electroencephalograms (EEGs) are critical for neurological evaluations, particularly in epilepsy, yet they demand specialized expertise that is often lacking in many regions. Artificial intelligence (AI) offers potential solutions to this gap. While existing AI models address certain aspects of EEG analysis, a fully automated system for routine EEG interpretation is required for effective epilepsy management and healthcare professionals' decision-making. This study aims to develop an AI-augmented model for automating EEG seizure tracking, thereby supporting a sustainable digital care pathway for epilepsy (DCPE). The goal is to improve patient monitoring, facilitate collaborative decision-making, ensure timely medication adherence, and promote patient compliance. Method: The study proposes an AI-augmented framework using machine learning, focusing on quantitative analysis of EEG data to automate DCPE. A focus group discussion was conducted with healthcare professionals to find the problem of the current digital care pathway and assess the feasibility, usability, and sustainability of the AI-augmented system in the digital care pathway. Results: The study found that a combination of random forest with principal component analysis and support vector machines with KBest feature selection achieved high accuracy rates of 96.52% and 95.28%, respectively. Additionally, the convolutional neural networks model outperformed other deep learning algorithms with an accuracy of 97.65%. The focus group discussion revealed that automating the diagnostic process in digital care pathway could reduce the time needed to diagnose epilepsy. However, the sustainability of the AI-integrated framework depends on factors such as technological infrastructure, skilled personnel, training programs, patient digital literacy, financial resources, and regulatory compliance. Conclusion: The proposed AI-augmented system could enhance epilepsy management by optimizing seizure tracking accuracy, improving monitoring and timely interventions, facilitating collaborative decision-making, and promoting patient-centered care, thereby making the digital care pathway more sustainable.

Ictal and interictal epileptic networks of 34 patients with Hypothalamic Hamartoma on scalp electroencephalography.

OBJECTIVE: To investigate ictal and interictal cortical involvement in epilepsy associated with hypothalamic hamartoma. We conducted a retrospective study of 34 patients with epilepsy and hypothalamic hamartoma, using data from long-term video-EEG-monitoring. METHODS: We analyzed onset and propagation of ictal and interictal scalp EEG and visualized the resulting networks of cortical involvement. According to clinical and EEG criteria we grouped patients in: (1) focal disease, (2) focal advanced disease, (3) extended disease. We compared networks between these groups and different seizure types. Eight patients underwent several video-EEGs, and we analyzed all to investigate epilepsy progression. RESULTS: Epileptic activity mainly involved frontal and temporal cortex regions. Involvement of frontal regions was more common in advanced stages of the disease, and strong fronto-temporal connections were observed in the ictal networks of patients in intermediate stages (25.0% (left) and 35.7% (right) of seizures with EEG correlate). Occurrence and timing of EEG-correlate significantly depended on the seizure type (Chi-2-test, p<<0.001). In patients with several EEGs, seizure activity increased by +0.67 seizures/day/month (mean). There were significant differences between patients with normal and impaired cognitive function, with the latter showing pronounced ictal involvement of fronto-temporal cortex areas (p<0.001). CONCLUSION: Overall, in epilepsy due to hypothalamic hamartoma, cortical involvement focuses on frontal and temporal regions and varies systematically with the stage of the disease, different seizure types and presence of impaired cognitive function. We propose that these data may help improve our general understanding of epileptogenesis and potentially provide insights for the surgical therapy of hypothalamic hamartomas.

Significance of EEG-electrode combinations while calculating filters with common spatial patterns.

Objective: Common spatial pattern (CSP) is a common filter technique used for pre-processing of electroencephalography (EEG) signals for imaginary movement classification tasks. It is crucial to reduce the amount of features especially in cases where few data is available. Therefore, different approaches to reduce the amount of electrodes used for CSP calculation are tried in this research. Methods: Freely available EEG datasets are used for the evaluation. To evaluate the approaches a simple classification pipeline consisting mainly of the CSP calculation and linear discriminant analysis for classification is used. A baseline over all electrodes is calculated and compared against the results of the approaches. Results: The most promising approach is to use the ability of CSP to provide information about the origin of the created filter. An algorithm that extracts the important electrodes from the CSP utilizing these information is proposed.The results show that using subject specific electrode positions has a positive impact on accuracy for the classification task. Further, it is shown that good performing electrode combinations in one session are not necessarily good performing electrodes in another session of the same subject. In addition to the combinations calculated using the developed algorithm, 26 additional electrode combinations are proposed. These can be taken into account when selecting well-performing electrode combinations. In this research we could achieve an accuracy improvement of over 10%. Conclusions: Carefully selecting the correct electrode combination can improve accuracy for classifying an imaginary movement task.

Maintained volitional activation of the muscle alters the cortical processing of proprioceptive afference from the ankle joint.

Cortical proprioceptive processing of intermittent, passive movements can be assessed by extracting evoked and induced electroencephalographic (EEG) responses to somatosensory stimuli. Although the existent prior research on somatosensory stimulations, it remains unknown to what extent ongoing volitional muscle activation modulates the proprioceptive cortical processing of passive ankle-joint rotations. Twenty-five healthy volunteers (28.8 ± 7 yr, 14 males) underwent a total of 100 right ankle-joint passive rotations (4° dorsiflexions, 4 ± 0.25 s inter-stimulus interval, 30°/s peak angular velocity) evoked by a movement actuator during passive condition with relaxed ankle and active condition with a constant plantarflexion torque of 5 ± 2.5 Nm. Simultaneously, EEG, electromyographic (EMG) and kinematic signals were collected. Spatiotemporal features of evoked and induced EEG responses to the stimuli were extracted to estimate the modulation of the cortical proprioceptive processing between the active and passive conditions. Proprioceptive stimuli during the active condition elicited robustly ∼26 % larger evoked response and ∼38 % larger beta suppression amplitudes, but ∼42 % weaker beta rebound amplitude over the primary sensorimotor cortex than the passive condition, with no differences in terms of response latencies. These findings indicate that the active volitional motor task during naturalistic proprioceptive stimulation of the ankle joint enhances related cortical activation and reduces related cortical inhibition with respect to the passive condition. Possible factors explaining these results include mechanisms occurring at several levels of the proprioceptive processing from the peripheral muscle (i.e. mechanical, muscle spindle status, etc.) to the different central (i.e. spinal, sub-cortical and cortical) levels.