Seizures

Validation of EEG mental workload markers using integrated statistical and machine learning analyses.

AIM: Attentional and working-memory processes can be monitored noninvasively using electroencephalography (EEG), which provides physiological indices of mental workload. Prior studies consistently report increased frontal-midline theta and beta power together with suppression of posterior alpha activity during cognitively demanding tasks. However, most investigations rely either on group-level sta...

Enhanced detection of subtle cortical abnormalities in focal epilepsy using 7 T MRI surface-based models and graph neural networks.

PURPOSE: MRI detection of subtle focal cortical dysplasia (FCD)-like abnormalities remains challenging in focal epilepsy. Higher signal-to-noise ratio and spatial resolution offered by ultra-high-field 7T MRI and surface-based graph-neural-network (GNN) analysis may improve detection of subtle cortical abnormalities. We evaluated whether combining 7T MRI with a surface-based GNN classifier improve...

Surface EEG to Identify Cognitive Motor Dissociation After Acute Brain Injury.

PURPOSE: Cognitive motor dissociation (CMD) is associated with long-term recovery in acute brain injury, but CMD testing is only available in few cent...

Disruption of the claustrum-ACC pathway contributes to human mind blanking.

The claustrum is a highly connected structure hypothesized to orchestrate conscious experience, yet its role in humans remains enigmatic. To address t...

An edge-AI enabled wearable platform for real-time epileptic seizure detection with geolocated alerting.

Epilepsy remains a major global health concern, particularly in regions where continuous medical monitoring is difficult to implement. This study intr...

Enhancing decision-making in surgery for a large temporocorneal meningioma through an explainable human-AI collaboration: a case report.

BACKGROUND: Meningiomas, particularly large temporocorneal meningiomas, pose significant surgical challenges due to their proximity to critical brain ...

Development of a human-artificial intelligence collaboration-based storybook series for understanding epilepsy and supporting self-management.

Epilepsy is a chronic condition that requires ongoing self-management, including medication adherence, trigger control, lifestyle regulation, and psyc...

CausalTCC: causal temporal contrastive learning for automated Alzheimer's disease biomarker discovery with bio-electrical signals.

OBJECTIVE: Learning robust representations from scarce labeled bio-electrical time-series data remains a critical challenge in clinical diagnosis. Whi...

Cognitive and brain function enhancement in Gen X group after personalized, AI supervised EEG-neurofeedback training.

Interventions supporting medical care and enhancing quality of life in neurodegenerative or age-related cognitive decline are strongly needed. El...

Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization.

OBJECTIVE: Electroencephalography (EEG) source localization is an ill-posed inverse problem in which conventional methods often rely on static anatomi...

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

Intravoxel incoherent motion (IVIM) is a diffusion-weighted magnetic resonance imaging (MRI) method that models slow (D, tissue diffusivity) and fast ...

An EEG-based hybrid machine learning approach for CT scan triage in mild traumatic brain injury.

Mild traumatic brain injury (mTBI) frequently prompts computed tomography (CT) imaging in emergency departments, despite a high proportion of negative...

Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable sub-band topography.

Parkinson's disease (PD) diagnosis remains challenging because subtle neural alterations may be difficult to capture using conventional clinical asses...

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

The human brain maintains functional stability under changing conditions through interacting processes that include synaptic plasticity, homeostatic r...

A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals.

This paper introduces TESSCCo (TV-control EEG-based Silent Speech Command Corpus), a new dataset including electroencephalography (EEG) signals during...

Assessing the Robustness of Deep Learning Based Brain Age Prediction Models Across Multiple EEG Datasets.

The increasing availability of large electroencephalography (EEG) datasets enhances the potential clinical utility of deep learning (DL) for cognitive...

Cloud EEG Privacy Using Red-Billed Blue Magpie Optimized Physics-Penalized Dual-Branch Spectral-Spatial Neural Network for Epileptic Seizure Prediction.

Epileptic seizure prediction is a critical research area that enables timely intervention and prevention of severe neurological complications. With th...

An Enhanced Machine Learning-Based Multimodal Framework for Seizure Detection Using EEG and MRI Data.

One of the most common neurological disorders that immediately alters a person's way of life is an epileptic seizure. Accurate seizure detection remai...

An Effective LRSF-DLNN-Based Autism Spectrum Disorder Prediction Using EEG and fMRI.

In today's society, autism spectrum disorder (ASD) is a common neurological disorder that affects a person's behavior and communication. Hence, an ear...

Predicting Pain: Electroencephalography Signatures of Neural Integration During Experimental Tonic Thermal Pain.

BACKGROUND: The identification of reliable neural signatures for pain remains a critical challenge in both clinical and experimental settings. While e...