Seizures

Neurophysiological EEG Characterization of Autism Spectrum Disorder Using DWT-Based Frequency Analysis With Selective Electrodes and Brain Segmentation: An Explainable AI-Driven Approach.

Autism Spectrum Disorder (ASD) diagnosis benefits from the technical analysis of neural oscillations. The objective identification of Autism Spectrum Disorder (ASD) is advanced through a dedicated engineering framework that analyzes neural oscillations via electroencephalogram (EEG) signals. This methodology synthesizes statistical signal processing, frequency-domain transformation, and computatio...

Artificial Intelligence in Neonatal Care: The Breadth of Promise, the Depth of Challenge-An Overview.

Artificial intelligence (AI) is becoming an integral tool in clinical care. The recent position statement by the Royal Australasian College of Physicians (RACP) provides a timely practical blueprint on implementing and monitoring the use of AI in clinical practice. Although the data-rich environment of NICU presents a good setting for evaluation of AI-assisted clinical application, research on AI ...

Predicting comorbid anxiety in adolescents with major depressive disorder: an EEG-based machine learning approach with SHAP interpretability.

Comorbid anxiety in adolescents with major depressive disorder (adMDD) is linked to higher suicide risk and poorer prognosis, necessitating precise sc...

Classification of neurodevelopmental disorders and typical development using deep learning and a portable patch-type electroencephalography device.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) in children and ofte...

Deep-learning based electroencephalogram denoising: A literature review.

Electroencephalography (EEG) is a pivotal tool for exploring brain functions. However, the low amplitude of EEG signals renders them inherently suscep...

Multimodal quantification of cognitive load using a printed wearable facial bio-potential system.

Cognitive load refers to the amount of mental effort required to process information and perform tasks. It has a strong impact on both learning and ta...

Frequency-decomposed EEG microstate analysis reveals altered cross-frequency coordination in adolescent major depressive disorder.

Adolescent major depressive disorder (MDD) involves alterations in large‑scale brain network dynamics. However, conventional EEG microstate studies ty...

Multilayer Validation Reveals a Glia-Associated Secretome Signature in Temporal Lobe Epilepsy.

Although multi-omics studies have increasingly revealed molecular alterations associated with epilepsy, clinically accessible cerebrospinal fluid (CSF...

Prefrontal Connectivity Alterations and Oscillatory Dynamics in Cannabis Use Disorder.

Long-term cannabis use can result in the development of cannabis use disorder (CUD) and dependence via the endocannabinoid system pathway. A brain net...

Forecasting Excessive Anesthesia Depth Using EEG α-Spindle Dynamics and Machine Learning.

Accurately predicting transitions to anesthetic drugs overdosage is a critical challenge in general anesthesia as it requires the identification of EE...

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 ...

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...

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...