Seizures

EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

In real-world occupational settings, mental fatigue commonly emerges from the combination of sleep deprivation with prolonged cognitive and physical workload. However, this multidimensional fatigue profile is rarely captured in controlled experimental paradigms that examine brain activation and fatigue-related responses. Consequently, the validity and transferability of cognitive fatigue biomarker...

Efficient epileptic seizure prediction using single channel EEG signal and knowledge distillation on deep neural networks.

Epilepsy is a common neurological disease, and in some patients, abnormal changes in brain activity typically begin before the onset of a seizure. Electroencephalography (EEG) is a practical method for recording electrical activity of brain and plays a crucial role in the diagnosis of epilepsy. Previous studies relied on multi-channel EEG signals and large deep neural networks, which require power...

Aggregating XAI-based explanations to identify spectral-spatial patterns in CNN-based resting-state EEG classification.

Convolutional neural networks (CNNs) achieve high performance in electroencephalographic (EEG) classification tasks; however, their decision-making me...

Decoding motor imagery related to major mimetic muscles from electroencephalography.

BACKGROUND: Functional and aesthetic deficits in individuals with facial nerve paralysis (FNP) significantly impair their quality of life. By decoding...

SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.

Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of...

Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study.

BACKGROUND: Artificial intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in health care. Despite growing resea...

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

Accurate and adaptive time-frequency representation is essential for analyzing nonstationary signals in critical applications, such as epileptic seizu...

Aggression in epilepsy and sleep: from historical accounts to brain networks and human behaviour.

For a long time, epilepsy has been associated with violent behaviour, acquiring a highly stigmatising reputation, shaped mainly by 19th-century medica...

Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks.

OBJECTIVE: Accurate and reliable neural decoding of locomotion holds promise for advancing clinical applications such as rehabilitation and prosthetic...

Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection.

Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on...

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms.

Interictal epileptiform discharges (IEDs) are essential for epilepsy diagnosis, yet visual electroencephalogram (EEG) analysis remains subjective and ...

Cross-Level Topological Framework: Learning Explainable Region-Channel Representations from EEG Signals for Emotional Decoding.

Constructing functional connectivity networks from electroencephalogram (EEG) channels and using graph neural networks for emotion recognition have em...

Diagnostic yield of electroencephalographyin the emergency department: protocol for the EMINENCE-M multicentre retrospective observational study.

INTRODUCTION: Emergency EEG (emEEG) is increasingly used in the emergency department (ED), but its diagnostic yield remains uncertain. This protocol d...

Evaluating multi-level membership inference risk in federated EEG learning.

Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communi...

Precision neuroprotection in cardiac surgery: a structured narrative review of multimodal, data-driven perioperative care.

Neurological injury remains a major contributor to morbidity, mortality, and long-term cognitive decline in patients undergoing cardiac surgery, despi...

Diagnosis of ADHD in children from EEG signals using amplitude modulation features.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and accurate diagnosis of this disorde...

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

Epilepsy manifests as a chronic neurological condition marked by recurrent seizures. Recent advances in computational analysis of Electroencephalograp...

Health-Related Quality of Life and Family Impact Among Children with Epilepsy: The Role of Seizure Control.

INTRODUCTION: Seizure control is the primary therapeutic goal in pediatric epilepsy, yet its multidimensional impact on health-related quality of life...

The AI dispatcher copilot: beyond cardiac arrest recognition to dynamic large language model-assisted Tele-CPR.

AIM: Out-of-hospital cardiac arrest (OHCA) remains a leading cause of death. Although emergency medical dispatchers represent the first link in the Ch...

Decoding concealed information using multimodal neurophysiological signals.

Detecting concealed information is a critical challenge in forensic investigations, security screening, and cognitive neuroscience. Conventional appro...