Seizures

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest.

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) supports prognostication; however, interpretation relies on expert judgment and is often subjective and delayed. We developed DeepCRI, a bedside-integrated deep learning system that produces continuously updated prognostic trajectories during the first 36...

The Insomnia EEG Score: a new tool for the classification of people with poor sleep.

Quantitative features could help objectively identify and grade insomnia severity, though there is currently no pathophysiological biomarker of insomnia. In this study, we used the largest cohort of individuals with and without insomnia to date to train a model capable of distinguishing people with insomnia from those without insomnia. We identified 720 spectral, 606 spindle, and 16 macro-architec...

Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography.

STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is wide...

Enhanced cortical connectivity during passive robotic-assisted gait training in individuals with spinal cord injury: An EEG study.

BACKGROUND: Robotic-assisted gait training (RAGT) has emerged as a promising strategy to promote neuroplasticity and motor recovery in individuals wit...

Lower pre-treatment TMS-evoked cortical reactivity and alpha-band oscillatory dynamics predict efficacy of primary motor cortex neuromodulation for chronic pain.

Repetitive transcranial magnetic stimulation (rTMS) to the primary motor cortex (M1) provides significant pain relief in ∼45% of chronic pain patients...

Bidirectional Mamba-2 Boosts EEG Super-Resolution via Regression and Diffusion.

MOTIVATIONS: Electroencephalography (EEG) is a non-invasive method that records brain electrical activity from scalp electrodes, offering millisecond ...

A novel machine learning approach for prediction of postoperative delirium using multi-domain features of high-density EEG.

Postoperative delirium (POD) poses a significant risk to patients, and accurate prediction of postoperative delirium can provide guidance for pos...

Large-scale brain network alteration among OCD patients with suicidal thoughts and behavior: A microstate analysis of the electroencephalogram.

BACKGROUND: The prevalence of suicidality in obsessive-compulsive disorder (OCD) is understudied. Moreover, identifying neurobiological markers of sui...

E2T: EEG-to-Trajectory Transformer for Motor Imagery-based Fully-DoF Motion Prediction.

Brain-computer interfaces (BCIs) using electroen-cephalography (EEG) enable non-invasive, real-time interaction for individuals with motor impairments...

MultiMindNet: AI-based mental health analysis using hybrid deep learning approach and Hybrid Ant-Grey Wolf Optimization (HAGWO) algorithm.

Mental health disorders like depression and anxiety pose global challenges, requiring accurate, non-invasive detection methods. Classical modes of dia...

Wheelchair movement signal classification from EEG for motor-impaired individuals using novel deep learning architecture.

Purpose: Traditional wheelchair controls often limit independence and pose safety risks for motor-impaired users. To address these challenges, this st...

Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.

INTRODUCTION: To evaluate the progress of artificial intelligence (AI)-based tools in interpreting clinical data, to compare the existing models, to i...

A Few-Layer Multilayer Perceptron is Worth Attention for EEG Classification in Rapid Serial Visual Presentation Task.

Rapid serial visual presentation (RSVP) enables efficient electroencephalography (EEG)-based brain-computer interfaces, yet single-trial decoding rema...

Severity-dependent alterations of EEG microstate dynamics in obsessive-compulsive disorder.

BACKGROUND: While elevated symptom severity in obsessive-compulsive disorder (OCD) is associated with a profound clinical burden and escalating psychi...

Consciousness Monitoring and Outcome Prediction Using EEG Connectivity in Severe Traumatic Brain Injury.

Neurological prognostication of patients in post-traumatic coma remains challenging due to the paucity of reliable markers in the acute phase. We aime...

Patient-specific instantaneous spatial temperature maps for MR-guided laser interstitial thermal therapy using a physics-assisted deep learning framework.

PURPOSE: Accurate prediction of the laser energy absorption and corresponding thermal spread is essential for safe and effective outcomes in magnetic ...

PhysioMotion Artifact: A task-driven EEG dataset with point-wise motion artifact annotations.

Physiological artifacts pose persistent challenges in electroencephalogram (EEG) data acquisition, often compromising interpretation and post-analysis...

A multicenter, video-EEG-based validation of a multimodal wearable device for focal seizure detection in adults: The SeizeIT2 study.

OBJECTIVE: Currently available wearable devices for detecting focal seizures primarily target major motor seizures or involve semi-invasive subscalp i...

FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep State Classification.

OBJECTIVE: Fetal sleep state classification is essential for identifying neurodevelopmental complications like hypoxia, but manual annotation is subje...

Diagnostic performance of AI-based EEG interpretation versus human clinical experts for epilepsy detection: systematic review and meta-analysis.

BACKGROUND: Electroencephalography (EEG) interpretation for epilepsy diagnosis faces persistent challenges including specialist shortages, variable in...