Seizures

Alzheimer’s Disease Stage Classification via Multimodal CNN on EEG Spectrograms and Cube-Drawing Images

Clinicians currently lack reliable tools to determine, at the point of mild cognitive impairment (MCI), which individuals will progress to Alzheimer’s disease (progressive MCI, PMCI) versus remain stable (SMCI). Early, patient-specific prognosis is therefore difficult using routine clinical evaluation alone. We propose a dual-branch Convolutional Neural Network (CNN) that fuses two low-cost bedsid...

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) targeting the primary motor cortex (M1) provides significant pain relief in approximately 45% of patients with chronic pain. Identifying markers that predict rTMS treatment responders to M1 before initiating treatment is crucial for informing decision-making and improving patient outcomes in clinical practice. In this secondary analysis of a clin...

Towards Automated Neonatal EEG Analysis: Multi-Center Validation of a Reliable Deep Learning Pipeline

To evaluate the reliability and generalization of NeoNaid, a fully automated software tool for neonatal EEG analysis, based on functional brain age (F...

Interpretable Machine Learning for Epileptic Seizure Detection on the BEED Using LIME with an Ensemble Network

This study aims to identify seizures in four different stages among epileptic patients, utilizing the Bangalore Epilepsy Dataset (BEED). This dataset,...

The Seizure Embedding Map: A Spatio-Temporal Transformer for Comparing Patients by Ictal Intracranial EEG Features at Scale

Planning invasive treatment for medication-resistant epilepsy relies on qualitatively interpreting seizure recordings from intracranial EEG (iEEG) rec...

Machine Learning Analysis of Routine EEG Accurately Predicts Anti-Seizure Medication Response

Despite the availability of more than 20 anti-seizure medications (ASMs), approximately half of patients with newly diagnosed epilepsy fail their firs...

Aging Detection Based on Dynamic State Transitions in Instantaneous Hilbert-Based Spatio-Temporal EEG Features

The spatial distribution of electroencephalography (EEG) oscillatory power and its temporal transitions are widely recognized as indicators of cogniti...

Mobility Function and Aperiodic Electrocortical Activity in Younger and Older Adults

Mobility declines with age to the extent that walking speed is often considered a vital sign. Identifying neurological mechanisms behind this decline ...

Multi-branch convolutional neural network using intracranial EEG high frequency oscillation features for predicting post-surgical seizure outcomes

Pathological high-frequency oscillations (HFOs 80-600 Hz) in intracranial EEG distinguish epileptogenic cortex. However, it is uncertain whether utili...

EEG-Based Prediction of rTMS Treatment Response in Depression: Nonlinear Features and Machine Learning with Minimal Electrode

Repetitive transcranial magnetic stimulation (rTMS) is an established intervention for treatment-resistant depression, but response rates remain highl...

Topological Entropy and Homology Reveal Interpretable and Real-Time Neural Signatures in Pediatric EEG

Decoding neural states from pediatric EEG in naturalistic settings remains challenging due to signal noise, motion artifacts, and intersubject variabi...

Quantitative EEG-Based Deep Learning for Neonatal Seizure Detection using Conv-LSTM

Neonatal seizures cause significant morbidity and mortality, both acutely and in the long term, contributing to adverse neurodevelopmental outcomes. T...

Bedtime Brain State Predicts the Impact of Closed-Loop Auditory Stimulation on Sleep and Cognition

Sleep interventions targeting slow-wave activity (SWA) show heterogeneous effects across individuals. We investigated whether pre-sleep brain states p...

Sleep Staging Foundation Models Encode Neural Disorder-Related EEG Representations that Generalize to Wakefulness

To leverage sleep foundation models trained on large datasets of polysomnography for neurological disorder detection during an awake state. Three publ...

Research on Epilepsy Detection and Recognition Based on the Combination of Time Frequency Transform and Deep Learning Model

To improve the detection performance of epileptic electroencephalogram (EEG) signals and address their non-stationary characteristics, this paper comp...

Explainable machine learning on weighted connectivity networks across frequencies for outcome prediction in comatose patients

Accurate early prediction of neurological outcomes in comatose patients after cardiac arrest is critical for guiding therapeutic decisions and improvi...

Sleep-Derived Features From Multi-Night In-ear EEG Identify Patterns Linked To Mild Cognitive Impairment

We investigated whether sleep features from multi-night, at-home in-ear EEG could distinguish mild cognitive impairment (MCI) from cognitively normal ...

Resting-state EEG and machine learning to investigate cortical connectivity as a biomarker in chronic mTBI

Mild traumatic brain injury (mTBI) is a heterogeneous condition with long-term sequelae, yet diagnosis in the chronic stage remains limited by relianc...

Bridging Brain Signals and Self-Reported Symptoms: An AI-Driven, High-Sensitivity Model for Detecting Suicidality in Major Depressive Disorder

Major depressive disorder (MDD) with suicidality represents a significant public health concern, as suicide ranks among the leading causes of death wo...

Information Leakage and Performance Overestimation in EEG-Based Schizophrenia Detection: Evidence from Literature and Empirical Analyses

Detecting schizophrenia (SZ) from electroencephalography (EEG) signals using machine- and deep learning models gained traction lately due to potential...