Seizures

EEG microstate-derived dynamic network biomarkers for lateralization and structural etiology in temporal lobe epilepsy.

OBJECTIVE: Temporal lobe epilepsy (TLE) is the most common focal epilepsy but remains highly heterogeneous across hemispheric and structural etiology. This study aimed to characterize microstate-based network dynamics in TLE and evaluate their diagnostic value for seizure lateralization and structural etiology using machine learning. METHODS: Resting-state electroencephalography (EEG) recordings f...

Development and validation of interpretable multimodal clinical-radiomics models for predicting epileptogenic foci and surgical outcomes in tuberous sclerosis complex: A multicenter study.

Precise localization and resection of epileptogenic (epi) foci from multiple cortical foci determine surgical outcomes in the tuberous sclerosis complex (TSC). Although the use of intracranial electroencephalography (EEG) for detecting epileptic discharges remains the gold standard for identifying epi foci, its invasiveness and cost limit clinical application. We aimed to develop and validate a no...

How much EEG is needed for deep learning with convolutional neural networks? Predicting the benefit from additional data.

Objective.This study quantifies how the accuracy of convolutional neural networks for electroencephalogram (EEG) classification depends on the amount ...

Multi-scale kernel and electrode attention network for EEG-based epileptic seizure detection.

Epilepsy is a common chronic neurological disorder, and automated detection of epileptic seizures using multi-channel electroencephalography (EEG) is ...

Identification and validation of key genes as diagnostic biomarkers for epilepsy using bioinformatics and machine learning.

Epilepsy is a common neurological disorder with a complex molecular pathogenesis. Approximately one-third of patients experience drug-resistant seizur...

Graph empirical mode decomposition and multiscale feature extraction for EEG-based classification of Alzheimer's disease and frontotemporal dementia.

BACKGROUND AND OBJECTIVE: Early and correct classification of neurodegenerative diseases like Alzheimer's Disease (AD) and Frontotemporal Dementia (FT...

Contextual anatomy-guided deep learning for accurate fovea segmentation in diabetic retinopathy fundus images.

Accurate fovea segmentation in fundus images is a critical step in diabetic retinopathy screening; however, it remains a challenging task due to the i...

Reliable detection of focal onset impaired awareness seizures in patients with epilepsy using wearable ECG: Development and validation study.

BACKGROUND: Underreporting of seizures, particularly focal onset impaired awareness seizures (FIAS), compromises the effectiveness of patient care and...

Poincaré feature-based classification of electroencephalography signals for multiple sclerosis diagnosis.

BACKGROUND: As one of the most widespread neurodegenerative disorders, Multiple Sclerosis (MS) is a progressive neuroinflammatory disorder affecting m...

Parietal repetitive transcranial magnetic stimulation enhances functional connectivity in patients with minimally conscious state.

The basis of disorders of consciousness is the destruction of brain functional connectivity, and the restoration of damaged connectivity is considered...

Differentiating bipolar disorder and schizophrenia using sleep EEG power and coherence features: A machine learning approach based on polysomnography.

Differentiating between bipolar disorder (BD) and schizophrenia (SZ) is challenging due to overlapping clinical symptoms and shared genetic risks, res...

AI in epilepsy neuroimaging.

PURPOSE OF REVIEW: Recent advances in the capabilities and usability of artificial intelligence (AI) architectures coupled with increased availability...

A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting.

Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by...

Achieving more human brain-like vision via human EEG representational alignment.

Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains...

Multi-branch convolutional neural network and intracranial EEG high-frequency oscillations predict post-surgical seizure outcomes.

OBJECTIVE: Pathological High-Frequency Oscillations (HFOs) identify epileptogenic cortex, but their surgical utility is unproven. Current epilepsy sur...

Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy.

BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiven...

Motor imagery EEG signal classification using minimally random convolutional kernel transform and hybrid deep learning.

The brain-computer interface (BCI) establishes a non-muscle channel that enables direct communication between the human body and an external device. E...

Bimodal EEG-fNIRS and Deep Learning for Classifying Intensity-Dependent Cortical Auditory Evoked Responses.

Detection of intensity-dependent cortical auditory evoked responses using electroencephalography (EEG) is essential in clinical audiology and research...

EEG and EMG dataset for analyzing movement-related cortical potentials in hand gesture tasks.

This dataset contains electroencephalography (EEG) and electromyography (EMG) recordings acquired during the execution of specific motor tasks aimed a...

Structural brain imaging biomarkers for predicting seizure recurrence after a first unprovoked seizure.

OBJECTIVES: Predicting seizure recurrence following a first unprovoked seizure (FUS) remains a significant clinical challenge, especially when routine...