Seizures

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 of training data and evaluates parametric models for extrapolating performance to larger datasets.Approach.We evaluated the classification accuracy of three neural network architectures across three EEG classification tasks, systematically varying th...

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 of great significance for improving diagnostic efficiency. Existing deep learning methods either reduce the number of EEG channels to lower computational costs or perform modality transformations to enhance feature representation. However, these appr...

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...

Discriminating between major depressive disorder and bipolar depression: Aberrant EEG microstate dynamics and machine learning classification.

BACKGROUND: Major depressive disorder (MDD) and bipolar depression (BD) are common mood disorders with overlapping clinical features, posing significa...

Prioritising follow-up for people with suspected epilepsy using a digital EEG biomarker.

Lengthy waits for follow-up testing are common for people with suspected epilepsy. This delays diagnosis, prolongs uncertainty and increases seizure r...

Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review.

INTRODUCTION: Generalized tonic - clonic seizures (GTCS) are among the most severe seizure types and a major cause of sudden unexpected death in epile...