Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Potential merits and flaws of large language models in epilepsy care: A critical review.

The current pace of development and applications of large language models (LLMs) is unprecedented an...

Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models.

INTRODUCTION: A challenge when applying an artificial intelligence (AI) deep learning (DL) approach ...

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.

The main purpose of this paper is to provide information on how to create a convolutional neural net...

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model.

This study presents a novel method to assess the learning effectiveness using Electroencephalography...

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) ...

Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.

Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been de...

Global brain analysis of minor hallucinations in Parkinson's disease using EEG and MRI data.

INTRODUCTION: Visual hallucination is a prevalent psychiatric disorder characterized by the occurren...

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI.

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from ...

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

BACKGROUND: Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with...

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization.

For depression diagnosis, traditional methods such as interviews and clinical scales have been widel...

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this c...

Robotic assessment of sensorimotor and cognitive deficits in patients with temporal lobe epilepsy.

OBJECTIVE: Individuals with temporal lobe epilepsy (TLE) frequently demonstrate impairments in execu...

EEG-BCI-based motor imagery classification using double attention convolutional network.

This article aims to improve and diversify signal processing techniques to execute a brain-computer ...

A robust deep learning detector for sleep spindles and K-complexes: towards population norms.

Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that a...

Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecti...

An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model.

This has become a significant study area in recent years because of its use in brain-machine interac...

Quercetin Exerts Anticonvulsant Effect through Mitigation of Neuroinflammatory Response in Pentylenetetrazole-induced Seizure in Mice.

Epilepsy is a chronic disease of the brain characterized by seizures. The currently available antico...

Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning.

Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in und...

EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification.

Because of the intricate topological structure and connection of the human brain, extracting deep sp...

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