Seizures

Understanding the Spatio-Temporal Coupling of Spikes and Spindles in Focal Epilepsy Through a Network-Level Computational Model.

The electrophysiological findings have shown that epileptiform spikes triggering sleep spindles within 1[Formula: see text]s across multiple channels are commonly observed during sleep in focal epilepsy (FE). Such spatio-temporal couplings of spikes and spindles (STCSSs) are defined as a kind of pathological waves, and frequent emergence of them may cause the degradation of cognitive function for ...

A Novel Fusion Framework Combining Graph Embedding Class-Based Convolutional Recurrent Attention Network with Brown Bear Optimization Algorithm for EEG-Based Parkinson's Disease Recognition.

Parkinson's disease recognition (PDR) involves identifying Parkinson's disease using clinical evaluations, imaging studies, and biomarkers, focusing on early symptoms like tremors, rigidity, and bradykinesia to facilitate timely treatment. However, due to noise, variability, and the non-stationary nature of EEG signals, distinguishing PD remains a challenge. Traditional deep learning methods strug...

Multimodal machine learning for deception detection using behavioral and physiological data.

Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom trials. Differentiating truth from lies is inheren...

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry.

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter ...

Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.

Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This resul...

Machine Learning-Based localization of the epileptogenic zone using High-Frequency oscillations from SEEG: A Real-World approach.

INTRODUCTION: Localizing the epileptogenic zone (EZ) using Stereo EEG (SEEG) is often challenging through manual analysis. Even methods based on signa...

Artificial intelligence (ChatGPT 4.0) vs. Human expertise for epileptic seizure and epilepsy diagnosis and classification in Adults: An exploratory study.

AIMS: Artificial intelligence (AI) tools like ChatGPT hold promise for enhancing diagnostic accuracy and efficiency in clinical practice. This explora...

Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.

Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause variou...

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvem...

GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals.

Low Back Pain (LBP) is the most prevalent musculoskeletal condition worldwide and a leading cause of disability, significantly affecting mobility, wor...

Predicting EEG seizures using graded spiking neural networks.

To develop and evaluate a novel, non-patient-specific epileptic seizure prediction system using graded spiking neural networks (GSNNs) implemented on ...

Deep learning models as learners for EEG-based functional brain networks.

Functional brain network (FBN) methods are commonly integrated with deep learning (DL) models for EEG analysis. Typically, an FBN is constructed to ex...

REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.

High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets...

EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brai...

Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning.

Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to th...

On-Chip Mental Stress Detection: Integrating a Wearable Behind-The-Ear EEG Device With Embedded Tiny Neural Network.

The study introduces an innovative approach to efficient mental stress detection by combining electroencephalography (EEG) analysis with on-chip neura...

Near-lossless EEG signal compression using a convolutional autoencoder: Case study for 256-channel binocular rivalry dataset.

Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to ...

Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment.

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes...

Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine.

Artificial intelligence (AI) is revolutionizing epilepsy care by advancing seizure detection, enhancing diagnostic precision, and enabling personalize...

An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term EEG Based on Deep Learning.

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of ...