Neurology

Seizures

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

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Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.

Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predi...

Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.

The analysis of cognitive patterns through brain signals offers critical insights into human cogniti...

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 with...

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

Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom ...

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 develop...

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 vi...

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 th...

Dual-pathway EEG model with channel attention for virtual reality motion sickness detection.

BACKGROUND: Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) ...

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)....

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 electroencephalograp...

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...

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 EE...

Predicting EEG seizures using graded spiking neural networks.

To develop and evaluate a novel, non-patient-specific epileptic seizure prediction system using grad...

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 electr...

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 ...

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

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet ...

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...

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 sam...

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 wit...

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