Neurology

Seizures

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

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Dynamic Graphs Analysis of EEG

In this study, we investigate the use of temporal dynamics in brain connectivity for the classificat...

Personalized real-time inference of momentary excitability from human EEG

The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols t...

The Use of Artificial Intelligence In Magnetic Resonance Imaging of Epilepsy: A Systematic Review and Meta-Analysis

The application of artificial intelligence (AI)/machine learning (ML) to MRI can be a powerful tool ...

Design and Implementation of a Decision Making System for Controlling a Hand Exoskeleton Based on EEG/EMG Signals

This paper presents an approach of combining Electroencephalography (EEG) and Electromyography (EMG)...

A Shared Neural Marker Predicts Creative Performance Across Distinct Problem-Solving Tasks

Creativity is essential for innovation, yet the brain mechanisms supporting its moment-to-moment var...

Distinct brain mechanisms support trust violations, belief integration, and bias in human-AI teams

This study provides an integrated electrophysiological and behavioral account of the neuro-cognitive...

A lightweight, physics-based, sensor-fusion filter for real-time EEG denoising and improved downstream AI classification

Physiological time-series data, like electroencephalography (EEG), are vulnerable to motion, ocular,...

Interpretable Machine Learning Identifies an Emergent Absence Seizure Mechanism

Absence epilepsy is a generalized seizure disorder marked by widespread spike-and-wave oscillations ...

Variational autoencoder for interpretable seizure onset phases detection

In this study, we describe a deep learning framework for automated seizure annotation in stereo elec...

Shared texture-like representations, not global form, underlie deep neural network alignment with human visual processing

Deep neural networks (DNNs) are a leading computational framework for understanding neural visual pr...

High-level Prediction of Continuous Speech During Mind-Wandering

Abundant evidence shows that when listening to speech or reading text, we continuously make predicti...

Toward Unified Biomarkers for Focal Epilepsy

Accurately localizing the epileptogenic network (EpiNet) remains a major barrier to effective epilep...

Learning Residual-based Biomarkers of Cognitive Health via Self-Supervised Learning on EEG State Transitions

Deep learning (DL) models have achieved impressive performance in EEG-based prediction tasks, but th...

Improved sensory representations as a result of temporal adaptation

Human perception is robust under challenging conditions, for example when sensory inputs change over...

Integrating Data Across Oscillatory Power Bands Predicts the Seizure Onset Zone in Focal Epilepsies

Accurate identification of the seizure onset zone (SOZ) using intracranial electroencephalography (i...

Spatio Temporal Attentional EEGNet: An Enhanced Deep Learning Model for Cognitive Workload Detection

Deep learning has emerged as a powerful tool for extracting meaningful patterns from electroencephal...

Deep Coupled Kuramoto Oscillatory Neural Network (DcKONN): A Biologically Inspired Deep Neural Model for EEG Signal Analysis

Deep neural networks applied to signal processing tasks often need specialized architectural mechani...

Designing a Model to Detect Beta Burst in EEG Using Nonlinear Dynamic Features Based on Machine Learning

Beta bursts are brief, transient increases in beta-band (13–30 Hz) EEG activity that play a key role...

A groove brain-music interface for enhancing individual experience of urge to move

When we listen to music, we often feel a pleasurable urge to move to music, known as groove. While p...

Functional MRI signals as fast as 1Hz are coupled to brain states and predict spontaneous neural activity

fMRI signals were traditionally seen as slow and sampled in the order of seconds, but recent technol...

Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection

Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts seve...

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