Seizures

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

Intravoxel incoherent motion (IVIM) is a diffusion-weighted magnetic resonance imaging (MRI) method that models slow (D, tissue diffusivity) and fast (D*, microvascular perfusion) signal components (f, signal fraction). This observational study explores the relationship between IVIM metrics and sleep macrostructure and physical activity levels in nineteen healthy elderly individuals (mean age 72.2...

An EEG-based hybrid machine learning approach for CT scan triage in mild traumatic brain injury.

Mild traumatic brain injury (mTBI) frequently prompts computed tomography (CT) imaging in emergency departments, despite a high proportion of negative findings. Objective, non-invasive tools that can support CT triage decisions under realistic clinical constraints are therefore needed. This study evaluates whether electroencephalography (EEG)-based biomarkers combined with temporal modeling can pr...

Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable sub-band topography.

Parkinson's disease (PD) diagnosis remains challenging because subtle neural alterations may be difficult to capture using conventional clinical asses...

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

The human brain maintains functional stability under changing conditions through interacting processes that include synaptic plasticity, homeostatic r...

A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals.

This paper introduces TESSCCo (TV-control EEG-based Silent Speech Command Corpus), a new dataset including electroencephalography (EEG) signals during...

Assessing the Robustness of Deep Learning Based Brain Age Prediction Models Across Multiple EEG Datasets.

The increasing availability of large electroencephalography (EEG) datasets enhances the potential clinical utility of deep learning (DL) for cognitive...

Cloud EEG Privacy Using Red-Billed Blue Magpie Optimized Physics-Penalized Dual-Branch Spectral-Spatial Neural Network for Epileptic Seizure Prediction.

Epileptic seizure prediction is a critical research area that enables timely intervention and prevention of severe neurological complications. With th...

An Enhanced Machine Learning-Based Multimodal Framework for Seizure Detection Using EEG and MRI Data.

One of the most common neurological disorders that immediately alters a person's way of life is an epileptic seizure. Accurate seizure detection remai...

An Effective LRSF-DLNN-Based Autism Spectrum Disorder Prediction Using EEG and fMRI.

In today's society, autism spectrum disorder (ASD) is a common neurological disorder that affects a person's behavior and communication. Hence, an ear...

Predicting Pain: Electroencephalography Signatures of Neural Integration During Experimental Tonic Thermal Pain.

BACKGROUND: The identification of reliable neural signatures for pain remains a critical challenge in both clinical and experimental settings. While e...

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

Retinotopic tuning of neural populations is a key organizing principle of human visual cortex. However, state-of-the-art models that predict neural re...

Hybrid CNN transformer framework for EEG-based epileptic seizure detection.

Early diagnosis and early intervention are important in the treatment of epilepsy, so detecting epileptic seizures from EEG signal is very important. ...

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

The structural and functional connectivity of the brain network is a combination of complex connections and interconnections among neurons of differen...

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Accurate and objective identification of Parkinson's Disease (PD) from Electroencephalogram (EEG) signals is important because EEG responses are compl...

Abrupt Scene Onsets and Gradually Emerging Scene Information Produce Distinct EEG Decoding Dynamics.

Multivariate analyses of M/EEG data are typically performed on neural responses time-locked to discrete stimulus onsets. Such designs usually reveal h...

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Depression is a serious mental health condition affecting millions worldwide. In recent years, deep learning models achieved remarkable performance in...

An Upper-Limb Motor Imagery EEG Dataset of Chronic Stroke Patients.

Motor imagery (MI)-based brain-computer interface (BCI) systems offer a promising approach for post-stroke motor rehabilitation. However, their clinic...

[An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer].

Electroencephalogram (EEG)-based emotion recognition is an important research area in affective computing and mental health assessment. To address the...

[A generalizable epilepsy detection network based on dual-attention mechanism].

Existing deep learning models for epileptic electroencephalogram (EEG) signal analysis frequently overlook intrinsic pathological characteristics duri...

Accuracy of the rapid-response electroencephalography's Automated Seizure Burden Estimator: A follow-up validation study of version 8 (AccuRASE II).

OBJECTIVE: Ceribell Inc.'s point-of-care electroencephalographic (EEG) system and artificial intelligence-based Automated Seizure Burden Estimator (AS...