Seizures

EEG-CMT: Spatial-Temporal Representation of EEG for Emotion Recognition Using Convolutional Neural Networks and Vision Transformers.

Background Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional relationships of EEG acquisition electrodes. Additionally, conventional models struggled to simultaneously capture both fine-grained temporal-spatial features and long-range dependencies in EEG signals. New method To address these limitatio...

Differentiating Depression Subtypes through three-electrode EEG and Emotional Stimuli: A Machine Learning Approach.

We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR). EEG was recorded from 70 participants (31 first depressive episode, FDE; 18 recurrent depressive episode, RDE; 21 control participants, HC) using a compact frontal montage (Fp1/Fpz/Fp2) during positive and negative VR conditions, focusing on the im...

A Deep Learning Approach for Analyzing Brainwave Signals During Audio Meditation in Expectant Mothers: Variational Mode Decomposition with a CNN-BiLSTM Model.

Meditation has increasingly been recognized as a helpful non-pharmacological intervention to treat psychological stress, anxiety, and depression durin...

Computational delineation and cellular profiling of murine cortical cell layers using multiplex immunofluorescence imaging.

BACKGROUND: The adult mammalian cerebral cortex has a vertical laminar organization consisting of six neuronal layers, with each layer subserving a sp...

A Digital Intervention for Capturing Real-Time Health Data for Epilepsy Seizure Forecasting: Protocol for the ATMOSPHERE Study.

BACKGROUND: Epilepsy is a chronic neurological disorder marked by recurrent and apparently unpredictable seizures and associated with premature death,...

Research on epilepsy detection and recognition based on the combination of time frequency transform and deep learning model.

To improve the detection performance of epileptic electroencephalogram (EEG) signals and address their non-stationary characteristics,this paper compa...

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 severe pressure on healthcare systems. Early detection...

Independent evaluation of deep learning models for detecting focal cortical dysplasia.

The purpose of this study is to perform an independent assessment of three state-of-the-art tools for the detection of focal cortical dysplasia (FCD) ...

MAGCANet: A multiscale adaptive graph-convolutional attention network for MI-EEG decoding.

OBJECTIVE: Motor imagery EEG (MI-EEG) decoding remains challenging due to low signal-to-noise ratios and pronounced inter-subject variability. Althoug...

Interrater Reliability of Electroencephalogram Interpretations in Febrile Comatose African Children: Implications for Automated Interpretation Algorithm Creation.

Electroencephalography (EEG) is a diagnostic and prognostic tool used worldwide in the clinical care of comatose patients. Scalability of EEG use in r...

An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection.

Chronic stress is an important threat in Public Health, as it negatively impacts both the Body and Mind. Current methods for measuring and identifying...

Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration.

Artificial intelligence (AI) has emerged as a transformative force in neurology, offering unprecedented potential to enhance diagnostic precision, str...

Corticomorphic Hybrid CNN-SNN Architecture for EEG-Based Low-Footprint Low-Latency Auditory Attention Detection.

PURPOSE: In the context of a multi-speaker "cocktail party" scenario where listeners selectively focus on specific speakers, human auditory attention ...

Identification of nonconvulsive status epilepticus in the ictal-interictal continuum using artificial intelligence: a prospective observational cohort study.

PURPOSE: This study aims to investigate differences in functional connectivity between patients on the ictal-interictal continuum (IIC) with nonconvul...

Temporal and Cell-Specific Regulation of Synaptic Homeostasis by the Chromatin Remodeler Chd1.

Disruptions in chromatin remodelers and synaptic proteins represent major genetic risk factors for autism spectrum disorder (ASD), yet how these disti...

Epilepsy: Modern Treatment Approaches, Clinical Aspects, and Patent Status.

Epilepsy is a chronic neurological disorder causing recurrent seizures. Improved diagnosis and management, including high-resolution imaging, genetic ...

A time-frequency cross-attention network model for epileptic seizure detection.

Epilepsy is a chronic neurological disease that profoundly impacts patients' daily lives. Electroencephalography (EEG) serves as a crucial tool for th...

Estimating cognitive function score from mild cognitive impairment to moderate dementia using a hybrid model combining plasma biomarkers with electroencephalogram signal.

BackgroundPredicting cognitive function across dementia stages remains challenging. Plasma biomarkers and electroencephalogram (EEG) features may prov...

Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks.

OBJECTIVE: Drug-resistant epilepsy (DRE) affects approximately one-third of patients with epilepsy. The molecular heterogeneity underlying DRE remains...

Sub-band Embedding Based EEG Spatio-Temporal Activity Representation for Emotion Recognition.

EEG-based emotion recognition is a crucial task with significant implications for mental health monitoring, affective computing, and clinical decision...