Seizures

Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods.

Deep learning for cross-subject EEG decoding is hindered by the high degree of inter-subject variability, which creates a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject sett...

Deep learning discriminates seizures from normal brain oscillations in the electroencephalogram of a rat model of post-traumatic epilepsy.

This study used machine learning to objectively identify seizures in the electroencephalogram of a model of post-traumatic epilepsy based on fluid percussion injury in male rats. We applied transfer learning to a neural-network trained and tested on three potentially distinct electroencephalographic phenotypes: (1) late-onset convulsive seizures associated with rare post-traumatic epilepsy, (2) ea...

Machine learning classification of patients after suicide attempts using demographic data, EEG connectivity and heart rate variability.

OBJECTIVE: The aim of this study was to develop a way to distinguish suicidal patients based on their electrophysiologic (EEG connectivity and heart r...

Feasibility of a hybrid SSVEP-motor imagery BCI with robotic feedback for upper limb motor rehabilitation in stroke patients.

BACKGROUND: Stroke remains a leading cause of long-term disability, necessitating innovative neurorehabilitation strategies to address persistent moto...

Causal ordinal connections based characterization of weighted effective brain network for schizophrenia detection.

The human brain is responsible for a wide range of a person's behavioral and cognitive capabilities. The functionality of the brain is affected by var...

Alzheimer's disease with progression analysis using a novel dilated convolutional attention based long short term memory model.

Alzheimer's disease (AD) is an irreversible neurodegenerative syndrome that affects memory, cognitive abilities and behaviour. Detecting AD in the ear...

Systematic review of machine learning and deep learning models for EEG-based detection of depression.

OBJECTIVE: Depression is a leading cause of global disability, motivating the development of objective and scalable diagnostic approaches. Quantitativ...

EEG-based schizophrenia detection using handcrafted biomarkers and a TOA-optimized hybrid multi-branch CNN-Transformer framework.

Schizophrenia is a chronic psychiatric disorder for which electroencephalography (EEG) offers a low-cost, non-invasive window into abnormal neural dyn...

A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries.

IntroductionEEGLAB is a widely used software for analyzing electroencephalography (EEG) datasets, with over 20 years of global use. This bibliometric ...

Integrating EEG microstate dynamics in a stacked ensemble for neurodiagnostic ASD assessment.

Autism Spectrum Disorder (ASD) remains diagnostically challenging due to its neurobiological heterogeneity and the current reliance on subjective beha...

A comparative evaluation of EEG-based deep learning models for schizophrenia detection with cross-dataset validation and explainable AI.

OBJECTIVES: Schizophrenia is a neuropsychiatric disorder that affects emotional, behavioral, and brain functions that can be tracked using electroence...

An EEG-EMG-kinematics dataset from wrist pointing tasks for biomarker research in neurorehabilitation.

This work presents a multimodal dataset containing synchronized electroencephalography (EEG), electromyography (EMG), and kinematic recordings acquire...

RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.

Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). ...

MRI-based machine learning model to distinguish hippocampal sclerosis (HS) ILAE type 1 and no HS gliosis only in medial temporal lobe epilepsy.

PURPOSE: Despite recent advances in preoperative work-up of drug resistant medial temporal lobe epilepsy (MTLE), predicting post-surgical seizure and ...

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks with Pyramid Squeeze Attention.

Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high informat...

Learning Where to Look: Differentiable Slice Selection and Efficient Channel Attention for FCD-II MRI Classification.

Focal Cortical Dysplasia (FCD) is a major cause of drug-resistant epilepsy both in children and adults. In most such cases, surgery is the most effect...

CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two dementia-related brain illnesses that are prevalent among elders in the twenty-fi...

A motor thalamic site in humans that suppresses involuntary breathing without awareness.

Breathing is generated by brainstem respiratory networks but can be controlled and modulated by forebrain activity. The recent clinical adoption of th...

Persistent white matter disruption underlies apparent functional normalization in intractable temporal lobe epilepsy: Evidence from multimodal MRI.

BACKGROUND: Intractable temporal lobe epilepsy (ITLE) poses ongoing therapeutic challenges due to resistance to antiseizure medications and limited im...

Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.

Obtaining sufficient electroencephalography (EEG) signals for training deep neural networks (DNNs) in brain-computer interfaces (BCIs) is challenging ...