Seizures

K-means++ guided multi-view CNN with channel attention for EEG emotion recognition.

The use of artificial intelligence for emotion recognition is the focus of improving human-computer interaction. Recently, deep learning has been widely used in the study of emotion recognition. However, how to correctly identify emotions still faces a huge challenge. We propose a multi-view deep CNN based on channel attention (MVACNN) for EEG emotion recognition. MVACNN first clustered the channe...

EEG-based schizophrenia classification using attention-integrated deep convolutional networks.

Schizophrenia is a complex psychiatric disorder marked by cognitive and perceptual disruptions, for which electroencephalography (EEG) provides a valuable non-invasive biomarker. In this study, we propose a convolutional attention-based deep learning framework for the automatic detection of schizophrenia from EEG signals. The model integrates spatial feature extraction via convolutional layers wit...

A unified framework for EEG seizure detection using universum-integrated generalized eigenvalues proximal support vector machine.

The paper presents novel Universum-enhanced classifiers: the Universum Generalized Eigenvalue Proximal Support Vector Machine (U-GEPSVM) and the Impro...

NeuroAdaptive multi-resolution integration network for decoding cognitive complexity levels in EEG-based pronoun resolution tasks.

Pronoun resolution represents a fundamental language comprehension process that varies in cognitive complexity. Prior studies have identified behavior...

An Interpretable Hybrid Neural Network Integrating Sinc-Convolution and Transformer for EEG-Based Depression Detection.

EEG recordings obtained before medication are regarded as valuable biological indicators for depression detection. Currently, depression diagnosis bas...

A causal attention network with time frequency channel feature fusion for epileptic seizure prediction.

BACKGROUND: Epilepsy poses ongoing physical and mental threats and causes substantial economic burdens. Better seizure forecasting enables faster medi...

Diffusion spectrum imaging-based machine learning for temporal lobe epilepsy lateralization.

OBJECTIVE: Accurate preoperative lateralization of temporal lobe epilepsy (TLE) remains challenging, particularly in cases with subtle or MRI-negative...

Multi-view ensemble learning for EEG-based detection of Obsessive-Compulsive Disorder.

Psychiatric disorders pose a critical challenge in modern healthcare due to their high prevalence, complex symptomatology, and reliance on subjective ...

Brain states analysis of EEG predicts multiple sclerosis and mirrors disease duration and burden.

BACKGROUND: Any treatment of multiple sclerosis should preserve mental function, considering how cognitive deterioration interferes with quality of li...

PyCaret machine learning library with three preprocessing steps after eLORETA source estimation predicts Alzheimer's disease.

Alzheimer's disease (AD) -the most common form of dementia- begins with mild memory loss and gradually progresses, eventually resulting in a generaliz...

Automatic EEG-based dream-related emotion recognition using fuzzy entropy and efficient signal decomposition methods.

BACKGROUND AND OBJECTIVE: Dreams can reflect our profound needs and desires, intrinsically linked to emotional processes. In recent years, research on...

Multi-scale EEG analysis identifies neural circuit signatures of iTBS responsiveness in major depressive disorder.

BACKGROUND: Response to transcranial magnetic stimulation (TMS) in major depressive disorder (MDD) is highly variable, underscoring the need for bioma...

Impact of mobile phone use on the brain activity: Audio call vs video call.

The growing dependence on mobile phones for communication has raised concerns regarding the neurological impact of radio-frequency electromagnetic fie...

Altered resting-state sensorimotor network in patients with obsessive-compulsive disorder: An EEG study.

BACKGROUND AND OBJECTIVE: Dysfunction in the cortical-striatal-thalamo-cortical circuit is considered a core pathological mechanism of obsessive-compu...

Multi-Domain Dynamic Weighting Network for Motor Imagery Decoding.

In motor imagery (MI)-based brain-computer interfaces (BCIs), convolutional neural networks (CNNs) are widely employed to decode electroencephalogram ...

Stress detection using the phase controlled Bi-channel adaptive features from the brain EEG signals.

This work proposes a stress classification system from the electroencephalogram (EEG) signals collected from the stress subjects. The scheme extracts ...

Modelling seizure-related predictors of epilepsy diagnostic gap in two urban informal settlements of Nairobi using machine learning.

BACKGROUND: There is a wide gap in epilepsy diagnosis, particularly in low- and middle-income countries. We used machine learning models to identify s...

GenEEG: Improving epileptic EEG detection through patient-adaptive latent diffusion and continual learning.

Automated seizure detection systems face significant challenges due to the limited availability of clinical EEG data, a substantial class imbalance be...

Graph attention network with comorbidity connectivity embedding for post-traumatic epilepsy risk prediction using sparse time-series electronic health records.

BACKGROUND: Traumatic brain injury (TBI) is a major risk factor for neurological disorders, including post-traumatic epilepsy (PTE), a debilitating co...

Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density.

Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investiga...