Seizures

Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.

An attention-augmented multimodal classification of alzheimer's disease and parkinson's disease vs healthy controls using MRI, EEG, and SNP data.

Due to the late manifestation of structural symptoms and symptomatic overlap, neurodegenerative dise...

Enhancing Brain-Computer interface performance through source-level attention mechanism: An EEG motor imagery study.

BACKGROUND: Brain-computer interfaces (BCIs) enable direct communication between humans and machines...

ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.

End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in ...

Explainable AI for pain perception: subject-independent EEG decoding using DeepSHAP and CNNs.

Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly ...

Robust multimodal mental workload classification: A cross-physiological condition machine learning approach.

BACKGROUND AND OBJECTIVE: Aircraft pilots can be faced with a high mental workload (MW) combined wit...

Optimization and validation of multiscale feature selection for EEG-based recognition of drivers' negative emotions.

OBJECTIVE: Negative emotions, such as stress and anger, are significant factors leading to dangerous...

Quantifying the impact of a computer-aided diagnostic score on the clinical diagnosis of functional seizures.

OBJECTIVE: The diagnosis of functional/dissociative seizures (FDS) without ictal video-electroenceph...

Quantum inspired wavelet and Fourier feature fusion for EEG based epilepsy and seizure detection.

The electroencephalography (EEG) signals are the cheapest approach to study the brain information, c...

Rapid, non-invasive diagnosis of tuberculosis using desorption separation ionization mass spectrometry (DSI-MS) and urinary carbonyl metabolite fingerprints.

Tuberculosis (TB) remains a leading global infectious disease that demands rapid, non-invasive diagn...

Exploring cognitive workload recognition using CogRepLKNet with EEG-fMRI.

Accurate multimodal Cognitive Workload Recognition (CWR) remains challenging due to the difficulty o...

Unlocking Insights from Postictal EEGs: Investigating Predictive Markers of Seizure Recurrence.

PURPOSE: Seizure recurrence, often presenting as clusters, is a major clinical concern linked to inc...

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 ...

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

Schizophrenia is a complex psychiatric disorder marked by cognitive and perceptual disruptions, for ...

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 Proxim...

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 ...

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 depress...

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 burde...

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

OBJECTIVE: Accurate preoperative lateralization of temporal lobe epilepsy (TLE) remains challenging,...

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, c...

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 cog...