Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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The promise of AI Large Language Models for Epilepsy care.

Artificial intelligence (AI) has been supporting our digital life for decades, but public interest i...

A hybrid EEG classification model using layered cascade deep learning architecture.

The problem of multi-class classification is always a challenge in the field of EEG (electroencephal...

eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms.

OBJECTIVE: Despite advances, analysis and interpretation of EEG still essentially rely on visual ins...

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.

. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work enviro...

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-base...

Detecting cognitive traits and occupational proficiency using EEG and statistical inference.

Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive st...

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.

The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Ev...

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally pro...

Fast reconstruction of EEG signal compression sensing based on deep learning.

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG...

Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI.

The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is th...

Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.

OBJECTIVE: In this study, we present a novel biomimetic deep learning network for epileptic spasms a...

Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.

OBJECTIVE: This longitudinal study investigated potential positive impact of CPAP treatment on brain...

A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI.

Deep learning (DL)-based methods have been successfully employed as asynchronous classification algo...

Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.

In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the ...

Timing matters for accurate identification of the epileptogenic zone.

OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning mo...

Classification of self-limited epilepsy with centrotemporal spikes by classical machine learning and deep learning based on electroencephalogram data.

Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epi...

Frame-based versus robot-assisted stereo-electro-encephalography for drug-resistant epilepsy.

BACKGROUND: Stereoelectroencephalography (SEEG) is an effective presurgical invasive evaluation for ...

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG.

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of...

Self-Supervised Learning for Electroencephalography.

Decades of research have shown machine learning superiority in discovering highly nonlinear patterns...

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