Neurology

Seizures

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

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Cognitive workload estimation using physiological measures: a review.

Estimating cognitive workload levels is an emerging research topic in the cognitive neuroscience dom...

Portable Multi-focal Visual Evoked Potential Diagnostics for Multiple Sclerosis/Optic Neuritis patients.

PURPOSE: Multiple Sclerosis (MS) is a neuro-inflammatory disease of the Central Nervous System (CNS)...

Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.

OBJECTIVE: Despite recent advances, the decoding of auditory attention from brain signals remains a ...

Metaphoric language in the differential diagnosis of epilepsy and psychogenic non-epileptic seizures: Time to move forward.

Conversation analysis (CA) to identify metaphoric language (ML) has been proposed as a tool for the ...

Bilateral upper limb robot-assisted rehabilitation improves upper limb motor function in stroke patients: a study based on quantitative EEG.

BACKGROUND: Upper limb dysfunction after stroke seriously affects quality of life. Bilateral trainin...

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their ...

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

Identifying the location, the spatial extent and the electrical activity of distributed brain source...

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signal...

An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key a...

Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction.

In electroencephalography (EEG) classification paradigms, data from a target subject is often diffic...

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model.

This study presents a comprehensive examination of sex-related differences in resting-state electroe...

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.

PURPOSE OF REVIEW: Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques...

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

Depression severity can be classified into distinct phases based on the Beck depression inventory (B...

ChatGPT's responses to questions related to epilepsy.

This is a correspondence on published article on "ChatGPT's responses to questions related to epilep...

Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

Within the broader context of improving interactions between artificial intelligence and humans, the...

An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification.

Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between...

An EEG-based marker of functional connectivity: detection of major depressive disorder.

Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays ...

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention....

MuLHiTA: A Novel Multiclass Classification Framework With Multibranch LSTM and Hierarchical Temporal Attention for Early Detection of Mental Stress.

Mental stress is an increasingly common psychological issue leading to diseases such as depression, ...

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty.

In this study, we aim to develop a machine learning model to predict the level of coordination betwe...

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