Seizures

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 sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the underlying Electrophysiological Source Imaging (ESI) problem. To guarantee a unique solution, most existing ESI methods pay more attention to solve this in...

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 aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has shown potential as a cost-effective alternative for rapid diagnosis. In this study, we review the current state of machine learning in the detection a...

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) signals is often needed to localize the epileptogenic zo...

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

In electroencephalography (EEG) classification paradigms, data from a target subject is often difficult to obtain, leading to difficulties in training...

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 electroencephalogram (EEG) data, leveraging two different ...

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 to translate raw data into applicable and interpr...

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 (BDI) test scores, a subjective questionnaire. Howev...

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise an...

ChatGPT's responses to questions related to epilepsy.

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

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 question has arisen regarding whether auditory an...

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 human brain and external devices. Among EEG-based...

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 for MDD, but rapid and reliable detection remains ...

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, addiction, and heart attack. In this study, an ear...

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

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-st...

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 between two players in tacit coordination games by anal...

A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography.

Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and psychological states, thus enhancing human-machine ...

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

UNLABELLED: In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its co...

An Effective Hybrid Deep Learning Model for Single-Channel EEG-Based Subject-Independent Drowsiness Recognition.

Nowadays, road accidents pose a severe risk in cases of sleep disorders. We proposed a novel hybrid deep-learning model for detecting drowsiness to ad...

Assessing the performance of ChatGPT's responses to questions related to epilepsy: A cross-sectional study on natural language processing and medical information retrieval.

BACKGROUND: Epilepsy is a neurological condition marked by frequent seizures and various cognitive and psychological effects. Reliable information is ...

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network.

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decodi...