Seizures

TPRO-NET: an EEG-based emotion recognition method reflecting subtle changes in emotion.

Emotion recognition based on Electroencephalogram (EEG) has been applied in various fields, including human-computer interaction and healthcare. However, for the popular Valence-Arousal-Dominance emotion model, researchers often classify the dimensions into high and low categories, which cannot reflect subtle changes in emotion. Furthermore, there are issues with the design of EEG features and the...

LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.

Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated ...

Epilepsy detection based on multi-head self-attention mechanism.

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due t...

Machine Learning in Electroconvulsive Therapy: A Systematic Review.

Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust w...

An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features.

Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accurac...

Multimodal fusion for anticipating human decision performance.

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach...

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translat...

An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the ...

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroenc...

An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.

Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in...

Adaptive Multimodel Knowledge Transfer Matrix Machine for EEG Classification.

The emerging matrix learning methods have achieved promising performances in electroencephalogram (EEG) classification by exploiting the structural in...

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.

OBJECTIVE: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotion...

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substa...

Seizure Detection Based on Lightweight Inverted Residual Attention Network.

Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models...

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.

BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated ...

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EE...

Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.

INTRODUCTION: Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there ar...

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed...

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting.

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnost...

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies...