Neurology

Seizures

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

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A systematic evaluation of Euclidean alignment with deep learning for EEG decoding.

Electroencephalography signals are frequently used for various Brain-Computer interface (BCI) tasks....

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

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations ...

Machine Learning in Electroconvulsive Therapy: A Systematic Review.

Despite years of research, we are still not able to reliably predict who might benefit from electroc...

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 ...

Multimodal fusion for anticipating human decision performance.

Anticipating human decisions while performing complex tasks remains a formidable challenge. This stu...

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 ...

Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements.

OBJECTIVES: The primary objective of this research is to improve the average classification performa...

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 detec...

Adaptive Multimodel Knowledge Transfer Matrix Machine for EEG Classification.

The emerging matrix learning methods have achieved promising performances in electroencephalogram (E...

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....

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 de...

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 E...

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...

Seizure Detection Based on Lightweight Inverted Residual Attention Network.

Timely and accurately seizure detection is of great importance for the diagnosis and treatment of ep...

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 state...

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 hi...

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 (EE...

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 burd...

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 t...

EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.

Human emotions are complex psychological and physiological responses to external stimuli. Correctly ...

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