[Retracted] EEG‐Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
I Ahmad, X Wang, M Zhu, C Wang, Y Pi… - Computational …, 2022 - Wiley Online Library
Epileptic seizure is one of the most chronic neurological diseases that instantaneously
disrupts the lifestyle of affected individuals. Toward developing novel and efficient …
disrupts the lifestyle of affected individuals. Toward developing novel and efficient …
Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
A review on machine learning for EEG signal processing in bioengineering
MP Hosseini, A Hosseini, K Ahi - IEEE reviews in biomedical …, 2020 - ieeexplore.ieee.org
Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …
conditions in patients since its discovery. Due to the many different types of classifiers …
Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
Eeg-gcnn: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network
N Wagh, Y Varatharajah - Machine Learning for Health, 2020 - proceedings.mlr.press
This paper presents a novel graph convolutional neural network (GCNN)-based approach
for improving the diagnosis of neurological diseases using scalp-electroencephalograms …
for improving the diagnosis of neurological diseases using scalp-electroencephalograms …
Applications of artificial intelligence in automatic detection of epileptic seizures using EEG signals: A review
S Saminu, G Xu, S Zhang… - Artificial Intelligence …, 2023 - ojs.bonviewpress.com
Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a
tedious and time-consuming task that may take several years of manual training due to its …
tedious and time-consuming task that may take several years of manual training due to its …
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
Deep learning for EEG-based biometric recognition
E Maiorana - Neurocomputing, 2020 - Elsevier
The exploitation of brain signals for biometric recognition purposes has received significant
attention from the scientific community in the last decade, with most of the efforts so far …
attention from the scientific community in the last decade, with most of the efforts so far …
Brainnet: Epileptic wave detection from seeg with hierarchical graph diffusion learning
Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's
population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves …
population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves …