Generative adversarial networks in EEG analysis: an overview
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as
engineering applications. However, one of the challenges associated with recording EEG …
engineering applications. However, one of the challenges associated with recording EEG …
A survey on eeg signal processing techniques and machine learning: Applications to the neurofeedback of autobiographical memory deficits in schizophrenia
In this paper, a general overview regarding neural recording, classical signal processing
techniques and machine learning classification algorithms applied to monitor brain activity is …
techniques and machine learning classification algorithms applied to monitor brain activity is …
Analysis of epileptic seizures based on EEG using recurrence plot images and deep learning
This work proposes deep learning (DL) based epileptic seizure detection by generating 2D
recurrence plot (RP) images of EEG signals for specific brain rhythms. The DL bypasses …
recurrence plot (RP) images of EEG signals for specific brain rhythms. The DL bypasses …
Compact convolutional neural network with multi-headed attention mechanism for seizure prediction
X Ding, W Nie, X Liu, X Wang, Q Yuan - International Journal of …, 2023 - World Scientific
Epilepsy is a neurological disorder related to frequent seizures. Automatic seizure prediction
is crucial for the prevention and treatment of epilepsy. In this paper, we propose a novel …
is crucial for the prevention and treatment of epilepsy. In this paper, we propose a novel …
Two-stage approach with combination of outlier detection method and deep learning enhances automatic epileptic seizure detection
Many approaches to automated epileptic seizure detection share a common challenge—the
trade-off between recall and precision. This study aims to develop a novel approach for …
trade-off between recall and precision. This study aims to develop a novel approach for …
Data augmentation for seizure prediction with generative diffusion model
Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-
based seizure prediction methods. However, existing DA approaches are just the linear …
based seizure prediction methods. However, existing DA approaches are just the linear …
[HTML][HTML] Deep Generative Models: The winning key for large and easily accessible ECG datasets?
Large high-quality datasets are essential for building powerful artificial intelligence (AI)
algorithms capable of supporting advancement in cardiac clinical research. However …
algorithms capable of supporting advancement in cardiac clinical research. However …
Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder
Introduction Major depressive disorder (MDD) is the most common mental disorder
worldwide, leading to impairment in quality and independence of life …
worldwide, leading to impairment in quality and independence of life …
Multichannel synthetic preictal EEG signals to enhance the prediction of epileptic seizures
Epilepsy is a chronic neurological disorder affecting 1% of people worldwide, deep learning
(DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for …
(DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for …
A systematic review of machine learning models in mental health analysis based on multi-channel multi-modal biometric signals
J Ehiabhi, H Wang - BioMedInformatics, 2023 - mdpi.com
With the increase in biosensors and data collection devices in the healthcare industry,
artificial intelligence and machine learning have attracted much attention in recent years. In …
artificial intelligence and machine learning have attracted much attention in recent years. In …