[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 …
MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
P Autthasan, R Chaisaen… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …
control of several applications by decoding neurophysiological phenomena, which are …
Dimensionality reduction methods for brain imaging data analysis
The past century has witnessed the grand success of brain imaging technologies, such as
electroencephalography and magnetic resonance imaging, in probing cognitive states and …
electroencephalography and magnetic resonance imaging, in probing cognitive states and …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network
In this article, an optimized variational mode decomposition (OVMD), reduced deep
convolutional neural network (RDCNN), and multi-kernel random vector functional link …
convolutional neural network (RDCNN), and multi-kernel random vector functional link …
An automatic method for epileptic seizure detection based on deep metric learning
L Duan, Z Wang, Y Qiao, Y Wang… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of
epilepsy which is a life-threatening neurological disorder. Many algorithms have been …
epilepsy which is a life-threatening neurological disorder. Many algorithms have been …
Epileptic seizure recognition using reduced deep convolutional stack autoencoder and improved kernel RVFLN from EEG signals
In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved
kernel random vector functional link network (IKRVFLN) are combined to recognize the …
kernel random vector functional link network (IKRVFLN) are combined to recognize the …
Classification of EEG signals for epileptic seizures using feature dimension reduction algorithm based on LPP
Y Liu, B Jiang, J Feng, J Hu, H Zhang - Multimedia Tools and Applications, 2021 - Springer
Computer-aided diagnosis of epilepsy based on Electroencephalography (EEG) analysis is
a beneficial practice which adopts machine learning to increase the recognition rate and …
a beneficial practice which adopts machine learning to increase the recognition rate and …
Epileptic seizure detection in EEG signals using discriminative Stein kernel-based sparse representation
The automatic seizure detection in electroencephalogram (EEG) signals is crucial for the
monitoring, diagnosis, and treatment of epilepsy. In this study, an intelligent detection …
monitoring, diagnosis, and treatment of epilepsy. In this study, an intelligent detection …
Motor imagery EEG signal classification using upper triangle filter bank auto-encode method
R Tang, Z Li, X Xie - Biomedical Signal Processing and Control, 2021 - Elsevier
In motor-imagery-based brain–computer interfaces, the frequency, and spatial information of
electroencephalography signals can be used to improve the performance of motor imagery …
electroencephalography signals can be used to improve the performance of motor imagery …