Removal of artifacts from EEG signals: a review
Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
Methods for artifact detection and removal from scalp EEG: A review
Electroencephalography (EEG) is the most popular brain activity recording technique used
in wide range of applications. One of the commonly faced problems in EEG recordings is the …
in wide range of applications. One of the commonly faced problems in EEG recordings is the …
Taxonomy on EEG artifacts removal methods, issues, and healthcare applications
Electroencephalogram (EEG) signals are progressively growing data widely known as
biomedical big data, which is applied in biomedical and healthcare research. The …
biomedical big data, which is applied in biomedical and healthcare research. The …
DeprNet: A deep convolution neural network framework for detecting depression using EEG
Depression is a common reason for an increase in suicide cases worldwide. Thus, to
mitigate the effects of depression, accurate diagnosis and treatment are needed. An …
mitigate the effects of depression, accurate diagnosis and treatment are needed. An …
An EEG channel selection method for motor imagery based brain–computer interface and neurofeedback using Granger causality
H Varsehi, SMP Firoozabadi - Neural Networks, 2021 - Elsevier
Motor imagery (MI) brain–computer interface (BCI) and neurofeedback (NF) with
electroencephalogram (EEG) signals are commonly used for motor function improvement in …
electroencephalogram (EEG) signals are commonly used for motor function improvement in …
The multiscale entropy algorithm and its variants: A review
A Humeau-Heurtier - Entropy, 2015 - mdpi.com
Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of
a time series by quantifying its entropy over a range of temporal scales. The algorithm has …
a time series by quantifying its entropy over a range of temporal scales. The algorithm has …
Identification and removal of physiological artifacts from electroencephalogram signals: A review
Electroencephalogram (EEG), boasting the advantages of portability, low cost, and
hightemporal resolution, is a non-invasive brain-imaging modality that can be used to …
hightemporal resolution, is a non-invasive brain-imaging modality that can be used to …
EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression
Background Artifact contamination reduces the accuracy of various EEG based
neuroengineering applications. With time, biomedical signal denoising has been the utmost …
neuroengineering applications. With time, biomedical signal denoising has been the utmost …
EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation
Stroke is the second foremost cause of death worldwide and is one of the most common
causes of disability. Several approaches have been proposed to manage stroke patient …
causes of disability. Several approaches have been proposed to manage stroke patient …
Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing
Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The
condition of bolt joints has a significant impact on the safe and reliable operation of the …
condition of bolt joints has a significant impact on the safe and reliable operation of the …