EEG artifact removal—state-of-the-art and guidelines
JA Urigüen, B Garcia-Zapirain - Journal of neural engineering, 2015 - iopscience.iop.org
This paper presents an extensive review on the artifact removal algorithms used to remove
the main sources of interference encountered in the electroencephalogram (EEG) …
the main sources of interference encountered in the electroencephalogram (EEG) …
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 …
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 …
A Machine Learning‐Based Big EEG Data Artifact Detection and Wavelet‐Based Removal: An Empirical Approach
The electroencephalogram (EEG) signals are a big data which are frequently corrupted by
motion artifacts. As human neural diseases, diagnosis and analysis need a robust …
motion artifacts. As human neural diseases, diagnosis and analysis need a robust …
[图书][B] The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance
PS Addison - 2017 - taylorfrancis.com
This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition
Abstract Independent Component Analysis (ICA) has proven to be an effective data driven
method for analyzing EEG data, separating signals from temporally and functionally …
method for analyzing EEG data, separating signals from temporally and functionally …
Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA
R Mahajan, BI Morshed - IEEE journal of Biomedical and …, 2014 - ieeexplore.ieee.org
Brain activities commonly recorded using the electroencephalogram (EEG) are
contaminated with ocular artifacts. These activities can be suppressed using a robust …
contaminated with ocular artifacts. These activities can be suppressed using a robust …
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 …
Automatic ocular artifacts removal in EEG using deep learning
B Yang, K Duan, C Fan, C Hu, J Wang - Biomedical Signal Processing and …, 2018 - Elsevier
Ocular artifacts (OAs) are one the most important form of interferences in the analysis of
electroencephalogram (EEG) research. OAs removal/reduction is a key analysis before the …
electroencephalogram (EEG) research. OAs removal/reduction is a key analysis before the …
Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data
Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye
blinks and electrical noise, etc., is an important problem in EEG signal processing research …
blinks and electrical noise, etc., is an important problem in EEG signal processing research …