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Title
Removal of Artifacts from EEG Signals: A Review
Authors
Keywords
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Journal
SENSORS
Volume 19, Issue 5, Pages 987
Publisher
MDPI AG
Online
2019-02-27
DOI
10.3390/s19050987
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- (2018) Houtan Jebelli et al. AUTOMATION IN CONSTRUCTION
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- (2018) Xun Chen et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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- (2018) Emina Alickovic et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Surrogate-Based Artifact Removal From Single-Channel EEG
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- A generic EEG artifact removal algorithm based on the multi-channel Wiener filter
- (2018) Ben Somers et al. Journal of Neural Engineering
- A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings
- (2018) Gabriella Tamburro et al. PeerJ
- A New fMRI Informed Mixed-Norm Constrained Algorithm for EEG Source Localization
- (2018) Hailing Wang et al. IEEE Access
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- (2018) Xun Chen et al. IEEE SENSORS JOURNAL
- Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features
- (2017) Thea Radüntz et al. Journal of Neural Engineering
- Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition
- (2017) Banghua Yang et al. Cognitive Computation
- MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG
- (2017) Li Dong et al. Frontiers in Neuroscience
- A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System
- (2017) Sheng Ge et al. IEEE Access
- Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis
- (2016) Frederik Van de Steen et al. BRAIN TOPOGRAPHY
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- (2016) Xun Chen et al. IEEE SENSORS JOURNAL
- Joint Blind Source Separation for Neurophysiological Data Analysis: Multiset and multimodal methods
- (2016) Xun Chen et al. IEEE SIGNAL PROCESSING MAGAZINE
- Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis
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- Methods for artifact detection and removal from scalp EEG: A review
- (2016) Md Kafiul Islam et al. NEUROPHYSIOLOGIE CLINIQUE-CLINICAL NEUROPHYSIOLOGY
- An unsupervised eye blink artifact detection method for real-time electroencephalogram processing
- (2016) Won-Du Chang et al. PHYSIOLOGICAL MEASUREMENT
- The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition
- (2016) Gang Wang et al. IEEE Journal of Biomedical and Health Informatics
- Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings
- (2016) Yuan Zou et al. IEEE Journal of Biomedical and Health Informatics
- FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing
- (2015) Ian Daly et al. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
- EEG artifact removal—state-of-the-art and guidelines
- (2015) Jose Antonio Urigüen et al. Journal of Neural Engineering
- Autoregressive model in the Lp norm space for EEG analysis
- (2015) Peiyang Li et al. JOURNAL OF NEUROSCIENCE METHODS
- A practical guide to the selection of independent components of the electroencephalogram for artifact correction
- (2015) Maximilien Chaumon et al. JOURNAL OF NEUROSCIENCE METHODS
- Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical correlation analysis (emiCCA)
- (2015) Li Dong et al. NEUROIMAGE
- Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA
- (2015) Ruhi Mahajan et al. IEEE Journal of Biomedical and Health Informatics
- Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal
- (2014) Nadia Mammone et al. Entropy
- Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation
- (2014) Mehdi Bagheri Hamaneh et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- The Effect of Multiscale PCA De-noising in Epileptic Seizure Detection
- (2014) Jasmin Kevric et al. JOURNAL OF MEDICAL SYSTEMS
- Robust artifactual independent component classification for BCI practitioners
- (2014) Irene Winkler et al. Journal of Neural Engineering
- A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG
- (2014) Xun Chen et al. SENSORS
- Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis
- (2014) Xun Chen et al. Journal of Applied Mathematics
- L1 Norm based common spatial patterns decomposition for scalp EEG BCI
- (2013) Peiyang Li et al. Biomedical Engineering Online
- The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique
- (2012) Kevin T. Sweeney et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Artifact Removal in Physiological Signals—Practices and Possibilities
- (2012) K. T. Sweeney et al. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
- EEG source imaging in epilepsy—practicalities and pitfalls
- (2012) Kitti Kaiboriboon et al. Nature Reviews Neurology
- Artifact suppression from EEG signals using data adaptive time domain filtering
- (2012) Md. Khademul Islam Molla et al. NEUROCOMPUTING
- Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches
- (2012) Doha Safieddine et al. EURASIP Journal on Advances in Signal Processing
- REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts
- (2011) Manousos A. Klados et al. Biomedical Signal Processing and Control
- A comparative study of different references for EEG default mode network: The use of the infinity reference
- (2010) Yun Qin et al. CLINICAL NEUROPHYSIOLOGY
- Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis
- (2010) Bogdan Mijović et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Electromyogenic artifacts and electroencephalographic inferences revisited
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- (2010) De Maarten Vos et al. NEUROINFORMATICS
- Automatic removal of eye movement artifacts from the EEG using ICA and the dipole model
- (2009) Weidong Zhou et al. Progress in Natural Science-Materials International
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