EEG artifact removal???state-of-the-art and guidelines, Journal of Neural Engineering, vol.12, issue.3, p.31001, 2015. ,
DOI : 10.1088/1741-2560/12/3/031001
Artifact Removal in Physiological Signals???Practices and Possibilities, IEEE Transactions on Information Technology in Biomedicine, vol.16, issue.3, pp.488-500, 2012. ,
DOI : 10.1109/TITB.2012.2188536
A Methodology for Validating Artifact Removal Techniques for Physiological Signals, IEEE Transactions on Information Technology in Biomedicine, vol.16, issue.5, pp.918-926, 2012. ,
DOI : 10.1109/TITB.2012.2207400
Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis, NeuroImage, vol.34, issue.4, pp.1443-1449, 2007. ,
DOI : 10.1016/j.neuroimage.2006.11.004
URL : https://hal.archives-ouvertes.fr/hal-00135628
Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study, Journal of Clinical Neurophysiology, vol.20, issue.4, pp.249-257, 2003. ,
DOI : 10.1097/00004691-200307000-00004
Removing Muscle Artifacts From EEG Data: Multichannel or Single-Channel Techniques?, IEEE Sensors Journal, vol.16, issue.7, pp.1986-1997, 2016. ,
DOI : 10.1109/JSEN.2015.2506982
Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram, IEEE Transactions on Biomedical Engineering, vol.53, issue.12, pp.2583-2587, 2006. ,
DOI : 10.1109/TBME.2006.879459
Online Removal of Muscle Artifact from Electroencephalogram Signals Based on Canonical Correlation Analysis, Clinical EEG and Neuroscience, vol.41, issue.1, pp.53-59, 2010. ,
DOI : 10.1016/S0898-1221(00)00101-2
A canonical correlation approach to exploratory data analysis in fMRI A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series, Proc. 10th Sci, pp.287-304, 2002. ,
The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique, IEEE Transactions on Biomedical Engineering, vol.60, issue.1, pp.97-105, 2013. ,
DOI : 10.1109/TBME.2012.2225427
Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data, IEEE Journal of Translational Engineering in Health and Medicine, vol.4, 2016. ,
DOI : 10.1109/JTEHM.2016.2544298
A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG, Sensors, vol.34, issue.10, pp.18370-18389, 2014. ,
DOI : 10.1016/j.neuroimage.2006.11.004
Wearable Electroencephalography, IEEE Engineering in Medicine and Biology Magazine, vol.29, issue.3, pp.44-56, 2010. ,
DOI : 10.1109/MEMB.2010.936545
URL : http://spiral.imperial.ac.uk/bitstream/10044/1/5910/1/final_paper.pdf
Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?, IEEE Journal of Biomedical and Health Informatics, vol.19, issue.1, pp.6-21, 2015. ,
DOI : 10.1109/JBHI.2014.2328317
Ten Lectures on Wavelets of CBMS-NSF Regional Conference Series in, Applied Mathematics, vol.61, 1992. ,
Compatibility of mother wavelet functions with the electroencephalographic signal, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, pp.113-117, 2012. ,
DOI : 10.1109/IECBES.2012.6498032
A wavelet based algorithm for the identification of oscillatory event-related potential components, Journal of Neuroscience Methods, vol.233, pp.63-72, 2014. ,
DOI : 10.1016/j.jneumeth.2014.06.004
Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association, vol.31, issue.432, pp.1200-1224, 1995. ,
DOI : 10.1007/978-3-0346-0416-1
URL : http://www-stat.stanford.edu/~donoho/Reports/1993/ausws.pdf
Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches, EURASIP Journal on Advances in Signal Processing, vol.38, issue.1, p.127, 2012. ,
DOI : 10.1016/j.neuroimage.2007.07.025
URL : https://hal.archives-ouvertes.fr/inserm-00727762
The removal of EMG in EEG by neural networks, Physiological Measurement, vol.31, issue.12, p.1567, 2010. ,
DOI : 10.1088/0967-3334/31/12/002
An Introduction to Bootstrap, 1993. ,
DOI : 10.1007/978-1-4899-4541-9
Constrained-realization Monte-Carlo method for hypothesis testing, Physica D: Nonlinear Phenomena, vol.94, issue.4, pp.221-235, 1996. ,
DOI : 10.1016/0167-2789(96)00050-4
URL : http://arxiv.org/pdf/comp-gas/9603001
Surrogate time series, Physica D: Nonlinear Phenomena, vol.142, issue.3-4, pp.346-382, 2000. ,
DOI : 10.1016/S0167-2789(00)00043-9
Testing Stationarity With Surrogates: A Time-Frequency Approach, IEEE Transactions on Signal Processing, vol.58, issue.7, pp.3459-3470, 2010. ,
DOI : 10.1109/TSP.2010.2043971
URL : https://hal.archives-ouvertes.fr/ensl-00475929
Improved Surrogate Data for Nonlinearity Tests, Physical Review Letters, vol.191, issue.4, p.635, 1996. ,
DOI : 10.1016/0375-9601(94)90134-1
Wavelet Methods for Time Series Analysis, 2006. ,
Wavelet Threshold Estimators for Data with Correlated Noise, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.2, pp.319-351, 1997. ,
DOI : 10.1111/1467-9868.00071
A Wavelet-Based Artifact Reduction From Scalp EEG for Epileptic Seizure Detection, IEEE Journal of Biomedical and Health Informatics, vol.20, issue.5, pp.1321-1332, 2016. ,
DOI : 10.1109/JBHI.2015.2457093
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.454, issue.1971, pp.903-995, 1971. ,
DOI : 10.1098/rspa.1998.0193
Empirical Mode Decomposition as a Filter Bank, IEEE Signal Processing Letters, vol.11, issue.2, pp.112-114, 2004. ,
DOI : 10.1109/LSP.2003.821662
URL : https://hal.archives-ouvertes.fr/inria-00570615
A complete ensemble empirical mode decomposition with adaptive noise, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4144-4147, 2011. ,
DOI : 10.1109/ICASSP.2011.5947265
Improved complete ensemble EMD: A suitable tool for biomedical signal processing, Biomedical Signal Processing and Control, vol.14, pp.19-29, 2014. ,
DOI : 10.1016/j.bspc.2014.06.009
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources, Neural Computation, vol.28, issue.46, pp.417-441, 1999. ,
DOI : 10.1109/72.536322
High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations, Frontiers in Human Neuroscience, vol.7, p.138, 2013. ,
DOI : 10.3389/fnhum.2013.00138
Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures, IEEE Signal Processing Magazine, vol.26, issue.1, pp.98-117, 2005. ,
DOI : 10.1109/MSP.2008.930649
Distributed Signal Processing for<newline/> Wireless EEG Sensor Networks, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.23, issue.6, pp.923-935, 2015. ,
DOI : 10.1109/TNSRE.2015.2418351
ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features, Psychophysiology, vol.16, issue.2, pp.229-240, 2011. ,
DOI : 10.1016/0301-0511(83)90059-5