Makarov , Valeri A. and Castellanos, Nazareth P. (2006) Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods , 158 (2). 300-312 . ISSN 0165-0270
Restricted to Repository staff only until 31 December 2020.
Independent component analysis (ICA) has been proven useful for suppression of artifacts in EEG recordings. It involves separation of measured signals into statistically independent components or sources, followed by rejection of those deemed artificial. We show that a "leak" of cerebral activity of interest into components marked as artificial means that one is going to lost that activity. To overcome this problem we propose a novel wavelet enhanced ICA method (wICA) that applies a wavelet thresholding not to the observed raw EEG but to the demixed independent components as an intermediate step. It allows recovering the neural activity present in "artificial" components. Employing semi-simulated and real EEG recordings we quantify the distortions of the cerebral part of EEGs introduced by the ICA and wICA artifact suppressions in the time and frequency domains. In the context of studying cortical circuitry we also evaluate spectral and partial spectral coherences over ICA/wICA-corrected EEGs. Our results suggest that ICA may lead to an underestimation of the neural power spectrum and to an overestimation of the coherence between different cortical sites. wICA artifact suppression preserves both spectral (amplitude) and coherence (phase) characteristics of the underlying neural activity. (c) 2006 Elsevier B.V. All rights reserved.
|Uncontrolled Keywords:||EEG recordings; Artifacts; Independent component analysis; Wavelet transform; Spectral coherence|
|Subjects:||Medical sciences > Biology > Neurosciences|
Alegre M, Labarga A, Gurtubay I, Iriarte J, Malanda A, Artieda J. Movementrelated changes in cortical oscillatory activity in ballistic, sustained and negative movements. Exp Brain Res 2003;148:17–25.
Amari S, Cichocki A, Yang H. A new learning algorithm for blind source separation. Adv Neural Inf Process Syst 1996;8:757–763.
Anemüller J, Sejnowski T, Makeig S. Complex independent component analysis of frequency—domain electroencephalographic data. Neural Networks 2003;16:1311–1323.
Bell A, Sejnowski T. An information-maximization approach to blind separation and blind deconvolution. Neural Comput 1995;7:1129–1159.
Bendat J, Piersol A. Random data: analysis and measurement procedures. New York: Wiley; 1986.
Brillinger D. Time series, data analysis and theory. San Francisco: Holden Day; 1981.
Brown G,Yamada S, Sejnowski T. Independent component analysis at the neural cocktail party. Trends Neurosci 2001;1:54–63.
Debnath L. Wavelet transforms and their applications. Birkhauser; 2002.
Delorme A, Jung C, Sejnowski T, Makeig S. Improved rejection of artefacts from EEG data using high order statistic and independent component analysis.
Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis. J Neurosci Meth 2004;134:9–21.
Donoho DL, Johnstone IM, Kerkyacharian G, Picardi D. Wavelet shrinkage: asymptopia?. J R Stat Soc 1995;B57:301–369.
Flexer A, Bauer H, Pripfl J, Dorffner G. Using ICA for removal of ocular artifacts in EEG recorded from blind subjects. Neural Networks 2005;18:998– 1005.
Friston K. Modes or models: a critique on independent component analysis for fMRI. Trends Cogn Sci 1998;2:373–375.
Goelz H, Jones R, Bones P. Wavelet analysis of transient biomedical signals and its application to detection of epileptiform activity in the EEG. Clin Electroenc 2000;31:181–191.
Hori H, Aiba M, He B. Spatio-temporal cortical source imaging of brain electrical activity by means of time-varying parametric projection filter. IEEE Trans Biomed Eng 2004;51:768–777.
Hyvärinen A, Pajunen P. Nonlinear independent component analysis: existence and uniqueness results. Neural Networks 1999;12:209–219.
Iriarte J, Urrestarazu E, Valencia M, Alegre M, Malanda A, Viteri C, et al. Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study. J Clin Neurophysiol 2003;20:249–257.
James C, Gibson O. Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Trans Biomed Eng 2003;50:1108–1116.
James C, Hesse C. Independent component analysis for biomedical signals. Physiol Meas 2005;26:15–39.
James CJ, Lowe D. Extracting multisource brain activity from single EM channel. Artif Intell Med 2003;28:89–104.
Jarvis M, Mitra P. Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput 2001;134:717–749.
Joyce C, Gorodnitsky I,Kutas M. Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 2004;41:313–325.
Jung T, Humphries C, Lee T, McKeown M, Iragui V, Makeig S, et al. Removing electroencephalographic artifacts by blind source separation. Psychophysiology 2000;37:163–178.
Jung T, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski T. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin Neurophysiol 2000;11:1745– 1758.
Kierkels J, Boxtel G,Vogten L.Amodel-bases objective evaluation of eyemovement correction in EEG recordings. IEEE Trans Biomed Eng 2006;53:246– 253.
Korzeniewska A, Manczak M, Kaminski M, Blinowska K, Kasicki S. Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J Neurosci Meth 2003;125:195– 207.
Lee T, Girolomi M, Sejnowski T. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput 1999;11:417–441.
Makinen V, May P, Tiitinen H. Spectral characterization of ongoin and auditory event-related brain process. Neurol Clin Neurophysiol 2004;3:104.
Mallat S. A wavelet tour of signal processing. San Diego: Academic Press; 1998.
McKeown M, Jung T, Makeig S, Brown G. Spatially independent activity patterns in functional magnetic resonance imaging data during the stroop color-maming task. Proc Natl Acad Sci USA 1998;3:803–810.
Melissant C, Ypma A, Frietman E, Stam C. A method for detection of alzheimer’s disease using ICA-enhanced EEG measurements. Artif Intell Med 2005;33:209–222.
Mochimaru F, Fujimoto Y, Ishikawa Y. The fetal electrocardiogram by independent component analysis and wavelet. Jpn J Physiol 2004;54:457– 463.
Mormann F, Fell J, Axmancher N, Weber B, Lehnertz K, Elger C, et al. Phase/amplitude reset and theta-gamma interaction in the human medial temporal lobe during a continous word recognition memory task. Hippocampus 2005;15:890–900.
Murata A. An attempt to evaluate mental workload using wavelet transform of EEG. Hum Factors 2005;47:498–508.
Percival D, Walden AT. Spectral analysis for physical applications. Cambridge University Press; 1993.
Privik R, Broughton R, Coppola R, Davidson R, FoxN, Nuwer M. Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology 1993;30:547–558.
Quiroga R, Garcia H. Single-trial event-related potentials with wavelet denoising. Clin Neurophysiol 2003;114:376–390.
Rodriguez E, George N, Lachaux JP, Martinerie J, Renault B, Varela F. Perception’s shadow: long-distance synchronization of human brain activity. Nature 1999;397:430–433.
Rong-YiY, Zhong C. Blind source separation of multichannel electroencephalogram based wavelet transform and ICA. Chinese Phys 2005;14:2176–2180.
Schiff S, Aldroubi A, Unser M, Sato S. Fast wavelet transformation of EEG. Electroencephal Clin Neurophysiol 1994;91:442–455.
Schreiber T, Schmitz A. Surrogate time series. Physica D 2000;142:646–652.
Stone JV. Independent component analysis: an introduction. Trends Cogn Sci 2002;6:59–64.
Theiler J, Eubank S, longtin A, Galdrikian B, Farmer D. Testing for nonlinearity in time series: the method of surrogate data. Physica D 1992;58:77–94.
Thomson D. Spectrum estimation and harmonic analysis. Proc IEEE 1982;70: 1055–1096.
Tong S, Bezerianos A, Paul J, Zhu Y, Thakor N. Removal of ECG interference from the EEG recordings in small animals using independent component analysis. J Neurosci Meth 2001;108:11–17.
Tran Y, Craig A, Boord P, Craig D. Using independent component analysis to remove artifact from electroencephalographic measured during stuttered speech. Med Biol Eng Comput 2004;42:627–633.
Urrestarazu E, Iriarte J, Alegre M, Valencia M, Viteri C, Artieda J. Independent component analysis removing artifacts in ictal recordings. Epilepsia 2004;45:1–8.
Varela F, Lachaux JP, Rodriguez E, Martinerie J. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2001;2:229–238.
Vigario R. Extraction of ocular artifats from EEG using independent component analysis. Electroencephal Clin Neurophysiol 1997;103:395–404.
Vigario R, Särelö J, Jousmäki V, Hämäläinem H, Oja E. Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans Biomed Eng 2000;47:589–593.
von Stein A, Rappelsberger P, Sarnthein J, Petsche H. Synchronization between temporal and parietal cortex during multimodal object processing in man. Cerebral Cortex 1999;9:137–150.
Wallstrom G, Kass R, Miller A, Cohn J, Fox N. Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. Int J Psychophysiol 2004;53:105–119.
Wan X, Iwata K, Riera J, Ozaki T, Kitamura M, Kawashima R. Artifact reduction for EEG/fMRI recordings: nonlinear reduction of ballistocardiogram artifacts. Clin Neurophysiol 2006;117:1–13.
|Deposited On:||10 Oct 2012 08:15|
|Last Modified:||07 Feb 2014 09:33|
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