Abstract
Blind Source Separation (BSS) approaches for multi-channel EEG processing are popular, and in particular Independent Component Analysis (ICA) algorithms have proven their ability for artefacts removal and source extraction for this very specific class of signals. However, the blind aspect of these techniques implies well-known drawbacks. As these methods are based on estimated statistics from the data and rely on an hypothesis of signal stationarity, the length of the window is crucial and has to be chosen carefully: large enough to get reliable estimation and short enough to respect the rather non-stationary nature of the EEG signals. In addition, another issue concerns the plausibility of the resulting separated sources. Indeed, some authors suggested that ICA algorithms give more physiologically plausible results than others. In this paper, we address both issues by comparing four popular ICA algorithms (namely FastICA, Extended InfoMax, JADER and AMICA). First of all, we propose a new criterion aiming to evaluate the quality of the decorrelation step of the ICA algorithms. This criterion leads to a heuristic rule of minimal sample size that guarantees statistically robust results. Next, we show that for this minimal sample size ensuring constant decorrelation quality we obtain quasi-constant ICA performances for some but not all tested algorithms. Extensive tests have been performed on simulated data (i.i.d. sub and super Gaussian sources mixed by random mixing matrices) and plausible data (macroscopic neural population models placed inside a three layers spherical head model). The results globally confirm the proposed rule for minimal data length and show that the use of sphering as decorrelation step might significantly change the global performances for some algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schomer, D., Lopes da Silva, F. (eds.): Niedermeyers’s Electroenephalography: Basic Principles, Clinical Applications and Related Fields. Wolters Kluwer, Lippincott Willimas & Wilkins (2011)
Sanei, S., Chambers, J.: EEG Signal Processing. John Wiley & Sons (2007)
Scherg, M., Berg, P.: Use of prior knowledge in brain electromagnetic source analysis. Brain Topography 4, 143–150 (1991)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing Learning Algorithms and Applications. John Wiley & Sons, New York (2002)
Delorme, A., Palmer, J., Onton, J., Oostenveld, R., Makeig, S.: Independent eeg sources are dipolar. PLoS ONE 7(2), e30135 (2012)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Palmer, J., Makeig, S., Delgado, K., Rao, B.: Newton method for the ICA mixture model. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, March 31-April 4, pp. 1805–1808 (2008)
Cardoso, J.: High-order contrasts for independent component analysis. Neural Computation 11(1), 157–192 (1999)
Särelä, J., Vigário, R.: Overlearning in marginal distribution-based ICA: analysis and solutions. J. Mach. Learn. Res. 4, 1447–1469 (2003)
Onton, J., Makeig, S.: Information-based modeling of event-related brain dynamics. Progress in Brain Research 159, 99–120 (2006)
Delorme, A., Makeig, S.: EEGLab: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134(1), 9–21 (2004)
Palmer, J., Makeig, S., Delorme, A., Onton, J., Acar, Z.A., Kreutz-Delgado, K., Rao, B.D.: Independent Component Analysis of High-density Scalp EEG Recordings. In: 10th EEGLAB Workshop, Jyväskylä, Finland, June 14-17 (2010)
Vaseghi, S., Jetelova, H.: Principal and independent component aanalysis in image processing (2008)
Wu, Y., Wu, B., Liu, J., Lu, H.: Probabilistic tracking on riemannian manifolds. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (December 2008)
Korats, G., Le-Cam, S., Ranta, R.: Impact of window length and decorrelation step on ICA algorithms for EEG blind source separation. In: Biosignals/Biostec INSTICC Annual Conference (2012)
Wendling, F., Bartolomei, F., Bellanger, J.J., Chauvel, P.: Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. European Journal of Neuroscience 15(9), 1499–1508 (2002)
Berg, P., Scherg, M.: A fast method for forward computation of multiple-shell spherical head models. Electroencephalography and Clinical Neurophysiology 90(1), 58–64 (1994)
Lopes da Silva, F.H., Hoeks, A., Smits, H., Zetterberg, L.H.: Model of brain rhythmic activity. Biological Cybernetics 15, 27–37 (1974)
Jansen, B., Rit, V.: Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics 73, 357–366 (1995)
Wendling, F., Hernandez, A., Bellanger, J.J., Chauvel, P., Bartolomei, F.: Interictal to ictal transition in human temporal lobe epilepsy: Insights from a computational model of intracerebral EEG. Clinical Neurophysiology 22(2), 343–356 (2005)
Hallez, H., Vanrumste, B., Grech, R., Muscat, J., De Clercq, W., Vergult, A., D’Asseler, Y., Camilleri, K., Fabri, S., Van Huffel, S., Lemahieu, I.: Review on solving the forward problem in EEG source analysis. J. Neuroeng. Rehabil. 4, 46 (2007)
Ma, J., Gao, D., Ge, F., Amari, S.-I.: A One-Bit-Matching Learning Algorithm for Independent Component Analysis. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 173–180. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Korats, G., Le Cam, S., Ranta, R., Hamid, M. (2013). Applying ICA in EEG: Choice of the Window Length and of the Decorrelation Method. In: Gabriel, J., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2012. Communications in Computer and Information Science, vol 357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38256-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-38256-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38255-0
Online ISBN: 978-3-642-38256-7
eBook Packages: Computer ScienceComputer Science (R0)