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Kurtosis, Renyi's Entropy and Independent Component Scalp Maps for the Automatic Artifact Rejection from EEG Data

Authors: Antonino Greco, Nadia Mammone, Francesco Carlo Morabito, Mario Versaci


The goal of this work is to improve the efficiency and the reliability of the automatic artifact rejection, in particular from the Electroencephalographic (EEG) recordings. Artifact rejection is a key topic in signal processing. The artifacts are unwelcome signals that may occur during the signal acquisition and that may alter the analysis of the signals themselves. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we enhance this technique introducing the Renyi-s entropy. The performance of our method was tested exploiting the Independent Component scalp maps and it was compared to the performance of the method in literature and it showed to outperform it.

Keywords: eeg, kurtosis, independent component analysis, Artifact, Renyi's entropy

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[1] A. Cichocki, S. A. Vorobyov (2000), "Application of ICA for automatic noise and interference cancellation in multisensory biomedical signals", Proceedings of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, Helsinki, Finland, June 19-22, pp, 621-626.
[2] T. P. Jung, S. Makeig, C. Humphries, T.-W. Lee, M. J. McKeown, V. Iragui and T. J. Sejnowski, "Removing electroencephalographic artifacts by blind source separation". Psychophysiology, 37(2):163-178, 2000.
[3] A. Delorme, S. Makeig, T. Sejnowski, "Automatic artifact rejection for EEG data using high-order statistics and independent component analysis". Proceedings of the 3rd International Workshop on ICA, San Diego, December. 2001. p. 457-62.
[4] S. Vorobyov, A. Cichocki, "Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis", Biol. Cybern. 86, 293-303 (2002).
[5] T.-W. Lee. "Independent Component Analysis". Kluwer Academic Publishers, 1998.
[6] G. Barbati, C. Porcaro, F. Zappasodi, P. M. Rossini, F. Tecchio, "Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals", Clinical Neurophysiology 115 (2004) 1220-1232.
[7] D. Erdogmus, K. E. Hild II, J. C. Principe, "Blind source separation using Renyi-s marginal entropies." Neurocomputing 49 (2002) 25-38.
[8] A. Delorme, S. Makeig, "EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis." Journal of Neuroscience Methods.