Semi-Automatic Artifact Rejection Procedure Based on Kurtosis, Renyi's Entropy and Independent Component Scalp Maps

Artifact rejection plays a key role in many signal processing applications. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Our aim is to automatically remove the artifacts, in particular from the Electroencephalographic (EEG) recordings. 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 try to enhance this technique proposing a new method based on the Renyi-s entropy. The performance of our method was tested and compared to the performance of the method in literature and the former proved to outperform the latter.

• Antonino Greco
• Nadia Mammone
• Francesco Carlo Morabito
• Mario Versaci

• Artifact
• EEG
• Renyi's entropy
• kurtosis
• independent component analysis.

[1] Cichocki A, Vorobyov S.A. (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] Jung T. P., Makeig S., Humphries C., Lee T. W., McKeown M. J., Iragui
V. and Sejnowski T. J. "Removing electroencephalographic artifacts by
blind source separation". Psychophysiology, 37(2):163-178, 2000.
[3] Delorme A., Makeig S., Sejnowski T. "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] Vorobyov S., Cichocki A., "Blind noise reduction for multisensory
signals using ICA and subspace filtering, with application to EEG
analysis", Biol. Cybern. 86, 293-303 (2002).
[5] Te-Won Lee. "Independent Component Analysis". Kluwer Academic
Publishers. 1998.
[6] Barbati G., Porcaro C., Zappasodi F., Rossini P. M., Tecchio F.,
"Optimization of an independent component analysis approach for
artifact identification and removal in magnetoencephalographic signals",
Clinical Neurophysiology 115 (2004) 1220-1232.
[7] Erdogmus D., Hild II K. E., Principe J. C. "Blind source separation
using Renyi-s marginal entropies." Neurocomputing 49 (2002) 25-38.
[8] Delorme A., Makeig S., "EEGLAB: An open source toolbox for analysis
of single-trial EEG dynamics including independent component
analysis.", Journal of Neuroscience Methods.
http://sccn.ucsd.edu/eeglab/download/eeglab_jnm03.pdf.