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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059954Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1489
 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.
 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.
 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.
 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).
 Te-Won Lee. "Independent Component Analysis". Kluwer Academic Publishers. 1998.
 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.
 Erdogmus D., Hild II K. E., Principe J. C. "Blind source separation using Renyi-s marginal entropies." Neurocomputing 49 (2002) 25-38.
 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.