Abstract: 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.
Abstract: 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.