Multiscale Entropy Analysis of Artifactual EEG Recordings

Electroencephalographic (EEG) recordings are employed in order to investigate the brain activity in neuropathological subjects, but unfortunately EEG are often contaminated by artifacts, signals that have no-cerebral origin and therefore distort the EEG analysis. We know that entropy measures reflect the degree of order/disorder of the EEG signal, so that is represents a good instrument for artifacts detection. In this paper we propose a multiresolution analysis, based on EEG wavelet processing, to extract cerebral EEG rhythms. The novelty of this paper is to apply theWavelet Entropy method not only to Shannon Entropy formulation, but also to Rényi Entropy and Tsallis Entropy formulation in order to characterize the functional dynamics of EEG signal.