Wavelet entropy: a new tool for analysis of short duration brain electrical signals

Since traditional electrical brain signal analysis is mostly qualitative, t he development of new quantitative methods is crucial for restricting the s ubjectivity in the study of brain signals. These methods are particularly f ruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics. Here, new method based on orthogonal discrete wavelet transform (ODWT) is applied. It takes as a basic element the ODWT of the EEG signal, and defines the relative wavelet energy, the wavelet entropy (WE) and the relative wavelet entropy (RWE). Th e relative wavelet energy provides information about the relative energy as sociated with different frequency bands present in the EEG and their corres ponding degree of importance. The WE carries information about the degree o f order/disorder associated with a multi-frequency signal response, and the RWE measures the degree of similarity between different segments of the si gnal. In addition, the time evolution of the WE is calculated to give infor mation about the dynamics in the EEG records. Within this framework, the ma jor objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG si gnals. For that aim, spontaneous EEG signals under different physiological conditions were analyzed. Further, specific quantifiers were derived to cha racterize how stimulus affects electrical events in terms of frequency sync hronization (tuning) in the event related potentials. (C) 2001 Published by Elsevier Science B.V.