Information-theoretic signal processing on the time-frequency plane and applications

Time-frequency analysis is a major tool in representing the energy distribution of time-varying signals. There has been a lot of research on various properties of these representations. However, there is a general lack of quantitative measures in describing the amount of information encoded into a time-frequency distribution. Recently, information-theoretic measures such as entropy and divergence have been adapted to the time-frequency plane to quantify the complexity of individual signals as well as the difference between signals. In this paper, we present a variety of information-theoretic measures and their definitions on the time-frequency plane. The properties of these measures and how they can be applied to signal classification problems are discussed in detail. We then present an application of information-theoretic signal processing to the analysis of event- related brain potentials.