Information processing on the time-frequency plane

Time-frequency analysis is a major tool in representing the energy distribution of time-varying signals. There has been much research on various properties of these representations. However, there is a general lack of quantitative analysis in describing the amount of information encoded into a time-frequency distribution. Recently, entropy based measures have been applied to the time-frequency plane to quantify the information content of signals. The paper aims to extend this approach to include other information theoretic measures, such as divergence measures, to quantify how time-frequency distributions discriminate signals in an information theoretic framework. Different distance measures, such as Kullback-Leibler distance, Renyi distance, and Jensen difference based measures, are adapted to the time-frequency plane. The robustness of different distance measures under an additive perturbation model is derived. The performance of different distance measures in quantifying the differences in information between signals is demonstrated. Finally, the proposed distance measures are applied to a set of event related brain potentials to discriminate different subject groups.