Optimal time frequency analysis of multiple time-translated locally stationary processes

A previously proposed model for non-stationary signals is extended in this contribution. The model consists of multiple time-translated locally stationary processes. The optimal Ambiguity kernel for the process in mean-square-error sense is computed analytically and is used to estimate the time-frequency distribution. The performance of the kernel is compared with other commonly used kernels. Finally the model is applied to electrical signals from the brain (EEG) measured during a concentration task.