Objective:Our goal is to use existing and to proposenew time-frequency entropy measures that objectively evalu-ate the improvement on epileptic patients after medication bystudying their resting state EEG recordings. An increase inthe complexity of the signals would confirm an improvementin the general state of the patient.Methods:We review theR ́enyi entropy based on time-frequency representations, alongwith its time-varying version. We also discuss the entropy basedon singular value decomposition computed from a time-frequencyrepresentation, and introduce its corresponding time-dependantversion. We test these quantities on synthetic data. Friedmantests are used to confirm the differences between signals (beforeand after proper medication). Principal component analysis isused for dimensional reduction prior to a simple thresholddiscrimination.Results:Experimental results show a consistent increase in complexity measures in the different regions of thebrain. These findings suggest that extracted features can beused to monitor treatment. When combined, they are useful for classification purposes, with areas under ROC curves higherthan 0.93 in some regions.Conclusion:Here we applied time-frequency complexity measures to resting state EEG signals fromepileptic patients for the first time. We also introduced a newtime-varying complexity measure. We showed that these features are able to evaluate the treatment of the patient, and to perform classification.Significance: The time-frequency complexities, and their time-varying versions, can be used to monitor the treatment of epileptic patients. They could be applied to a wider range of problems.