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Oscillatory processes are central for the understanding of the neural basesof cognition and behaviour. To analyse these processes, time-frequency (TF)decomposition methods are applied and non-parametric cluster-based statisticalprocedure are used for comparing two or more conditions. While this combinationis a powerful method, it has two drawbacks. One the unreliable estimation ofsignals outside the cone-of-influence and the second relates to the length ofthe time frequency window used for the analysis. Both impose constrains on thenon-parametric statistical procedure for inferring an effect in the TF domain.Here we extend the method to reliably infer oscillatory differences within thefull TF map and to test single conditions. We show that it can be applied insmall time windows irrespective of the cone-of-influence and we further developits application to single-condition case for testing the hypothesis of thepresence or not of time-varying signals. We present tests of this new method onreal EEG and behavioural data and show that its sensitivity to single-conditiontests is at least as good as classic Fourier analysis. Statistical inference inthe full TF map is available and efficient in detecting differences betweenconditions as well as the presence of time-varying signal in single condition.

Extension of the non-parametric cluster-based time-frequency statistics to the full time windows and to single condition tests