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

TL;DR

This paper extends non-parametric cluster-based time-frequency statistics to analyze full TF maps and single conditions, improving reliability and sensitivity in EEG data analysis.

Contribution

It introduces a method that reliably infers oscillatory differences across the entire TF map and tests single conditions, overcoming previous limitations.

Findings

Effective in small time windows regardless of cone-of-influence

Comparable sensitivity to classic Fourier analysis for single-condition detection

Efficiently detects differences between conditions and time-varying signals

Abstract

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