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Fast implementation (~>100x faster than R/C++) of the ClusterDepth multiple comparison algorithm from Frossard and Renaud Neuroimage 2022

This is especially interesting to EEG signals. Currently only acts on a single channel/timeseries. Multichannel as discussed in the paper is the next step.


using ClusterDepth
pval_corrected = clusterdepth(erpMatrix; τ=2.3,nperm=5000)


FWER check

We checked FWER for troendle(...) and clusterdepth(...) (link to docs)

For clustedepth we used 5000 repetitions, 5000 permutations, 200 tests.

simulation noise uncorrected type
clusterdepth white 1.0 0.0554
clusterdepth red* 0.835 0.0394
troendle white XX XX
troendle red* XX XX

Uncorrected should be 1 - it is very improbable, than none of the 200 tests in one repetition is not significant (we expect 5% to be).

Corrected should be 0.05 (CI-95 [0.043,0.0564])

* red noise introduces strong correlations between individual trials, thus makes the tests correlated while following the H0.


Algorithm published in - Frossard & Renaud 2022, Neuroimage

Please also cite this toolbox: DOI

Some functions are inspired by R::permuco, written by Jaromil Frossard. Note: Permuco is GPL licensed, but Jaromil Frossard released the relevant clusteterdepth functions to me under MIT. Thereby, this repository can be licensed under MIT.