i-test is the MATLAB implementatation of the second-level (group-level) statistical test for the decoding accuracy proposed by Hirose (https://doi.org/10.1016/j.neuroimage.2021.118456), which is an extension of "Permutation-based prevalence inference using the minimum statistic", proposed by Allefeld et al., 2016
This repository include six subdirectories.
i-test toolbox
itest: includes MATLAB functions for the i-test.
implementation: Implementation-level functions
subfunction: simple subfunctions for input parser
Replication_of_study: The MATLAB codes for the replication of the study Hirose 2021.
Accessory tools
GUI: Simple GUI apps.
woid: i-test without identical distribution among participants (Under Development).
References
Hirose, Valid and powerful group statistics for decoding accuracy: Information Prevalence Inference using the i-th order statistic (i-test), https://doi.org/10.1016/j.neuroimage.2021.118456
Carsten Allefeld, Kai Görgen and John-Dylan Haynes, Valid population inference for information-based imaging: From the second-level t-test to prevalence inference, NeuroImage 2016, https://doi.org/10.1016/j.neuroimage.2016.07.040. https://github.com/allefeld/prevalence-permutation/).