#MATLAB RANDOM PERMUTE MATRIX HOW TO#
#MATLAB RANDOM PERMUTE MATRIX CODE#

Structure in experiment and data Different types of data However, in this tutorial the focus will be on non-parametric testing. Besides nonparametric statistical tests, ft_timelockstatistics and ft_freqstatistics can also perform parametric statistical tests (see the Parametric and non-parametric statistics on event related fields tutorial). These are the functions ft_timelockstatistics and ft_freqstatistics which compute significance probabilities using a non-parametric permutation tests.

FieldTrip contains a set of statistical functions that provide a solution for the MCP. Contrary to the familiar parametric statistical framework, it is straightforward to solve the MCP in the nonparametric framework. In this tutorial, we describe nonparametric statistical testing of MEEG-data.

The FWER is also called the false alarm rate. A solution of the MCP requires a procedure that controls the FWER at some critical alpha-level (typically, 0.05 or 0.01). The FWER is the probability under the hypothesis of no effect of falsely concluding that there is a difference between the experimental conditions at one or more (channel,time)-pairs. Now, the MCP involves that, due to the large number of statistical comparisons (one per (channel,time)-pair), it is not possible to control the so called family-wise error rate (FWER) by means of the standard statistical procedures that operate at the level of single (channel,time)-pairs. This number is usually in the order of several thousands. The MCP arises from the fact that the effect of interest (i.e., a difference between experimental conditions) is evaluated at an extremely large number of (channel,time)-pairs. MEEG-data have a spatiotemporal structure: the signal is sampled at multiple channels and multiple time points (as determined by the sampling frequency). This problem originates from the fact that MEEG-data are multidimensional. In the statistical analysis of MEEG-data we have to deal with the multiple comparisons problem (MCP). Background The multiple comparisons problem This tutorial contains hands-on material that we use for the MEG/EEG toolkit course and it is complemented by this lecture. If you are interested how parametric statistical tests can be used with FieldTrip, you can read the Parametric and non-parametric statistics on event-related fields tutorial. If you are interested in that, you can read the Cluster-based permutation tests on time-frequency data tutorial. This tutorial is not covering statistical test on time-frequency representations. We assume that the preprocessing and averaging steps of the analysis are already clear for the reader.

#MATLAB RANDOM PERMUTE MATRIX CODE#

Below we will repeat code to select the trials and preprocess the data as described in the first tutorials ( trigger based trial selection, artifact rejection, event related averaging and planar gradient). In this tutorial we will continue working on the dataset of a single subject described in the trigger-based trial selection preprocessing tutorial.

#MATLAB RANDOM PERMUTE MATRIX HOW TO#

Subsequently it is shown how to use FieldTrip to perform cluster-based permutation tests on actual axial and planar event-related fields in a between-trials (using single-subject data) and in a within-subjects design (using data from multiple subjects). The tutorial starts with a long background section that sketches the background of permutation tests. The objective of this tutorial is to give an introduction to the statistical analysis of event-related EEG and MEG data (denoted as M/EEG data in the following) by means of cluster-based permutation tests. Tutorial statistics eeg meg timelock plotting meg-language neighbours Cluster-based permutation tests on event related fields Introduction

Matlab random permute matrix