[FieldTrip] Cluster-based permutation statistics using ft_timelockstatistics assistance
Merlin Kelly
merlinkelly75 at gmail.com
Tue Sep 5 18:31:53 CEST 2023
Hello
I am currently trying to use the command ft_timelockstatistics to replicate
the method of data analysis as demonstrated in the paper: "EEG alphaâ€“theta
dynamics during mind wandering in the context of breath focus meditation:
An experience sampling approach with novice meditation practitioners". They
also have used fieldtrip for permutation data analysis, but the code they
provide doesn't seem to function for the alpha:theta ratio data.
I have recorded my data in a within-subject study with a baseline and two
independent variables. I have my data separated between these three classes
in a 4D array with dimensions, subject x trial/epoch x EEG channel x ratio
data. Below is the code on how I convert this data to be fieldtrip friendly:
function [DATA, layout]=permutation(data, Participant_index, index)
load('electrode19.mat')
load('chanlocs.mat')
DATA = cell(1, max(unique(Participant_index)));
%for each participant, load data in struct form and prepare for
%fieldtrip
for i=1:max(unique(Participant_index))
clear Data
Data=struct();
Data.label=electrode19;
Data.hdr.nChans=numel(electrode19);
Data.hdr.label=electrode19';
Data.hdr.Fs=500;
Data.fsample=500;
data_sel = data(Participant_index(index) == i, :,:,:);
if size(data_sel, 1) ~= 1
data_sel = reshape(data_sel, [], size(data_sel, 3), size(data_sel, 4));
else
data_sel = squeeze(data_sel);
end
Data.trial = data_sel;
Data.time=1:0.1:3.5;
Data.hdr.nSamples=1;
Data.dimord = 'chan_time';
try
cfg=[];
Data=ft_preprocessing(cfg, Data);
Data = ft_timelockanalysis(cfg, Data);
Data = ft_datatype_timelock(Data);
% Data.trialinfo=labels;
cfg.layout = 'biosemi32.lay';
Data.layout = ft_prepare_layout(cfg);
layout = ft_prepare_layout(cfg);
Data.layout=Data;
DATA{i} = Data;
catch
DATA{i} = NaN;
end
end
end
and below is the code for which I use this converted data and apply it to
the command ft_timelockstatistics:
clear cls
%Loads the directory names and the processed data
dirs = dir('C:/Users/merli/OneDrive - University College
London/Desktop/Study_!/P*/*.mat');
load('C:\Users\merli\OneDrive - University College
London\Desktop\Study_!\Data.mat');
%For loop and unique command seperates each class and participant so we can
extract an index
for i=1:size(dirs,1)
class = split(dirs(i).name, '_');
Par = split(dirs(i).folder, 'P');
cls{i} = class{1};
P{i} = Par{2};
end
[uniqueStrings, ~, index] = unique(cls);
[PuniqueStrings, ~, Pindex] = unique(P);
%Seperate data to each class based on the previous index
%percentage ratio is a 4D array with dimensions subject x trial/epoch x
%channel x frequency data
%Testing error by removing incomplete sessions
% x = percentage_ratio(3:58,:,:,:);
% size(x)
% index = index(3:58);
% Pindex = Pindex(3:58);
BaselineData = percentage_ratio(index == 1, :, :, :);
FrameData = percentage_ratio(index == 2, :, :, :);
ShamData = percentage_ratio(index == 3, :, :, :);
% Pindex(index == 1) == 1
%Converts 4D array to timelock data for fieldtrip analysis
[BDataP, BlayoutP] = permutation(BaselineData, Pindex, index == 1);
[FDataP, FlayoutP] = permutation(FrameData, Pindex, index == 2);
[SDataP, SlayoutP] = permutation(ShamData, Pindex, index == 3);
% figure,plot(1:0.1:3.5, BDataP{14}.avg)
% figure,plot(1:0.1:3.5, FDataP{14}.avg)
% figure,plot(1:0.1:3.5, SDataP{14}.avg)
%configuration definition for permutation statistics
cfg=[];
cfg.method = 'triangulation';
cfg.layout = BlayoutP;
neighbours = ft_prepare_neighbours(cfg);
cfg.method = 'montecarlo'; % use the Monte Carlo Method to calculate the
significance probability
cfg.statistic = 'ft_statfun_depsamplesT';% use dependent samples t statistic
cfg.correctm = 'cluster';
cfg.clusteralpha = 0.05; % alpha level of the sample-specific test
statistic that will be used for thresholding
cfg.clusterstatistic = 'maxsum'; % test statistic that will be evaluated
under the permutation distribution.
cfg.neighbours = neighbours; % definition of neighbours
cfg.tail = 0; %two-sided test
cfg.clustertail = 0;
cfg.alpha = 0.025; % alpha level of the permutation test
cfg.numrandomization = 1000; % number of draws from the permutation
distribution
%define design matrix
Bsubj=size(BDataP,2);
Ssubj=size(SDataP,2);
Fsubj=size(FDataP,2);
%Currently instead of using all participants, am just doing it for some
%as it'll cause errors when using everyones data
cfg.design(1,:) = ([2:8 2:8]); %Each number represents the time it happened
cfg.design(2, :) = ([ones(1,7)*1 ones(1,7)*2]); %Each number is the
different independant variable
cfg.uvar = 1; % row of design matrix that contains unit variable (in this
case: subjects)
cfg.ivar = 2; % row of design matrix that contains independent variable
(the conditions)
[stat] = ft_timelockstatistics(cfg, SDataP{2:8}, FDataP{2:8});
This code is currently functioning, but not as intended. The variable
stat.stat is giving a channel x len(ratio data) array
which I assume is supposed to be the probability between both conditions,
but all values are 1, except two random
values that equal 0.23. I don't understand why I'm reciving these results
as I assume it's impossible to recieve a
probability of 1 unless the data is exactly the same for each observation.
Additionally, when I run the code it states:
Warning: the number of randomizations (1000) is larger than the maximum
number of unique
permutations (128), better use cfg.numrandomization='all'
Even though I have 601 different trials/epochs for each class per subject.
Can anyone identify if I have made any mistakes or point me in the
direction required to analyse my data correctly. Any help would be
appreciated.
Best
Merlin
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