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freq_run_baselinehelper.m
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%% EEG pattern glare analysis - Author: Tom Jefferis
%% This file is used for running all the statistics for onsets, partitions and onsets 2,3vs4,5,vs 6,7
%% setting up paths to plugins and default folder
clear all;
restoredefaultpath;
addpath('./methods');
addpath('C:\Users\Tom\AppData\Roaming\MathWorks\MATLAB Add-Ons\Collections\MIMT\MIMT')
grand_avg_filename = 'time_domain_mean_intercept_onsets_2_3_4_5_6_7_8_grand-average.mat'; % file name within folder that has participant data
single_trial_filename = 'time_domain_mean_intercept_onsets_2_3_4_5_6_7_8_trial-level.mat'; % file name for single trial data
grand_avg_partitions_filename = 'time_domain_partitions_partitioned_onsets_2_3_4_5_6_7_8_grand-average.mat'; % file name within folder that has participant data
single_trial_partitions_filename = 'time_domain_partitions_partitioned_onsets_2_3_4_5_6_7_8_trial-level.mat'; % file name for single trial data
grand_avg_freq_filename = 'frequency_domain_mean_intercept_onsets_2_3_4_5_6_7_8_grand-average.mat'; % file name within folder that has participant data
single_trial_freq_filename = 'frequency_domain_mean_intercept_onsets_2_3_4_5_6_7_8_trial-level.mat'; % file name for single trial data
grand_avg_freq_partitions_filename = 'frequency_domain_partitions_partitioned_onsets_2_3_4_5_6_7_8_grand-average.mat'; % file name within folder that has participant data
single_trial_freq_partitions_filename = 'frequency_domain_partitions_partitioned_onsets_2_3_4_5_6_7_8_trial-level.mat'; % file name for single trial data
grand_avg_eyes = 'time_domain_eye_confound_onsets_2_3_4_5_6_7_8_grand-average.mat';
%% Experiment parameters
% ROI window
%time_window = [3.0 3.99];
%time_window = [3.09, 3.18; 3.18, 3.45; 3.45, 3.83; 3.08, 3.99;];
time_window = [0.5 3];
n_participants = 40;
baseline_period = [-0.2 -0.05];
%baseline_period = [2.75 2.95];
aggregated_roi = false; % uses aggregated average approach to find the ROI
max_windows = 4; % maximum amount of windows the roi finder finds
spatial_roi = false; % generate a spatial region of interest - not useful for most of these analysis ----- NEED TO FIX FOR FREQ
posneg = false; %true = positive side of roi false = negative
%% Time Domain config
% if need statistical test without plotting
stat_run = true;
%% frequency config
wavelet_width = 4; % mostly obselite now moving to time based calculation of width
frequency_ranges = {[8,12],[20, 35],[30,45],[40,60],[60,80]}; % start and end frequency for stat tests
power_itc = 'pow'; %looking at power ot itc options: pow, itc
decimate = 250; % this is the frequency domain resampling for memory efficiancy, this should be calculated using nyquist
%% Plotting config
clust_volume = false; % cluter volume over time
topograpic_map_plot = false; % make topographic maps
plot_erps = false; % plotting ERPs for each condition and PGI
median_split_plots = false; % plots the median split across time window for factors
tfr_plots = false; % frequency spectrum plots
gfp_plot = false; % plots GFP as well as GFP with windows of analysis. Only generated when aggregated_roi set to true
plot_designs = false; %plots design matrix for partitions ONLY
plot_partitions_erps = false; % 10x2 figure of median split partitions for factor
generate_ci = true; % do we want confidence intervals !!BREAKS MEDIAN SPLIT PLOTS AND PARTITION SPLIT IF FALSE!!
%% generate experiment dsign
time_freq = 'frequency'; % time or frequency domain options: time or frequency
factor_scores = {'all'}; % options: none, headache, visual-stress, discomfort, all
onsets_parts = {'onsets','partitions','onsets-23-45-67'}; % options: onsets, partitions, onsets-23-45-67, eyes, partition1, partitions-vs-onsets
type_of_effects = {'habituation','sensitization'}; % habituation or sensitization
three_way_type = {'sensitization'}; % same as previous but only used when making the 3 way comparison
partitionss = {'orthog'}; % orthogonolize design matrix for partitions (zero center), options: normal, orthog
%% disable this when wanting to run for real results
testing = true;
paper_figs = true;
%% End of config
%% parfor loop running pgi analysis for all onsets_parts
if contains(time_freq, "time")
for i = 1:numel(onsets_parts)
onsets_part = onsets_parts{i};
for m = 1:numel(partitionss)
partitions = partitionss{m};
if strcmp(onsets_part,'onsets')
b = 1;
else
b = numel(type_of_effects);
end
for j = 1:b
type_of_effect = type_of_effects(j);
tab = pgi_analysis(grand_avg_filename, single_trial_filename, grand_avg_partitions_filename, single_trial_freq_partitions_filename, time_window, n_participants, baseline_period, ...
aggregated_roi, max_windows, spatial_roi, posneg, stat_run, wavelet_width, frequency_ranges{1},decimate, power_itc, tfr_plots, clust_volume, topograpic_map_plot, ...
plot_erps, median_split_plots, gfp_plot, plot_designs, plot_partitions_erps, generate_ci, time_freq, factor_scores, ...
onsets_part, type_of_effect, three_way_type, partitions, testing, paper_figs);
end
end
end
else
for i = 1:numel(onsets_parts)
onsets_part = onsets_parts{i};
for k = 1:numel(frequency_ranges)
frequency_range = frequency_ranges{k};
avgfreq = (frequency_range(1)+frequency_range(2))/2;
duration = 1000/avgfreq;
wavelet_width = round(200 / duration);
if duration * wavelet_width > 200
wavelet_width = wavelet_width - 1;
end
for m = 1:numel(partitionss)
partitions = partitionss{m};
if strcmp(onsets_part,'onsets')
b = 1;
else
b = numel(type_of_effects);
end
for j = 1:b
type_of_effect = type_of_effects(j);
tab = pgi_analysis(grand_avg_filename, single_trial_freq_filename, grand_avg_partitions_filename, single_trial_freq_partitions_filename, time_window, n_participants, baseline_period, ...
aggregated_roi, max_windows, spatial_roi, posneg, stat_run, wavelet_width, frequency_range, decimate, power_itc, tfr_plots, clust_volume, topograpic_map_plot, ...
plot_erps, median_split_plots, gfp_plot, plot_designs, plot_partitions_erps, generate_ci, time_freq, factor_scores, ...
onsets_part, type_of_effect, three_way_type, partitions, testing, paper_figs);
end
end
end
end
end
%% Experiment parameters
% ROI window
%time_window = [3.0 3.99];
%time_window = [3.09, 3.18; 3.18, 3.45; 3.45, 3.83; 3.08, 3.99;];
time_window = [0.5 3];
n_participants = 40;
baseline_period = [-0.2 -0.05];
%baseline_period = [2.75 2.95];
aggregated_roi = false; % uses aggregated average approach to find the ROI
max_windows = 4; % maximum amount of windows the roi finder finds
spatial_roi = false; % generate a spatial region of interest - not useful for most of these analysis ----- NEED TO FIX FOR FREQ
posneg = false; %true = positive side of roi false = negative
%% Time Domain config
% if need statistical test without plotting
stat_run = true;
%% frequency config
wavelet_width = 4; % mostly obselite now moving to time based calculation of width
frequency_ranges = {[8,12],[20, 35],[30,45],[40,60],[60,80]}; % start and end frequency for stat tests
power_itc = 'pow'; %looking at power ot itc options: pow, itc
decimate = 250; % this is the frequency domain resampling for memory efficiancy, this should be calculated using nyquist
%% Plotting config
clust_volume = false; % cluter volume over time
topograpic_map_plot = false; % make topographic maps
plot_erps = false; % plotting ERPs for each condition and PGI
median_split_plots = false; % plots the median split across time window for factors
tfr_plots = false; % frequency spectrum plots
gfp_plot = false; % plots GFP as well as GFP with windows of analysis. Only generated when aggregated_roi set to true
plot_designs = false; %plots design matrix for partitions ONLY
plot_partitions_erps = false; % 10x2 figure of median split partitions for factor
generate_ci = true; % do we want confidence intervals !!BREAKS MEDIAN SPLIT PLOTS AND PARTITION SPLIT IF FALSE!!
%% generate experiment dsign
time_freq = 'frequency'; % time or frequency domain options: time or frequency
factor_scores = {'none'}; % options: none, headache, visual-stress, discomfort, all
onsets_parts = {'onsets','partitions','onsets-23-45-67'}; % options: onsets, partitions, onsets-23-45-67, eyes, partition1, partitions-vs-onsets
type_of_effects = {'habituation','sensitization'}; % habituation or sensitization
three_way_type = {'sensitization'}; % same as previous but only used when making the 3 way comparison
partitionss = {'normal'}; % orthogonolize design matrix for partitions (zero center), options: normal, orthog
%% disable this when wanting to run for real results
testing = true;
paper_figs = true;
%% End of config
%% parfor loop running pgi analysis for all onsets_parts
if contains(time_freq, "time")
for i = 1:numel(onsets_parts)
onsets_part = onsets_parts{i};
for m = 1:numel(partitionss)
partitions = partitionss{m};
if strcmp(onsets_part,'onsets')
b = 1;
else
b = numel(type_of_effects);
end
for j = 1:b
type_of_effect = type_of_effects(j);
tab = pgi_analysis(grand_avg_filename, single_trial_filename, grand_avg_partitions_filename, single_trial_freq_partitions_filename, time_window, n_participants, baseline_period, ...
aggregated_roi, max_windows, spatial_roi, posneg, stat_run, wavelet_width, frequency_ranges{1},decimate, power_itc, tfr_plots, clust_volume, topograpic_map_plot, ...
plot_erps, median_split_plots, gfp_plot, plot_designs, plot_partitions_erps, generate_ci, time_freq, factor_scores, ...
onsets_part, type_of_effect, three_way_type, partitions, testing, paper_figs);
end
end
end
else
for i = 1:numel(onsets_parts)
onsets_part = onsets_parts{i};
for k = 1:numel(frequency_ranges)
frequency_range = frequency_ranges{k};
avgfreq = (frequency_range(1)+frequency_range(2))/2;
duration = 1000/avgfreq;
wavelet_width = round(200 / duration);
if duration * wavelet_width > 200
wavelet_width = wavelet_width - 1;
end
for m = 1:numel(partitionss)
partitions = partitionss{m};
if strcmp(onsets_part,'onsets')
b = 1;
else
b = numel(type_of_effects);
end
for j = 1:b
type_of_effect = type_of_effects(j);
tab = pgi_analysis(grand_avg_filename, single_trial_freq_filename, grand_avg_partitions_filename, single_trial_freq_partitions_filename, time_window, n_participants, baseline_period, ...
aggregated_roi, max_windows, spatial_roi, posneg, stat_run, wavelet_width, frequency_range, decimate, power_itc, tfr_plots, clust_volume, topograpic_map_plot, ...
plot_erps, median_split_plots, gfp_plot, plot_designs, plot_partitions_erps, generate_ci, time_freq, factor_scores, ...
onsets_part, type_of_effect, three_way_type, partitions, testing, paper_figs);
end
end
end
end
end