forked from fieldtrip/fieldtrip
-
Notifications
You must be signed in to change notification settings - Fork 0
/
ft_freqdescriptives.m
224 lines (200 loc) · 8.63 KB
/
ft_freqdescriptives.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
function [freq] = ft_freqdescriptives(cfg, freq)
% FT_FREQDESCRIPTIVES computes descriptive univariate statistics of
% the frequency or time-frequency decomposition of the EEG/MEG signal,
% thus the powerspectrum and its standard error.
%
% Use as
% [freq] = ft_freqdescriptives(cfg, freq)
% [freq] = ft_freqdescriptives(cfg, freqmvar)
%
% The data in freq should be organised in a structure as obtained from
% from the FT_FREQANALYSIS or FT_MVARANALYSIS function. The output structure is comparable
% to the input structure and can be used in most functions that require
% a freq input.
%
% The configuration options are
% cfg.variance = 'yes' or 'no', estimate standard error in the standard way (default = 'no')
% cfg.jackknife = 'yes' or 'no', estimate standard error by means of the jack-knife (default = 'no')
% cfg.keeptrials = 'yes' or 'no', estimate single trial power (useful for fourier data) (default = 'no')
% cfg.channel = Nx1 cell-array with selection of channels (default = 'all'),
% see FT_CHANNELSELECTION for details
% cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all')
% cfg.frequency = [fmin fmax] or 'all', to specify a subset of frequencies (default = 'all')
% cfg.latency = [tmin tmax] or 'all', to specify a subset of latencies (default = 'all')
%
% A variance estimate can only be computed if results from trials and/or
% tapers have been kept.
%
% Descriptive statistics of bivariate metrics is not computed by this function anymore. To this end you
% should use FT_CONNECTIVITYANALYSIS.
%
% To facilitate data-handling and distributed computing you can use
% cfg.inputfile = ...
% cfg.outputfile = ...
% If you specify one of these (or both) the input data will be read from a *.mat
% file on disk and/or the output data will be written to a *.mat file. These mat
% files should contain only a single variable, corresponding with the
% input/output structure.
%
% See also FT_FREQANALYSIS, FT_FREQSTATISTICS, FT_FREQBASELINE, FT_CONNECTIVITYANALYSIS
% Undocumented local options:
% cfg.feedback
% cfg.latency
% cfg.previous
% cfg.version
% Copyright (C) 2004-2006, Pascal Fries & Jan-Mathijs Schoffelen
% Copyright (C) 2010, Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_defaults
ft_preamble init
ft_preamble debug
ft_preamble loadvar freq
ft_preamble provenance freq
ft_preamble trackconfig
% the ft_abort variable is set to true or false in ft_preamble_init
if ft_abort
return
end
% check if the input cfg is valid for this function
cfg = ft_checkconfig(cfg, 'renamed', {'jacknife', 'jackknife'});
% throw warnings for the deprecated options
cfg = ft_checkconfig(cfg, 'deprecated', 'biascorrect');
cfg = ft_checkconfig(cfg, 'deprecated', 'channelcmb');
cfg = ft_checkconfig(cfg, 'deprecated', 'cohmethod');
cfg = ft_checkconfig(cfg, 'deprecated', 'combinemethod');
cfg = ft_checkconfig(cfg, 'deprecated', 'complex');
cfg = ft_checkconfig(cfg, 'deprecated', 'combinechan');
cfg = ft_checkconfig(cfg, 'deprecated', 'keepfourier');
cfg = ft_checkconfig(cfg, 'deprecated', 'partchan');
cfg = ft_checkconfig(cfg, 'deprecated', 'pseudovalue');
cfg = ft_checkconfig(cfg, 'renamed', {'toilim' 'latency'});
cfg = ft_checkconfig(cfg, 'renamed', {'foilim' 'frequency'});
% set the defaults
cfg.feedback = ft_getopt(cfg, 'feedback', 'textbar');
cfg.jackknife = ft_getopt(cfg, 'jackknife', 'no');
cfg.variance = ft_getopt(cfg, 'variance', 'no');
cfg.trials = ft_getopt(cfg, 'trials', 'all', 1);
cfg.channel = ft_getopt(cfg, 'channel', 'all');
cfg.frequency = ft_getopt(cfg, 'frequency', 'all');
cfg.latency = ft_getopt(cfg, 'latency', 'all');
cfg.keeptrials = ft_getopt(cfg, 'keeptrials', 'no');
% check if the input data is valid for this function
freq = ft_checkdata(freq, 'datatype', {'freq', 'freqmvar'}, 'feedback', cfg.feedback);
% get data in the correct representation, it should only have power
freq = ft_checkdata(freq, 'cmbrepresentation', 'sparsewithpow', 'channelcmb', {});
% determine some specific details of the input data
hasrpt = ~isempty(strfind(freq.dimord, 'rpt')) || ~isempty(strfind(freq.dimord, 'subj'));
hastim = ~isempty(strfind(freq.dimord, 'time'));
varflg = strcmp(cfg.variance, 'yes');
jckflg = strcmp(cfg.jackknife, 'yes');
keepflg = strcmp(cfg.keeptrials, 'yes');
% check sensibility of configuration
if sum([varflg keepflg]>1), ft_error('you should specify only one of cfg.keeptrials or cfg.variance'); end
if ~hasrpt && (varflg || keepflg), ft_error('a variance-estimate or a single trial estimate without repeated observations in the input is not possible'); end
if ~hasrpt && ~strcmp(cfg.trials, 'all'), ft_error('trial selection requires input data with repeated observations'); end
if ~varflg && jckflg, varflg = 1; end
% select data of interest
tmpcfg = keepfields(cfg, {'trials', 'channel', 'latency', 'frequency', 'showcallinfo'});
freq = ft_selectdata(tmpcfg, freq);
% restore the provenance information
[cfg, freq] = rollback_provenance(cfg, freq);
if jckflg
% the data is 'sparsewithpow', so it contains a powspctrm and optionally a crsspctrm
% the checking of a 'rpt' is handled above, so it can be assumed that the 'rpt' is the
% first dimension
nrpt = size(freq.powspctrm,1);
sumpowspctrm = sum(freq.powspctrm,1);
freq.powspctrm = (sumpowspctrm(ones(nrpt,1),:,:,:,:) - freq.powspctrm)./(nrpt-1);
clear sumpowspctrm;
if isfield(freq, 'crsspctrm')
sumcrsspctrm = sum(freq.crsspctrm,1);
freq.crsspctrm = (sumcrsspctrm(ones(nrpt,1),:,:,:,:) - freq.crsspctrm)./(nrpt-1);
clear sumcrsspctrm;
end
end
if varflg
siz = [size(freq.powspctrm) 1];
outsum = zeros(siz(2:end));
outssq = zeros(siz(2:end));
n = zeros(siz(2:end));
ft_progress('init', cfg.feedback, 'computing power...');
for j = 1:siz(1)
ft_progress(j/siz(1), 'computing power for replicate %d from %d\n', j, siz(1));
tmp = reshape(freq.powspctrm(j,:,:,:), siz(2:end));
n = n + double(isfinite(tmp));
tmp(~isfinite(tmp)) = 0;
outsum = outsum + tmp;
outssq = outssq + tmp.^2;
end
ft_progress('close');
if jckflg
bias = (n-1).^2;
else
bias = 1;
end
powspctrm = outsum./n;
powspctrmsem = sqrt(bias.*(outssq - (outsum.^2)./n)./(n - 1)./n);
elseif keepflg
%nothing to do
powspctrm = freq.powspctrm;
elseif hasrpt
%compute average only
siz = [size(freq.powspctrm) 1];
powspctrm = reshape(nanmean(freq.powspctrm,1), siz(2:end));
else
%nothing to do
powspctrm = freq.powspctrm;
end
if hasrpt && ~keepflg
dimtok = tokenize(freq.dimord, '_');
newdimord = dimtok{2};
for k = 3:numel(dimtok)
newdimord = [newdimord,'_',dimtok{k}];
end
else
newdimord = freq.dimord;
end
% create the output-structure
output = [];
output.dimord = newdimord;
output.freq = freq.freq;
output.label = freq.label;
output.powspctrm = powspctrm;
if isfield(freq, 'time'), output.time = freq.time; end
if isfield(freq, 'grad'), output.grad = freq.grad; end
if isfield(freq, 'cumtapcnt'), output.cumtapcnt = freq.cumtapcnt; end
if isfield(freq, 'cumsumcnt'), output.cumsumcnt = freq.cumsumcnt; end
if exist('powspctrmsem', 'var'), output.powspctrmsem = powspctrmsem; end
% remember the trialinfo
if strcmp(cfg.keeptrials, 'yes') && isfield(freq, 'trialinfo')
output.trialinfo = freq.trialinfo;
end
% do the general cleanup and bookkeeping at the end of the function
ft_postamble debug
ft_postamble trackconfig
ft_postamble previous freq
% rename the output variable to accomodate the savevar postamble
freq = output;
ft_postamble provenance freq
ft_postamble history freq
ft_postamble savevar freq