MEEG time-lock searchlight
This example shows MVPA analyses performed on MEEG data.
The input dataset involved a paradigm where a participant saw images of six object categories.
The code presented here can be adapted for other MEEG analyses, but there please note: * the current examples do not perform baseline corrections or signal normalizations, which may reduce discriminatory power.
Note: running this code requires FieldTrip.
- For CoSMoMVPA's copyright information and license terms, #
- see the COPYING file distributed with CoSMoMVPA. #
Contents
get timelock data in CoSMoMVPA format
% set configuration config=cosmo_config(); data_path=fullfile(config.tutorial_data_path,'meg_obj6'); % show dataset information readme_fn=fullfile(data_path,'README'); cosmo_type(readme_fn); % reset citation list cosmo_check_external('-tic'); % load preprocessed data data_fn=fullfile(data_path,'meg_obj6_s00.mat'); data_tl=load(data_fn); % Show data_tl % >@@> cosmo_disp(data_tl); % <@@<
******************************************************************************
MEG responses to a human participant viewing pictures of six object categories
******************************************************************************
Contents
--------
- meg_obj6_s00.mat
Preprecessed data from four runs in Matlab / GNU Octave format.
Data is stored in a FieldTrip structure with the following relevant
fields:
+ .trial
n_samples * n_sensors * n_time array with single trial data
+ .time
1 * n_time vector of time relative to stimulus onset.
+ .label
1 * n_sensors cell array with MEG channel labels.
+ .trialinfo
n_time * 6 matrix with trial information; the columns represent:
1) stimulus category in the range `(1:6)`, with the order
being 'body','car','face','flower','insect','scene'.
2) stimulus index in the range `(1:48)`.
3) unused.
4) cumulative number of catch trials in the current run.
5) stimulus presentation duration (in miliseconds).
6) run number.
In the current release, n_samples=494, n_sensors=295, n_time=301.
Version
-------
version 0.1, 26 April 2016
Methods
-------
This dataset contains data from an experiment where a participant
(32y right-handed male) viewed images of six categories while being measured
with MEG. Over four runs, trials were presented at a rate of 1Hz while the
participant performed a one-back task. Blocks of eight trials where separated
by blink periods to reduce eye movement artefaces.
Data was preprocessed using the FieldTrip toolbox [OFMS11].
Data was bandpass-filtered between 1Hz and 40Hz, resampled from 1000Hz to
250Hz, and baseline period for the period of -250 to -50 ms relative to (i.e.
before) stimulus onset.
Based on visual inspection, trials and sensors that seemed noisy were removed.
Catch trials (trials were the stimulus was repeated relative to the previous
trial) were also removed.
License
-------
The contents are made available by Nikolaas N. Oosterhof <nikolaas.oosterhof
|at| unitn.it> under the Creative Commons CC0 1.0 Universal Public Domain
Dedication ("CC0"). See the LICENSE file for details, or visit
http://creativecommons.org/publicdomain/zero/1.0/deed.en.
Download
--------
A zip file with all data is available from the CoSMoMVPA_ website [OCH16]:
http://www.cosmomvpa.org/datadb-meg_obj6.zip
.. _CoSMoMVPA: http://www.cosmomvpa.org
Contact
-------
Nikolaas N. Oosterhof <nikolaas.oosterhof |at| unitn.it>
References
----------
:ref:`Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J.-M. (2011) <OFMS11>` FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.. Computational Intelligence and Neuroscience, 2011:156869.
:ref:`Oosterhof, N. N., Connolly, A. C., and Haxby, J. V. (2016) <OCH16>`. CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave. biorxiv.org, 2016. doi:10.1101/047118.
Warning: A value of class "logical" was indexed with
no subscripts specified. Currently the result of
this operation is the indexed value itself, but in a
future release, it will be an error.
.avg
[ 1.57e-13 2.83e-13 3.59e-13 ... 1.86e-13 2.99e-14 -9.54e-14
1.3e-13 1.17e-13 1.57e-13 ... 1.01e-13 -7.79e-14 -2.44e-13
3e-13 2.84e-13 3.21e-13 ... 2.1e-13 1.76e-13 5.76e-14
: : : : : :
-1.7e-13 -1.45e-13 -2.25e-13 ... -2.85e-13 -3.51e-13 -4.05e-13
3.47e-13 2.5e-13 1.98e-13 ... 2.63e-15 -1.2e-13 -3.68e-13
-1.18e-14 -1.5e-14 -1.55e-14 ... 4.1e-15 6.34e-15 9.04e-15 ]@295x301
.var
[ 3.7e-23 3.68e-23 3.53e-23 ... 3.06e-23 3.23e-23 3.2e-23
1.94e-23 1.99e-23 2.02e-23 ... 1.31e-23 1.31e-23 1.32e-23
2.12e-23 2.06e-23 2e-23 ... 2.08e-23 2.15e-23 2.23e-23
: : : : : :
2.14e-23 2.18e-23 2.27e-23 ... 2.14e-23 2.17e-23 2.33e-23
3.62e-23 3.8e-23 3.68e-23 ... 3.48e-23 3.27e-23 3.28e-23
8.86e-26 8.74e-26 8.49e-26 ... 8.09e-26 7.97e-26 7.91e-26 ]@295x301
.time
[ -0.296 -0.292 -0.288 ... 0.896 0.9 0.904 ]@1x301
.dof
[ 494 494 494 ... 494 494 494
494 494 494 ... 494 494 494
494 494 494 ... 494 494 494
: : : : : :
494 494 494 ... 494 494 494
494 494 494 ... 494 494 494
494 494 494 ... 494 494 494 ]@295x301
.label
{ 'MEG0113'
'MEG0112'
'MEG0123'
:
'MEG2642'
'MEG2643'
'MEG2641' }@295x1
.trial
<double>@494x295x301
(:,:,1) = [ -3.42e-12 1.32e-12 6.77e-12 ... -7.52e-13 -3.61e-12 2.06e-13
1.78e-12 1.42e-12 -2.35e-12 ... -5.65e-12 1.27e-11 1.48e-13
7.84e-12 1.71e-13 -6.44e-13 ... 7.08e-12 4.61e-12 1.45e-13
: : : : : :
-2.46e-12 -1.41e-16 2.27e-12 ... 3.94e-12 1.11e-12 -1.49e-13
-7.9e-12 4.2e-12 4.11e-12 ... -6.51e-12 5.64e-12 1.69e-13
-2.32e-13 -2.49e-12 7.39e-12 ... -4.17e-14 4.16e-12 2.23e-13 ]@494x295
(:,:,2) = [ -4.71e-12 -1.14e-13 6.4e-12 ... -2.31e-12 -1.31e-12 2.36e-13
3.29e-13 2.29e-12 -1.58e-12 ... -7.59e-12 1.21e-11 1.61e-13
4.95e-12 -1.93e-12 6.54e-13 ... 6.65e-12 4.87e-12 1.28e-13
: : : : : :
2.2e-13 -1.04e-12 -6.38e-14 ... 4.31e-12 1.35e-14 -2.23e-13
-6.59e-12 1.98e-12 3.95e-12 ... -6.81e-12 7.02e-12 1.7e-13
1.47e-12 -4.4e-12 4.12e-12 ... 5.68e-13 8.1e-16 8.56e-14 ]@494x295
(:,:,3) = [ -4.91e-12 -8.06e-13 6.51e-12 ... -2.31e-12 9.04e-13 2.02e-13
-2.54e-13 2.11e-12 -7.06e-14 ... -7.79e-12 6.7e-12 5.52e-14
5.23e-14 -3.46e-12 2.19e-12 ... 4.27e-12 5.39e-12 7.02e-14
: : : : : :
2.2e-12 -1.47e-12 -3.68e-12 ... 2.66e-12 -1.31e-12 -2.06e-13
-5.43e-12 4.94e-13 3.67e-12 ... -6.07e-12 4.98e-12 1.2e-13
1.5e-12 -6.54e-12 7.26e-13 ... 9.1e-13 -4.64e-12 1.08e-14 ]@494x295
: :
(:,:,299) = [ 6.21e-12 5.55e-13 3.17e-12 ... -5.9e-14 5.16e-12 4.83e-13
-6.51e-12 4.99e-13 5.83e-12 ... -5.88e-12 -1.05e-11 -9.06e-14
-2.5e-12 9.82e-13 3.51e-12 ... 2.18e-12 -1.08e-12 1.64e-14
: : : : : :
6.17e-12 3.21e-12 5.44e-12 ... -2.33e-12 6.82e-12 1.21e-13
1.7e-12 1.18e-12 -2.92e-13 ... -1.17e-11 6.53e-12 1.19e-16
7.27e-12 -3.62e-12 -1.97e-12 ... -1.76e-13 1.71e-12 -3.43e-15 ]@494x295
(:,:,300) = [ 1.29e-11 3.42e-12 2.89e-12 ... 2.43e-12 8.43e-12 7.99e-13
-6.53e-12 -2.33e-12 3.17e-12 ... -5.23e-12 -9.38e-12 -3.72e-14
-1.68e-12 6.17e-13 2.72e-12 ... 2.28e-13 7.02e-13 1.03e-13
: : : : : :
7.02e-12 4.53e-12 5.36e-12 ... -1.64e-12 -1.15e-12 2.73e-13
4.55e-12 1.99e-12 -4.22e-12 ... -7.83e-12 7.7e-12 1.26e-13
5.51e-12 -3.78e-12 -2.74e-12 ... 3.19e-13 -3.31e-12 2.01e-13 ]@494x295
(:,:,301) = [ 1.97e-11 6.19e-12 3.72e-12 ... 2.91e-12 1.02e-11 1e-12
-6.07e-12 -5.01e-12 -4.51e-13 ... -3.67e-12 -5.4e-12 7e-15
-5.07e-13 1.77e-12 3.3e-12 ... -2.88e-13 2.25e-12 1.2e-13
: : : : : :
6.38e-12 5.19e-12 3.13e-12 ... -5.81e-13 -6.2e-12 2.74e-13
7.23e-12 1.79e-12 -7.43e-12 ... -5.62e-12 7.64e-12 1.98e-13
3.53e-12 -3.41e-12 -3.94e-12 ... 2.08e-12 -8.5e-12 2.42e-13 ]@494x295
.dimord
'rpt_chan_time'
.grad
.balance
.current
'none'
.chanori
[ -0.974 0.226 0.00639
-0.974 0.226 0.00639
-0.974 0.226 0.00639
: : :
0.925 -0.379 -0.0148
0.925 -0.379 -0.0148
0.925 -0.379 -0.0148 ]@306x3
.chanpos
[ -10.6 4.54 -1.15
-10.6 4.54 -1.15
-10.6 4.54 -1.15
: : :
9.93 -4.43 2.42
9.93 -4.43 2.42
9.93 -4.43 2.42 ]@306x3
.chantype
{ 'megplanar'
'megplanar'
'megmag'
:
'megplanar'
'megplanar'
'megmag' }@306x1
.chanunit
{ 'T'
'T'
'T'
:
'T'
'T'
'T' }@306x1
.coilori
[ -0.974 0.226 0.00639
-0.974 0.226 0.00639
-0.974 0.226 0.00639
: : :
0.925 -0.379 -0.0148
0.925 -0.379 -0.0148
0.925 -0.379 -0.0148 ]@510x3
.coilpos
[ -10.6 4.56 -0.312
-10.6 4.51 -1.99
-10.4 5.35 -1.18
: : :
9.61 -5.2 2.45
10.2 -3.65 2.39
9.93 -4.43 2.42 ]@510x3
.coordsys
'neuromag'
.label
{ 'MEG0113'
'MEG0112'
'MEG0111'
:
'MEG2642'
'MEG2643'
'MEG2641' }@306x1
.tra
[ -1 1 0 ... 0 0 0
0 0 -1 ... 0 0 0
0 0 0 ... 0 0 0
: : : : : :
0 0 0 ... 0 0 0
0 0 0 ... -1 1 0
0 0 0 ... 0 0 1 ]@306x510
.type
'neuromag306'
.unit
'cm'
.trialinfo
[ 4 28 0 1 142 1
3 18 0 2 147 1
2 15 0 2 147 1
: : : : : :
6 47 0 23 142 4
4 28 0 24 146 4
3 18 0 24 147 4 ]@494x6
.cfg
.keeptrials
'yes'
.callinfo
.usercfg
.keeptrials
'yes'
.inputhash
{ '1f0c477 ... c8e4a55' }
.fieldtrip
'unknown'
.matlab
'8.1.0.604 (R2013a)'
.computer
'maci64'
.hostname
'nicks-MacBook-Pro-2'
.user
'nick'
.pwd
'/Users/ ... scripts'
.calltime
[ 2.02e+03 3 3 13 55 53.2 ]
.proctime
[ 1.91 ]
.procmem
[ 3.51e+08 ]
.outputhash
{ '390e69f ... facc363' }
.version
.name
'/Users/ ... lysis.m'
.id
'$Id$'
.trackconfig
'off'
.checkconfig
'loose'
.checksize
[ 1e+05 ]
.showcallinfo
'yes'
.debug
'no'
.trackcallinfo
'yes'
.trackdatainfo
'no'
.trackparaminfo
'no'
.trials
'all'
.channel
{ 'MEG0112'
'MEG0113'
'MEG0123'
:
'MEG2641'
'MEG2642'
'MEG2643' }@295x1
.covariance
'no'
.removemean
'yes'
.vartrllength
[ 0 ]
.feedback
'text'
.preproc
[ ]
.previous
.channel
{ 'MEG0112'
'MEG0113'
'MEG0123'
:
'MEG2641'
'MEG2642'
'MEG2643' }@295x1
.trials
[ 1 2 3 ... 729 730 733 ]@1x494
.callinfo
.usercfg
.channel
{ <char>@1x7
<char>@1x7
<char>@1x7
:
<char>@1x7
<char>@1x7
<char>@1x7 }@295x1
.trials
[ 1 2 3 ... 729 730 733 ]@1x494
.inputhash
{ }
.fieldtrip
'unknown'
.matlab
'8.1.0.604 (R2013a)'
.computer
'maci64'
.hostname
'nicks-MacBook-Pro-2'
.user
'nick'
.pwd
'/Users/ ... scripts'
.calltime
[ 2.02e+03 3 3 13 55 42.7 ]
.proctime
[ 0.571 ]
.procmem
[ 2.79e+08 ]
.outputhash
{ }
.version
.name
'/Users/ ... tdata.m'
.id
'unknown'
.trackconfig
'off'
.checkconfig
'loose'
.checksize
[ 1e+05 ]
.showcallinfo
'yes'
.debug
'no'
.trackcallinfo
'yes'
.trackdatainfo
'no'
.trackparaminfo
'no'
.tolerance
[ 1e-05 ]
.select
'intersect'
.latency
'all'
.frequency
'all'
.previous
.callinfo
.usercfg
struct (empty)
.inputhash
{ }
.fieldtrip
'unknown'
.matlab
'8.1.0.604 (R2013a)'
.computer
'maci64'
.hostname
'nicks-MacBook-Pro-2'
.user
'nick'
.pwd
'/Users/ ... scripts'
.calltime
[ 2.02e+03 3 3 13 55 40.5 ]
.proctime
[ 0.425 ]
.procmem
[ 2.21e+05 ]
.outputhash
{ }
.version
.name
'/Users/ ... ddata.m'
.id
'$Id$'
.trackconfig
'off'
.checkconfig
'loose'
.checksize
[ 1e+05 ]
.showcallinfo
'yes'
.debug
'no'
.trackcallinfo
'yes'
.trackdatainfo
'no'
.trackparaminfo
'no'
.previous
{ .trl .trl .trl .trl
<double>@164x8 <double>@209x8 <double>@186x8 <double>@174x8
.channel .channel .channel .channel
<cell> <cell> <cell> <cell>
.demean .demean .demean .demean
<char>@1x3 <char>@1x3 <char>@1x3 <char>@1x3
.baselinewindow .baselinewindow .baselinewindow .baselinewindow
<double>@1x2 <double>@1x2 <double>@1x2 <double>@1x2
.callinfo .callinfo .callinfo .callinfo
<struct> <struct> <struct> <struct>
.version .version .version .version
<struct> <struct> <struct> <struct>
.trackconfig .trackconfig .trackconfig .trackconfig
<char>@1x3 <char>@1x3 <char>@1x3 <char>@1x3
.checkconfig .checkconfig .checkconfig .checkconfig
<char>@1x5 <char>@1x5 <char>@1x5 <char>@1x5
.checksize .checksize .checksize .checksize
<double> <double> <double> <double>
.showcallinfo .showcallinfo .showcallinfo .showcallinfo
<char>@1x3 <char>@1x3 <char>@1x3 <char>@1x3
.debug .debug .debug .debug
<char>@1x2 <char>@1x2 <char>@1x2 <char>@1x2
.trackcallinfo .trackcallinfo .trackcallinfo .trackcallinfo
<char>@1x3 <char>@1x3 <char>@1x3 <char>@1x3
.trackdatainfo .trackdatainfo .trackdatainfo .trackdatainfo
<char>@1x2 <char>@1x2 <char>@1x2 <char>@1x2
.trackparaminfo .trackparaminfo .trackparaminfo .trackparaminfo
<char>@1x2 <char>@1x2 <char>@1x2 <char>@1x2
.offset .offset .offset .offset
<double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty)
.toilim .toilim .toilim .toilim
<double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty)
.begsample .begsample .begsample .begsample
<double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty)
.endsample .endsample .endsample .endsample
<double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty)
.minlength .minlength .minlength .minlength
<double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty)
.trials .trials .trials .trials
<char>@1x3 <char>@1x3 <char>@1x3 <char>@1x3
.feedback .feedback .feedback .feedback
<char>@1x3 <char>@1x3 <char>@1x3 <char>@1x3
.length .length .length .length
<double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty) <double>@0x0 (empty)
.overlap .overlap .overlap .overlap
<double> <double> <double> <double>
.previous .previous .previous .previous
<struct> <struct> <struct> <struct> }
% convert to cosmomvpa struct, using cosmo_meeg_dataset % >@@> ds=cosmo_meeg_dataset(data_tl); % <@@< % show the dataset % >@@> cosmo_disp(ds); % <@@<
.samples
[ -3.42e-12 1.32e-12 6.77e-12 ... 2.91e-12 1.02e-11 1e-12
1.78e-12 1.42e-12 -2.35e-12 ... -3.67e-12 -5.4e-12 7e-15
7.84e-12 1.71e-13 -6.44e-13 ... -2.88e-13 2.25e-12 1.2e-13
: : : : : :
-2.46e-12 -1.41e-16 2.27e-12 ... -5.81e-13 -6.2e-12 2.74e-13
-7.9e-12 4.2e-12 4.11e-12 ... -5.62e-12 7.64e-12 1.98e-13
-2.32e-13 -2.49e-12 7.39e-12 ... 2.08e-12 -8.5e-12 2.42e-13 ]@494x88795
.fa
.chan
[ 1 2 3 ... 293 294 295 ]@1x88795
.time
[ 1 1 1 ... 301 301 301 ]@1x88795
.a
.fdim
.labels
{ 'chan'
'time' }
.values
{ { 'MEG0113' 'MEG0112' 'MEG0123' ... 'MEG2642' 'MEG2643' 'MEG2641' }@1x295
[ -0.296 -0.292 -0.288 ... 0.896 0.9 0.904 ]@1x301 }
.meeg
.samples_field
'trial'
.sa
.trialinfo
[ 4 28 0 1 142 1
3 18 0 2 147 1
2 15 0 2 147 1
: : : : : :
6 47 0 23 142 4
4 28 0 24 146 4
3 18 0 24 147 4 ]@494x6
% set the targets in ds.sa.targets (trial condition) % Hint: use the first column from ds.sa.trialinfo % >@@> ds.sa.targets=ds.sa.trialinfo(:,1); % 6 categories % <@@< % set the chunks in ds.sa.chunks (independent measurements) % all trials are here considered to be independent, so the chunks % must all have a different value % >@@> nsamples=size(ds.samples,1); ds.sa.chunks=(1:nsamples)'; % <@@< % in addition give a label to each trial index2label={'body','car','face','flower','insect','scene'}; ds.sa.labels=cellfun(@(x)index2label(x),num2cell(ds.sa.targets)); % just to check everything is ok cosmo_check_dataset(ds);
Count number of channels, time points and trials
>@@>
fprintf('There are %d channels, %d time points and %d trials\n',... numel(unique(ds.fa.chan)),numel(unique(ds.fa.time)),... size(ds.samples,1)); % <@@<
There are 295 channels, 301 time points and 494 trials
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Part I: compute difference between faces and scenes %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % For each time point and sensor; then visualize the results % for the magnetometer (meg_axial) sensors. % slice 'ds' twice to get 'ds_face' and 'ds_scene', each with only trials % from the face and scene categories % >@@> ds_face=cosmo_slice(ds,cosmo_match(ds.sa.labels,'face')); ds_scene=cosmo_slice(ds,cosmo_match(ds.sa.labels,'scene')); % <@@< % prepare dataset for output ds_faceVSscene=cosmo_slice(ds_face,1); ds_faceVSscene.sa=struct(); % destroy sample attributes % Compute difference between average of faces versus average of scenes; % store the result in the samples field of ds_faceVSscene % >@@> ds_faceVSscene.samples=mean(ds_face.samples)-mean(ds_scene.samples); % <@@< % Convert ds_faceVSscene to a fieldtrip structure and convert ft_faceVSscene=cosmo_map2meeg(ds_faceVSscene);
Use FieldTrip to visualize the face versus house contrast
chantype='meg_axial'; layout=cosmo_meeg_find_layout(ds_faceVSscene,'chantype',chantype); figure(); cfg=struct(); cfg.interactive='yes'; cfg.zlim=[-1 1]; cfg.layout=layout; % show figure with plots for each sensor ft_multiplotER(cfg, ft_faceVSscene);
the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 34 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 7 MB the call to "ft_multiplotER" took 3 seconds and required the additional allocation of an estimated 72 MB
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Part 2: run searchlight over time
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % set MVPA parameters fprintf('The input has feature dimensions %s\n', ... cosmo_strjoin(ds.a.fdim.labels,', ')); % only select relevant time period and sensors sensor_posterior_axial={'MEG1631', 'MEG1641', 'MEG1731', 'MEG1841', ... 'MEG1911', 'MEG1921', 'MEG1941', 'MEG2231', ... 'MEG2311', 'MEG2321', 'MEG2341', 'MEG2431', ... 'MEG2441', 'MEG2511', 'MEG2531'}; % define the mask msk=cosmo_dim_match(ds,'time',@(t) t>=-.1 & t<=.4,... 'chan',sensor_posterior_axial); % first slice the dataset, then use cosmo_dim_prune to avoid using % non-selected data. Assign the result to ds_sel %%%% >>> Your code here <<< %%%% ds_sel_orig=cosmo_slice(ds,msk,2); cosmo_disp(ds_sel_orig); ft_orig=cosmo_map2meeg(ds_sel_orig);
The input has feature dimensions chan, time
.samples
[ -2.79e-13 -1.12e-13 -1.01e-13 ... 2.61e-13 9.61e-14 2.27e-13
-3.4e-13 -2.66e-13 -1.67e-13 ... -1.14e-13 -6.45e-14 -3.72e-14
-1.07e-13 -1.91e-13 -2.43e-13 ... -2.15e-13 -4.53e-14 -8.97e-14
: : : : : :
-9.03e-14 4.5e-14 2.61e-13 ... 2.05e-13 6.45e-13 3.76e-13
3.67e-13 4.59e-13 6.28e-13 ... 1.47e-13 4.06e-13 2.12e-13
7.34e-14 3.65e-13 2.6e-13 ... 1.2e-13 3.19e-13 4.7e-13 ]@494x1890
.fa
.chan
[ 173 176 185 ... 271 274 280 ]@1x1890
.time
[ 50 50 50 ... 175 175 175 ]@1x1890
.a
.fdim
.labels
{ 'chan'
'time' }
.values
{ { 'MEG0113' 'MEG0112' 'MEG0123' ... 'MEG2642' 'MEG2643' 'MEG2641' }@1x295
[ -0.296 -0.292 -0.288 ... 0.896 0.9 0.904 ]@1x301 }
.meeg
.samples_field
'trial'
.sa
.trialinfo
[ 4 28 0 1 142 1
3 18 0 2 147 1
2 15 0 2 147 1
: : : : : :
6 47 0 23 142 4
4 28 0 24 146 4
3 18 0 24 147 4 ]@494x6
.targets
[ 4
3
2
:
6
4
3 ]@494x1
.chunks
[ 1
2
3
:
492
493
494 ]@494x1
.labels
{ 'flower'
'face'
'car'
:
'scene'
'flower'
'face' }@494x1
ds_sel=cosmo_dim_prune(ds_sel_orig); cosmo_disp(ds_sel); ft_sel=cosmo_map2meeg(ds_sel);
.samples
[ -2.79e-13 -1.12e-13 -1.01e-13 ... 2.61e-13 9.61e-14 2.27e-13
-3.4e-13 -2.66e-13 -1.67e-13 ... -1.14e-13 -6.45e-14 -3.72e-14
-1.07e-13 -1.91e-13 -2.43e-13 ... -2.15e-13 -4.53e-14 -8.97e-14
: : : : : :
-9.03e-14 4.5e-14 2.61e-13 ... 2.05e-13 6.45e-13 3.76e-13
3.67e-13 4.59e-13 6.28e-13 ... 1.47e-13 4.06e-13 2.12e-13
7.34e-14 3.65e-13 2.6e-13 ... 1.2e-13 3.19e-13 4.7e-13 ]@494x1890
.fa
.chan
[ 1 2 3 ... 13 14 15 ]@1x1890
.time
[ 1 1 1 ... 126 126 126 ]@1x1890
.a
.fdim
.labels
{ 'chan'
'time' }
.values
{ { 'MEG1631' 'MEG1641' 'MEG1731' ... 'MEG2441' 'MEG2511' 'MEG2531' }@1x15
[ -0.1 -0.096 -0.092 ... 0.392 0.396 0.4 ]@1x126 }
.meeg
.samples_field
'trial'
.sa
.trialinfo
[ 4 28 0 1 142 1
3 18 0 2 147 1
2 15 0 2 147 1
: : : : : :
6 47 0 23 142 4
4 28 0 24 146 4
3 18 0 24 147 4 ]@494x6
.targets
[ 4
3
2
:
6
4
3 ]@494x1
.chunks
[ 1
2
3
:
492
493
494 ]@494x1
.labels
{ 'flower'
'face'
'car'
:
'scene'
'flower'
'face' }@494x1
% define the neighborhood for time with a time radius of 2 time points, % and assign to time_nbrhood. % Hint: use cosmo_interval_neighborhood, time_nbrhood=cosmo_interval_neighborhood(ds_sel,'time','radius',2); cosmo_disp(time_nbrhood)
.a
.fdim
.labels
{ 'time' }
.values
{ [ -0.1 -0.096 -0.092 ... 0.392 0.396 0.4 ]@1x126 }
.meeg
.samples_field
'trial'
.fa
.time
[ 1 2 3 ... 124 125 126 ]@1x126
.neighbors
{ [ 1 2 3 ... 43 44 45 ]@1x45
[ 1 2 3 ... 58 59 60 ]@1x60
[ 1 2 3 ... 73 74 75 ]@1x75
:
[ 1.82e+03 1.82e+03 1.82e+03 ... 1.89e+03 1.89e+03 1.89e+03 ]@1x75
[ 1.83e+03 1.83e+03 1.83e+03 ... 1.89e+03 1.89e+03 1.89e+03 ]@1x60
[ 1.85e+03 1.85e+03 1.85e+03 ... 1.89e+03 1.89e+03 1.89e+03 ]@1x45 }@126x1
.origin
.a
.fdim
.labels
{ 'chan'
'time' }
.values
{ { <char>@1x7 <char>@1x7 <char>@1x7 ... <char>@1x7 <char>@1x7 <char>@1x7 }@1x15
[ -0.1 -0.096 -0.092 ... 0.392 0.396 0.4 ]@1x126 }
.meeg
.samples_field
'trial'
.fa
.chan
[ 1 2 3 ... 13 14 15 ]@1x1890
.time
[ 1 1 1 ... 126 126 126 ]@1x1890
%%%% >>> Your code here <<< %%%% % Define the measure to be cosmo_crossvalidation_measure, % and assign to measure measure=@cosmo_crossvalidation_measure; %%%% >>> Your code here <<< %%%% %nsamples=numel(ds_sel.samples); %rp=cosmo_randperm(nsamples); %ds_sel.sa.targets=ds_sel.sa.targets(rp); partitions=cosmo_independent_samples_partitioner(ds_sel,... 'fold_count',5,... 'test_ratio',0.2); cosmo_disp(partitions)
.test_indices
{ [ 1 [ 12 [ 2 [ 9 [ 5
3 14 4 11 10
9 24 5 15 18
: : : : :
483 469 475 489 465
486 487 480 490 481
489 ]@90x1 488 ]@90x1 493 ]@90x1 491 ]@90x1 488 ]@90x1 }
.train_indices
{ [ 2 [ 1 [ 1 [ 1 [ 1
4 2 3 2 2
5 4 6 3 3
: : : : :
490 492 491 492 492
493 493 492 493 493
494 ]@360x1 494 ]@360x1 494 ]@360x1 494 ]@360x1 494 ]@360x1 }
Define the partitioning scheme using cosmo_independent_samples_partitioner, and assign to partitions. Use 'fold_count',5 to use 5 folds, and use 'test_ratio',.2 to use 20% of the data for testing (and 80% for training) in each fold.
%%%% >>> Your code here <<< %%%% % Use the LDA classifier and the partitions just defined, measure_args=struct(); measure_args.partitions=partitions; measure_args.classifier=@cosmo_classify_lda; ds_sl=cosmo_searchlight(ds_sel,time_nbrhood,measure,measure_args); plot(ds_sl.a.fdim.values{1},ds_sl.samples) xlabel('time'); ylabel('classification accuracy');
+00:00:01 [####################] -00:00:00
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Part III: channel-time searchlight
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % select limited time period msk=cosmo_dim_match(ds,'time',@(t) t>=-.1 & t<=.4); % first slice the dataset using the mask and assign to 'ds_sel' ds_sel=cosmo_slice(ds,msk,2); % Now use cosmo_dim_prune to avoid attempts to using % non-selected data in the searchlight ds_sel=cosmo_dim_prune(ds_sel); % Set the seachlight parameters chan_count=10; % 10 center channels in each searchlight time_radius=2; % 2*2+1=5 time bins chan_type='meg_combined_from_planar'; % define the neighborhood for each dimensions % First, set 'chan_nbrhood' using cosmo_meeg_chan_neighborhood, % and use the 'chantype' and 'count' parameters to set the channel type % and the number of sensors in each searchlight. chan_nbrhood=cosmo_meeg_chan_neighborhood(ds_sel,'chantype',chan_type,... 'count',chan_count); cosmo_disp(chan_nbrhood);
.neighbors
{ [ 1 296 591 ... 3.64e+04 3.67e+04 3.7e+04 ]@1x2394
[ 1 296 591 ... 3.63e+04 3.66e+04 3.69e+04 ]@1x2394
[ 1 296 591 ... 3.64e+04 3.67e+04 3.7e+04 ]@1x2394
:
[ 136 431 726 ... 3.66e+04 3.69e+04 3.72e+04 ]@1x2520
[ 251 546 841 ... 3.66e+04 3.69e+04 3.72e+04 ]@1x2520
[ 136 431 726 ... 3.66e+04 3.69e+04 3.72e+04 ]@1x2520 }@102x1
.fa
.chan
[ 1 2 3 ... 100 101 102 ]@1x102
.a
.fdim
.labels
{ 'chan' }
.values
{ { 'MEG0112+0113' 'MEG0122+0123' 'MEG0132+0133' ... 'MEG2622+2623' 'MEG2632+2633' 'MEG2642+2643' }@1x102 }
.meeg
.samples_field
'trial'
.origin
.fa
.chan
[ 1 2 3 ... 293 294 295 ]@1x37170
.time
[ 1 1 1 ... 126 126 126 ]@1x37170
.a
.fdim
.labels
{ 'chan'
'time' }
.values
{ { <char>@1x7 <char>@1x7 <char>@1x7 ... <char>@1x7 <char>@1x7 <char>@1x7 }@1x295
[ -0.1 -0.096 -0.092 ... 0.392 0.396 0.4 ]@1x126 }
.meeg
.samples_field
'trial'
%%%% >>> Your code here <<< %%%% % Second, set 'time_nbrhood' using cosmo_interval_neighborhood, % using the 'time' dimension time_nbrhood=cosmo_interval_neighborhood(ds_sel,'time','radius',2); %%%% >>> Your code here <<< %%%% % cross neighborhoods for chan-time searchlight % Hint: use cosmo_cross_neighborhood, and use chan_nbrhood and time_nbrhood % (in that order) in a cell as the second argument nbrhood=cosmo_cross_neighborhood(ds_sel,{chan_nbrhood,time_nbrhood}); %%%% >>> Your code here <<< %%%% % print how many neighbors features have on average nbrhood_nfeatures=cellfun(@numel,nbrhood.neighbors); fprintf('Features have on average %.1f +/- %.1f neighbors\n', ... mean(nbrhood_nfeatures), std(nbrhood_nfeatures));
+00:00:00 [####################] -00:00:00 crossing neighborhoods Features have on average 96.5 +/- 6.2 neighbors
% set the 'measure' variable to a function handle to the % split-half correlation measure measure=@cosmo_correlation_measure; % Define the partitioning scheme using % cosmo_independent_samples_partitioner. % Use 'fold_count',1 to use 1 folds, % and use 'test_ratio',.5 to use 50% of the data for testing (and 50% for % training) in the single fold. partitions=cosmo_independent_samples_partitioner(ds_sel,'fold_count',1,... 'test_ratio',0.5); %%%% >>> Your code here <<< %%%% measure_args=struct(); measure_args.partitions=partitions;
run searchlight
run the searchlight using the parameters above, and assign the result to a varibale 'ds_sl'
ds_sl=cosmo_searchlight(ds_sel,nbrhood,measure,measure_args);
%%%% >>> Your code here <<< %%%%
+00:00:12 [####################] -00:00:00
visualize timeseries results
% deduce layout from output layout=cosmo_meeg_find_layout(ds_sl); fprintf('The output uses layout %s\n', layout.name); % map ds_sl to a FieldTrip structure. Assign the result to 'sl_ft' sl_ft=cosmo_map2meeg(ds_sl); %%%% >>> Your code here <<< %%%% figure(); cfg = []; cfg.interactive = 'yes'; cfg.zlim=[-1 1]; cfg.layout = layout; % show figure with fisher-transformed correlations for each sensor ft_multiplotER(cfg, sl_ft);
The output uses layout neuromag306cmb.lay the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 1 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_multiplotER" took 2 seconds and required the additional allocation of an estimated 150 MB
visualize topology results
show figure with topology for 100 before to 400ms after stimulus onset in bins of 50 ms
figure(); cfg.xlim=-0.1:0.05:0.4; ft_topoplotER(cfg, sl_ft);
the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 16 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 9 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 5 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 1 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 0 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 14 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 1 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 5 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 11 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 8 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 1 seconds and required the additional allocation of an estimated 0 MB the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB the call to "ft_topoplotTFR" took 2 seconds and required the additional allocation of an estimated 2 MB the call to "ft_topoplotER" took 10 seconds and required the additional allocation of an estimated 86 MB
Show citation information
cosmo_check_external('-cite');
Warning: A value of class "char" was indexed with no subscripts specified. Currently the result of this operation is the indexed value itself, but in a future release, it will be an error. If you use CoSMoMVPA and/or some other toolboxes for a publication, please cite: R. Oostenveld, P. Fries, E. Maris, J.-M. Schoffelen (2011). FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data, Computational Intelligence and Neuroscience, vol. 2011, Article ID 156869, 9 pages.doi:10.1155/2011/156869. FieldTrip toolbox available online from http://fieldtrip.fcdonders.nl N. N. Oosterhof, A. C. Connolly, J. V. Haxby (2016). CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave. Frontiers in Neuroinformatics, doi:10.3389/fninf.2016.00027.. CoSMoMVPA toolbox available online from http://cosmomvpa.org The Mathworks, Natick, MA, United States. Matlab 9.1.0.441655 (R2016b) (September 7, 2016). available online from http://www.mathworks.com