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Chengdu, China |
- By whom: Robert Oostenveld
- When: 27-29 September 2017
- Where: Room 133 (theoretical part) and Room 135 (practical part), Center for Information in Medicine, School of Life Science & Technology, University of Electronic Science & Technology of China (UESTC)
- Local Organizer: Pedro Valdés-Sosa, Vincent (Qing Wang) and Eduardo.
- Advice for the attendees: Bring a computer with a recent MATLAB version and FieldTrip.
This will be a loosely organized workshop that comprises a series of lectures and hands-on sessions. In the first workshop session Robert will give an introduction on FieldTrip and we will do a hands-on session on preprocessing EEG data. After that we will jointly decide what the next topic will be. Possible topics include basic or advanced analysis methods for EEG/MEG using FieldTrip, the use of the BIDS standard for EEG/MEG data organization and sharing, and the use of the megconnectome pipelines from the Human Connectome Project.
- 3h - preprocessing of Cuban EEG dataset (eyes opened/closed and hyperventilation)
- 1h - forward and inverse modeling lecture - slides
- 2h - anatomical processing and lead fields for Cuban EEG dataset
Please note that the script for the Cuban EEG dataset is further down on this page.
{% include markup/info %} Please read the FieldTrip reference paper to understand the FieldTrip toolbox design.
If you are not familiar with MATLAB or are not certain about your MATLAB skills, please go through the MATLAB for psychologists tutorial (which is also useful for non-psychologists). {% include markup/end %}
For the hands-on sessions you have to start MATLAB. We will provide a recent FieldTrip copy and the hands-on data on a USB stick. Alternatively, you can get them from the download server (which will be slower).
To get going, you need to start MATLAB. Then, you need to issue the following command
restoredefaultpath
cd <path_to_fieldtrip>
addpath(pwd)
ft_defaults
{% include markup/danger %} Please do NOT use the graphical path management tool from MATLAB. In this hands-on session we'll manage the path from the command line, but in general you are much better off using the startup.m file than the path GUI.
Please do NOT add FieldTrip with all subdirectories, subdirectories will be added automatically when needed, and only when needed. {% include markup/end %}
The restoredefaultpath command clears your path, keeping only the official MATLAB toolboxes. The addpath(pwd) statement adds the present working directory, i.e. the directory containing the FieldTrip main functions. The ft_defaults command ensures that all required subdirectories are added to the path.
If you get the error "can't find the command ft_defaults" you should check the present working directory.
After installing FieldTrip to your path, you need to change into the hands-on specific directory, containing the data that is necessary to run the specific hands-on session.
%%
cfg = [];
cfg.dataset = 'MC0001519.set';
data = ft_preprocessing(cfg);
figure
plot(data.time{1}, data.trial{1}(1,:));
title(data.label{1})
cfg = [];
cfg.viewmode = 'vertical';
cfg = ft_databrowser(cfg, data)
%% cut the data into 1 second pieces
cfg = [];
cfg.length = 1; % seconds
data_1sec = ft_redefinetrial(cfg, data);
cfg = [];
cfg.viewmode = 'vertical';
ft_databrowser(cfg, data_1sec);
% in the GUI: increase the horizontal scale a few times
cfg = [];
cfg.viewmode = 'vertical';
cfg.continuous = 'yes';
ft_databrowser(cfg, data_1sec);
% in the GUI: increase the horizontal scale a few times
%% select every 2nd trial
cfg = [];
cfg.trials = 1:2:length(data_1sec.trial);
data_1sec_sel = ft_selectdata(cfg, data_1sec);
cfg = [];
cfg.viewmode = 'vertical';
cfg.continuous = 'yes';
ft_databrowser(cfg, data_1sec_sel);
% in the GUI: increase the horizontal scale a few times
%%
cfg = [];
cfg.dataset = 'MC0001519.set';
cfg.trialfun = 'ft_trialfun_general';
cfg.trialdef.eventtype = '?';
dummy = ft_definetrial(cfg);
event = ft_read_event('MC0001519.set');
disp(event(1));
disp(event(2));
disp(event(3));
disp(event(4));
disp(event(end));
cfg = [];
cfg.dataset = 'MC0001519.set';
cfg.trialfun = 'ft_trialfun_general';
cfg.trialdef.prestim = 0;
cfg.trialdef.poststim = 10;
cfg.trialdef.eventtype = 'gui';
cfg = ft_definetrial(cfg);
% in the GUI: select eyes_opened
data = ft_preprocessing(cfg);
cfg = [];
cfg.viewmode = 'vertical';
cfg.continuous = 'yes';
ft_databrowser(cfg, data);
% in the GUI: increase the horizontal scale to 60 seconds
%% use a custom trialfun for reading the eyes_opened segments
cfg = [];
cfg.dataset = 'MC0001519.set';
cfg.trialfun = 'trialfun_opened';
cfg = ft_definetrial(cfg);
data = ft_preprocessing(cfg);
cfg = [];
cfg.viewmode = 'vertical';
cfg.continuous = 'yes';
ft_databrowser(cfg, data);
% in the GUI: increase the horizontal scale to 60 seconds
%% use a custom trialfun for reading both segments
cfg = [];
cfg.dataset = 'MC0001519.set';
cfg.trialfun = 'trialfun_both';
cfg = ft_definetrial(cfg);
data = ft_preprocessing(cfg);
cfg = [];
cfg.viewmode = 'vertical';
cfg.continuous = 'yes';
ft_databrowser(cfg, data);
% in the GUI: increase the horizontal scale to 60 seconds
%% select opened and closed
cfg = [];
cfg.trials = find(data.trialinfo==1);
data_opened = ft_selectdata(cfg, data);
cfg = [];
cfg.trials = find(data.trialinfo==2);
data_closed = ft_selectdata(cfg, data);
%%
cfg = [];
cfg.length = 1; % seconds
data_opened_1sec = ft_redefinetrial(cfg, data_opened);
cfg = [];
cfg.length = 1; % seconds
data_closed_1sec = ft_redefinetrial(cfg, data_closed);
% EXCERCISE: use ft_redefinetrial on "data" and look at the trialinfo in
% the output
cfg = [];
cfg.method = 'mtmfft';
cfg.taper = 'hanning';
spectrum_opened = ft_freqanalysis(cfg, data_opened_1sec);
spectrum_closed = ft_freqanalysis(cfg, data_closed_1sec);
%%
figure
cfg = [];
cfg.layout = 'EEG1010.lay';
cfg.xlim = [2 40];
ft_multiplotER(cfg, spectrum_opened, spectrum_closed)
% EXCERCIS
% - go back and use cfg.reref and cfg.refchannel
% - go back and use ft_rejectvisual
% - go back and use demean=yes and/or some filtering
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
cfg = [];
cfg.method = 'runica';
comp = ft_componentanalysis(cfg, data);
% this takes about 30 seconds
cfg = [];
cfg.layout = 'EEG1010.lay';
cfg.viewmode = 'component';
cfg.continuous = 'yes';
ft_databrowser(cfg, comp)
% in the GUI: write down the artefact components
cfg = [];
cfg.component = [1 2];
data_clean = ft_rejectcomponent(cfg, comp);
cfg = [];
cfg.viewmode = 'vertical';
cfg.continuous = 'yes';
ft_databrowser(cfg, data_clean);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
data_clean = data;
figure
ft_plot_sens(data_clean.elec, 'label', 'label');
% in the FIGURE: turn the axes around, then do
ft_plot_axes(data_clean.elec);
edit average58.ele
elec = [];
elec.elecpos = [
119 91 12
61 91 12
141 140 69
39 140 69
154 147 123
26 147 123
158 115 163
22 115 163
120 50 205
60 50 205
158 83 52
22 83 52
173 71 107
7 71 106
158 76 177
22 76 177
90 155 52
90 177 129
90 128 194
117 152 58
63 152 58
115 130 191
65 130 191
125 126 32
55 126 32
158 102 170
22 102 170
167 108 91
13 108 91
172 104 114
8 104 114
171 81 143
9 81 143
142 93 191
38 93 191
90 93 5
90 171 88
90 145 182
90 90 209
90 49 211
103 110 202
77 110 202
118 147 176
62 147 176
132 150 167
48 150 167
120 169 127
60 169 127
158 114 69
22 114 69
143 149 94
37 149 94
117 166 99
63 166 99
172 101 138
8 101 138
158 57 173
22 57 173
];
elec.label = data_clean.label;
elec.unit = 'mm';
figure
ft_plot_sens(elec, 'label', 'label');
% in the FIGURE: turn the axes around, then do
ft_plot_axes(elec);
%%
tmp = load('standard_bem.mat');
headmodel = tmp.vol;
cfg = [];
cfg.method = 'interactive';
cfg.headshape = headmodel.bnd(1);
elec_realigned = ft_electroderealign(cfg, elec);
% In the GUI: rotate 90 degrees around x, then apply
% In the GUI: translate [-90 90 75], then apply and close
%%
if false
mri = ft_read_mri('DICOM/MR.1.3.12.2.1107.5.2.6.14077.5.0.23815012430271304');
disp(mri.transform)
end
mri = ft_read_mri('FREESURFER_SURF/mri/T1.nii');
disp(mri.transform)
mri = ft_determine_coordsys(mri);
% in the GUI: press r, a, s, n
% see https://www.fieldtriptoolbox.org/tutorial/sourcemodel
cfg = [];
ft_sourceplot(cfg, mri);
cfg = [];
cfg.method = 'flip';
mri = ft_volumereslice(cfg, mri);
cfg = [];
ft_sourceplot(cfg, mri);
ft_determine_coordsys(mri);
%%
cfg = [];
cfg.coordsys = 'ctf';
mri_realigned = ft_volumerealign(cfg, mri);
cfg = [];
ft_sourceplot(cfg, mri_realigned);
figure
ft_determine_coordsys(mri, 'interactive', 'no');
figure
ft_determine_coordsys(mri_realigned, 'interactive', 'no');
%%
cfg = [];
cfg.spmversion = 'spm12';
cfg.output = {'scalp', 'skull', 'brain'};
mri_seg = ft_volumesegment(cfg, mri_realigned);
% this takes some 2.5 minutes
save mri_seg mri_seg
load mri_seg
cfg = [];
% cfg.method = 'projectmesh';
cfg.tissue = {'scalp', 'skull', 'brain'};
cfg.spmversion = 'spm12';
cfg.numvertices = [500 1000 1500];
mesh = ft_prepare_mesh(cfg, mri_seg);
%%
figure
hold on
ft_plot_mesh(mesh(1), 'edgecolor', 'none', 'facecolor', 'r');
ft_plot_mesh(mesh(2), 'edgecolor', 'none', 'facecolor', 'b');
ft_plot_mesh(mesh(3), 'edgecolor', 'none', 'facecolor', 'g');
alpha 0.2
ft_plot_axes(mesh);
%%
% this is a work-around for a bug in ft_electroderealig
% it does not show the labels
ft_plot_sens(elec, 'label', 'label');
% note the dense triangle at the back of the head
cfg = [];
cfg.method = 'interactive';
cfg.headshape = mesh(1);
elec_realigned = ft_electroderealign(cfg, elec);
% In the GUI: rotate 90 degrees around x, then apply
% In the GUI: rotate -90 degrees around z, then apply
% In the GUI: translate [-110 90 50], then apply and close
%%
mesh = ft_convert_units(mesh, 'm');
cfg = [];
cfg.tissue = {'scalp', 'skull', 'brain'};
cfg.conductivity = [1 1/20 1];
% cfg.method = 'bemcp';
% headmodel_bemcp = ft_prepare_headmodel(cfg, mesh);
% cfg.method = 'dipoli';
% headmodel_dipoli = ft_prepare_headmodel(cfg, mesh);
cfg.method = 'openmeeg';
headmodel_openmeeg = ft_prepare_headmodel(cfg, mesh);
save headmodel_openmeeg
%%
load headmodel_dipoli
cfg = [];
cfg.resolution = 10;
cfg.unit = 'mm';
cfg.headmodel = headmodel_dipoli;
sourcemodel = ft_prepare_sourcemodel(cfg);
figure
ft_plot_mesh(sourcemodel);
figure
ft_plot_mesh(sourcemodel.pos(sourcemodel.inside,:));
ft_plot_axes(sourcemodel);
cfg = [];
cfg.grid = sourcemodel;
cfg.headmodel = headmodel_dipoli;
cfg.elec = elec_realigned;
leadfield = ft_prepare_leadfield(cfg);
%%
cfg = [];
cortex_lh = ft_read_headshape('FREESURFER_SURF/surf/lh.pial');
cortex_rh = ft_read_headshape('FREESURFER_SURF/surf/rh.pial');
figure
ft_plot_mesh(cortex_lh);
ft_plot_mesh(cortex_rh);
ft_plot_axes(cortex_lh);
ft_plot_mesh(sourcemodel.pos(sourcemodel.inside,:));
% This shows that the cortical sheet is currently not coregistered
% with the headmodel and electrodes. Also the sensity (number of
% vertices) is too high. See https://www.fieldtriptoolbox.org/tutorial/sourcemodel
% for the preferred pipeline.