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.

Program

Wednesday morning

  • 1h - welcome and intro lecture - slides
  • 2h - hands-on on preprocessing of EEG data.

Thursday morning

  • 3h - preprocessing of Cuban EEG dataset (eyes opened/closed and hyperventilation)

Friday morning

  • 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.

Getting started with the hands-on sessions

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).

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 FTP 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

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.

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.

Script for the Cuban EEG dataset

%%

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 http://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 http://www.fieldtriptoolbox.org/tutorial/sourcemodel
% for the preferred pipeline.