To fit dipole models to ICA components in an EEGLAB dataset,
you first need to perform ICA decomposition and then select the components to be fitted.
To use DIPFIT to fit independent component maps
for an EEGLAB dataset,
you must first build or load the dataset, import
a channel location file (see the EEGLAB tutorial on Importing
a Channel Location File) and compute an ICA decomposition of
your data (see
Performing Independent Component Analysis of EEG data).
To follow the dipole fitting example used in this tutorial, download a 69-channel sample dataset for DIPFIT and BESA dipole localization here: eeglab_dipole.set (>1 MB).
This sample dataset contains a channel location file and pre-computed ICA weights.
Note: to save space, this dataset has been reduced to only a single trial,
after performing ICA on about 800 similar trials;
You should not try to apply ICA to this dataset,
but should instead use the pre-processed ICA weight matrix
to test dipole localization.
Load the dateset into EEGLAB using menu item
File > Load Existing Dataset.
Next, you must select which component to fit. Independent component (IC) 5
of the sample dataset decomposition is a typical lateral posterior alpha rhythm process.
To plot component scalp map, use menu item
Plot > Component maps > In 2-D,
enter 5 for the component number and option
'electrodes', 'pointlabels' to obtain the plot below. Note that
the channel location file for this subject has been scanned using the
Polhemus system, so the electrode locations are not exactly symmetrical.
There are three steps required to create equivalent dipole models
for independent components:
- Setting model and preferences: This involves choosing the model
(spherical or boundary element) and
excluding some channels from the fitting procedure
(e.g., eye channels).
- Grid scanning: This involves scanning possible positions in a coarse 3-D grid
to determine an acceptable starting point for fitting equivalent dipoles to each component.
- Non-linear interactive fitting: This involves running an optimization algorithm
to find the best position for each equivalent dipole.
Below we describe these three steps in detail.
Note that the grid scanning and non-linear optimization
may also be performed automatically for a group of selected components.
This is described later in this chapter.
A3.2. Setting up DIPFIT model and preferences
Before running DIPFIT, we must select some input parameters. Select the EEGLAB menu item
Tools > Locate dipoles using DIPFIT > Head model and settings
to modify DIPFIT settings. This will pop up the window below:
The top edit box, "
Model (click to select)," specifies the type of head
model -- spherical or boundary element (BEM). By default, the spherical head model
uses four spherical surfaces (skin, skull, CSF, cortex) to model the brain. The
BEM model is composed of three 3-D surfaces (skin, skull, cortex) extracted
from the MNI (Montreal Neurological Institute) caconical template brain also
used in SPM (see
here
for details, the brain used is referred to as "collin27"). In many case, the BEM model is more realistic than the
four concentric spheres model, and will return more accurate
results. However the BEM model is a numerical model and sometimes leads to incorrect solutions due to numerical instabilities, whereas
the spherical model is an analytical model. Furthermore, the spherical model returns results that are comparable with the BESA tool
(see validation study
below).
For this example, select
Boundary element model.
Clicking on the model name updates the fields below. The entry
"
Model file" contains the head model parameters (surface
information, conductances, etc...). These are Matlab files and
may be edited. See the FieldTrip
documentation for more information on
the head model files. The entry "
Output coordinates" contains the format
of the output dipole coordinates. Note that all dipoles are always
plotted in MNI/Talairach coordinates. These parameters should only
be updated for custom brain models.
The entry "MRI" contains the
name of the MRI image to plot dipoles within. You may enter a custom or
individual subject MR image file, assuming this file has first been normalized
to the MNI brain. See a tutorial page on
How to normalize an MR brain image to the MNI brain template using SPM2. Note that the
SPM2 software directory must be in your Matlab path
to use the resulting file in DIPFIT.
The entry
"
Template channel .loc file" contains the name of the template
channel location file associated with the head model. This information is
critical, as your dataset channel location file may be different from
the template. If so, a co-registration
transform is required to align your channel locations
using the template locations associated with the model. Before
we move on to co-registration, note the last edit box
"
Omit channels for dipole fit". By pressing
List, a list
of channels appears that allows users to exclude eye channels
(or other, possibly non-head channels)
from the dipole fitting procedure.
Channel co-registration is the most important part and critical part
of the DIPFIT setting procedure. If your channel locations are not aligned with the
surface of the selected head model, the coordinates of the dipoles returned by
DIPFIT will be meaningless.
Solution 1 (required for fitting the sample data):
If all your electrode locations are within the International 10-20 System,
the easiest way to
align your channels to the head model
is to simply use the standard channel coordinates associated
with the head model. Here, no co-registration is required.
To do this, press the "No coreg" checkbox
and close the DIPFIT settings window (by pressing "OK").
Then go to the channel editing window (select menu item Edit > Channel location).
The resulting channel editor window is shown below:
Press the "
Look up locs" to look up your channel locations
(by matching the channel labels) in the template channel location file.
If you had wanted to use the template channel locations
for the spherical model, you would have selected the first option in the pop-up
menu "
Use BESA file for four-shell DIPFIT spherical model".
If you want to use the template channel locations for the BEM model,
scroll down the pop-up menu and click on the button labeled
"
Use MNI coordinate file for the BEM DIPFIT model",
then press "
OK. At this point you may perform coarse grid scanning
and non-linear fine fitting as explained in the next section.
Solution 2: If you are using channel locations and/or labels
not in the International 10-20 System --
for example, scanned electrode positions, or
some commercial high-density electrode cap file --
you will need to align or co-register your electrode locations
with the selected head model. DIPPLOT does not actually allow you
to align your electrode locations to the head model itself, but rather
allows you to align your electrode locations to matching template electrode locations
associated with the head model.
To try this,
click on "Manual coreg." in the DIPFIT settings window.
The following instruction window will first appear:
If you have selected the spherical head model and pressed "
OK,"
the following co-registration window will appear. Here,
the electrode locations are plotted on the sphere that represents the head.
Note the schematic nose on the lower right; this will rotate with the head sphere.
Each small red or green sphere indicates an electrode location, with fiducial locations
(conventionally, the nasion and ear canal centers) drawn as
bigger and darker spheres (more visible in the second image below).
If you have selected the BEM model, the following window will appear:
Use the "
Warp" button to align and scale your electrode locations file
so that it becomes best aligned with the template electrode file associated with
the head model.
If you have no channels with labels that are common to the labels in the template montage,
a channel correspondance window will pop up:
The channel labels from your dataset electrode structure
are shown in the right column, while the left column shows channel labels
from the template channel file associated with the head model.
Arrows in both colums indicate electrodes with the same labels in the other column.
If your channels labels do not correspond to the International 10-20 System labels
used in the template montage, press the "
Pair channels" button
and choose the nearest channel to each of your dataset channels
in the template montage. See
here and
Oostenveld 2001 for more information about 10-20 channel locations.
When you press "
OK," the function will
perform the optimal linear 3-D warp (translation, rotation, and scaling)
to align your channel montage to the template montage associated with the head model.
The warping procedure uses the Fieldtrip toolbox which is installed by default with EEGLAB. The result will be shown in the channel montage
window (see below). You may press the "
Labels on" button to toggle
display of the channel labels for your channel structure (green) or the
template channel file associated with the head model (red). You may also
restrict the display to subsets of channels using the "
Electrodes" buttons.
Fine tuning: To finely tune the alignment manually, repeatedly
edit the values in the edit boxes. Here:
-
Yaw means rotation
in the horizontal plane around the z axis.
-
Pitch and Roll
are rotations around the x and y axes.
The resulting co-registration window should look something like this:
Note about fiducials: Your channel
structure may contain standard fiducial locations (nasion and pre-auricular points).
If you import a channel file with fiducial locations into the channel editor,
in EEGLAB v4.6- give them the standard 'fiducial' channel type "
FID"
and they will be stored in the channel information structure,
EEG.chaninfo.
This will also be done automatically if your fiducials have the standard names,
"
Nz" (nasion), "
LPA" (left pre-auricular point), and "
RPA"
(right pre-auricular point ).
Note that fiducial locations are stored outside the standard channel location
structure,
EEG.chanlocs, for compatibility with other EEGLAB plotting functions.
Thereafter, fiducial locations will appear in the channel co-registration window (above)
and may be used (in place of location-matched scalp channels) to align your electrode
montage to the template locations associated with the head model.
Use the "
Align fiducials" button to do this.
Press "
OK" to update the DIPFIT settings window. This will
display the resulting talairach transformation matrix, a vector
comprised of nine fields named
[shiftx shifty shiftz pitch roll yaw scalex scaley scalez].
Then press "
OK" in the DIPFIT settings window and proceed to localization.
A3.3. Initial fitting - Scanning on a coarse-grained grid
Before you perform interactive dipole fitting,
first allow DIPFIT to scan for the best-fitting dipole locations on a coarse 3-D grid covering the whole brain. The solutions of this grid search are not very accurate yet, but they are acceptable as starting locations for the non-linear optimalization. Starting from these best grid locations will speed up finding the final best-fitting solution.
(The next section, A3.4, explains the fine tuning using the non-linear optimization).
The tutorial instructions below are valid for both the spherical head model
and the boundary element model.
To scan dipoles on a coarse grid, select menu item
Tools > Locate dipoles using DIPFIT > Coarse fit (grid scan).
If you use the sample dataset, the window below will pop up:
The first edit box "
Component(s)" allows you to select a
subset of components to fit.
Note: selecting only a few components does not
increase the speed of the computation, since the forward model still
has to be computed at each location on the grid.
The "
Grid in X-direction," "
Grid
in Y-direction," and "
Grid in Z-direction" edit boxes allow
you to specify the size and spacing of the coarse grid. By default, DIPFIT
uses 10 steps equally spaced between -radius and +radius of the sphere that
best matches the electrode positions. Since equivalent dipoles are
not likely to be in the lower hemisphere of the model head,
by default DIPFIT only scans through positive Z values.
The last edit box, "
Rejection threshold RV(%),"
allows you to set a threshold on the maximum residual variance that is accepted. Using this threshold, components that do not resemble a dipolar field distribution will not be assigned a dipole location.
Press
OK and a bar will pop up to show the progress of the coarse grid scanning.
Note: during dipole localization, the electrode positions are projected
to the skin surface of the spherical or BEM head model, though the electrode positions as saved in the dataset
itself are
not altered.
DIPFIT starts the grid scan by first excluding all 3-D grid locations that are
outside the head.
It then computes the forward model (dipole-to-electrode projections)
at each remaining grid location and compares it with all component topographies.
The progress of this process is shown in the text window.
When the process is completed, select menu item
Tools > Locate dipoles using DIPFIT > Plot
component dipoles to call the window below:
Simply press
OK, to produce the figure below:
Here, all the dipoles plotted had a residual variance (vis a vis their component maps)
below 40% (as we specified in the coarse grid search interactive window).
Note that some components end up having the same x, y and z
coordinates because of the coarseness of the grid. You may view individual
components by pressing the "
Plot one" button. The component index
and its residual variance are then indicated in the top-left corner (see
the
dipplot() visualization tutorial
in A3.5. below for further details).
A3.4. Interactive fine-grained fitting
To scan dipoles interactively, call menu item
Tools > Locate dipoles using DIPFIT > Fine fit (iterative). The following windows pop up. Enter a component index
(here, 3) in the "
Component to fit" edit box.
Prior to fitting the component, press the "
Plot map" button to show the component scalp map. The following window pops up.
This component, showing a clear left-right symmetric activity, cannot be accurately modeled using a
single dipole. To fit this component using two dipoles constrained to
be located symmetrically across the (corpus callosum) midline,
set both dipole 1 and 2 to
active and
fit
(by checking the relevant checkboxes in the pop window). Then
press the "
Fit selected dipole position(s)" button. If fitting fails, enter different starting positions (e.g., [-10 -68 17] for first dipole and [10 -68 17] for the second dipole and refit). When fitting
is done, note the low residual variance for the two-dipole model on the top right
corner of the interactive interface (0.53 %). Then, press the "
Plot
dipole(s)" button. The following plot pops up (see the
dipplot() visualization tutorial in A3.5. below).
Note that the polarity of components is not fixed (but their
orientation is): the product of the component scalp map value and the component
activitation value (i.e., the back-projected contribution of the component to the scalp data)
is in the original data units (e.g., microvolts), but the polarities of the scalp map and of the
activation values are undetermined. For example, you may multiply both the
scalp map and the component activity by -1 while preserving the component
back-projection to the scalp (since the two negative factors cancel out).
As a result, you may choose to flip the visualized dipole orientations (use the
pop-up window for fitting, then press the "
Flip in|out" button and
then the "
Plot dipole(s)" button to re-plot the dipoles with indicated
orientations reversed.
Important note: when you are done, press
the
OK button in the interactive interface for dipole fitting. Do
not press
Cancel or close the window -- if you do, all the dipole
locations that you have computed using this interface will be lost! Also,
this DIPFIT menu is the only one that does not generate EEGLAB history.
The first menu item,
Tools > Locate dipoles
using DIPFIT > Autofit component, uses the same method
for dipole fitting but also generates an EEGLAB history command that can be
re-used in batch scripts.
A3.5. Automated dipole fitting
Automated dipole fitting performs the grid search and the non-linear fitting on several component without human intervention. You still need to select the model in the DIPFIT settings interactive window though.
To find a best-fitting equivalent dipole for the component above, select the EEGLAB menu item
Tools > Locate dipoles using DIPFIT >
Autofit (coarse fit, fine fit, plot) to automatically fit
selected ICA components. Set the "Component indices" to "5",
enter "100" in the "rejection threshold" edit box so the
iterative solution is computed regardless of the residual variance for
the coarse fit, and check the "Plot resulting dipoles" checkbox
to plot component dipoles at the end of fitting. Then, press OK.
The function starts by scanning a 3-D grid to determine acceptable starting positions for each component. Then it uses the non-linear optimization algorithm to find the exact dipole position for each component. At the end of the procedure, the following window pops up.
The residual variance "
RV: 2.75%" is shown below the component number "
Comp: 5" on the right top corner indicates that the component being plotted is component 5 and that the residual variance is 2.75%. The
diplot() function above
allows you to rotate the head model in 3-D with the mouse,
plot MRI slices closest to the equivalent dipole, etc...
(See the
dipplot() visualization tutorial in A3.6. for more details.)
A3.6. Visualizing dipole models
Use menu item
Tools > Locate dipoles using DIPFIT
> Plot component dipoles and select component number 3 and
5 to produce the plot shown below (assuming you performed the fit for both components 3 and
5 as described above). Select all options except the "
Summary
mode" and enter "
'view', [51 18]" in the "
Additional dipplot()
options" text box to set the initial 3-D view; the function will print the component indices.
A plot pops-up. After 3D rotation it may look like the following plot.
Press the "
Plot one" button. You may scroll through the components
by pressing the "
Next/Prev" buttons. Note that the closest MRI slices to the dipole being currently plotted are shown. Note also that if you do not select the option "
Plot closest MRI slices" in the graphic interface, and then press the "
Tight view" button in the dipplot window, you will be able to see the closest MRI slices at the location of the plotted dipole as shown below. Try moving the 3-D perspective
of the tight view plot with the mouse, and/or selecting the three cardinal viewing angles (sagittal, coronal, top) using the lower left control buttons.
Note that it is not yet possible to toggle the dipole "stems" or "projection lines" on and off
from within the graphic interface. You need to use the EEGLAB menu again, unselecting the relevant
checkboxes. Also, it is not yet possible to use the subject's own anatomical MRI
as background (it will be possible in the future upon completion of a EEG/MRI co-registration function
as described below).
Finally, again call menu item
Tools > Locate
dipoles using DIPFIT > Plot component dipoles. Select
"
Summary mode" and press "
OK".
For comparison, using the spherical model, the location of the dipoles would have been as shown in the following picture. Notice the similarity of the talairach coordinates for the two models. Notice also that the BEM model does not overestimate dipole depth compare to the spherical model (which is usually the case when the BEM model mesh is too coarse). The MRI used to plot the spherical result is the average MNI brain (over about 150 subjects) whereas the MRI used to plot the BEM is the average MNI brain of a single subject (which was used to generate the model leadfield matrix).
The entry "Background image" contains the name of the
MRI in which to plot dipoles. You may enter a custom MRI file
assuming this file has been normalized to the MNI brain (see How to normalize a
subject MR image to the MNI brain using SPM2. Note that
the SPM2 package directory in your network
must be in your Matlab path to be able to use
the resulting file in EEGLAB. This is
true both for the spherical head model (co-registered to the MNI brain)
and the BEM model (based on the MNI brain).
Note: Co-registration of the MNI average MR image
with landmark electrode positions for the spherical model.
To plot dipole locations in the average MR image
(as in the
dipplot()
plots above), we co-registered the MNI average brain MRI
with landmark electrode positions. For the average MRI image, we
used a publicly available average brain image (average of 152
T1-weighted stereotaxic volumes made available by the ICBM project) from the
MNI database
(Montreal Neurological Institute (MNI), Quebec).
Co-registration of the MNI brain and the standard EEG landmarks
was accomplished automatically using fiducials and the Cz (vertex) electrode position,
then slightly adjusted manually. The co-registration differs slightly
from that used in DIPFIT1; dipoles are localized about 10 mm deeper than in
DIPFIT1. Other coordinates (x,y) are nearly identical. We believe
the co-registration in DIPFIT2 to be more accurate than in DIPFIT1.
You may also see the co-registered model head sphere
plotted over the MNI brain in the
dipplot()
window by pressing the "Mesh on" button in the BEM model below (here in 'tight view').
Or, for the spherical model below (in summary mode).
Note that the sphere model is stretched in the visualization to the ellipsoid that best matches
the shape of the headsurface of the average MRI.
A3.6. Plotting dipole locations on scalp maps
Select menu item
Plot > Component maps > In
2-D, enter "
1:20" in the component index edit box.
Note: The sample dataset structure contains pre-computed component dipoles.
If you are processing another dataset, first select menu item
Tools > Locate dipoles using DIPFIT > Autofit
components to fit ICA components from 1 to 20 automatically as
described in section A1.1. above.
Check the "
Plot dipole" checkbox (see below)
and press
OK.
Note: You may also enter additional dipole fitting options in the
last edit box; press the
Help button for details.
The following plot will pop up.
A3.7 Using DIPFIT to fit independent MEG components
Dipfit (v2.1) now supports source localization for MEG data acquired using CTF (Vancouver, CA) systems. Note that a custom head model needs to be used for MEG source localization since it is not possible to do accurate source localization on a template head model from MEG data. The main reason for this limitation is that, first, MEG sensor positions do not follow any positioning standard. Each system may have its own arrangement. Furthermore, unlike in EEG the sensors are not placed on an elastic cap that automatically fits to the head of the subject. The MEG sensor coils are located within a fixed helmet in which the majority of subjects' heads fit loosely. The position of a given sensor relative to a cortical structure could be quite different from subject to subject, and even across different recordings from the same subject. For this reason, the sensor's location is always reported relative to the center and the orientation of the subject's head during the recording, which is never exactly the same across recordings.
Consequently, a head model needs to be created for each recording using the CTF MRIViewer and localSphere programs (see the CTF documentation). Then, in EEGLAB, call menu item Tools > Locate dipoles using DIPFIT > Head model and settings, and select the tab CTF in the upper pop-window. Set up the DIPFIT model and preferences as follows:
- Select CTF as model.
- The model file is the file 'default.hdm' created by CTF localSphere, located in the 'xxx.ds' folder.
- The MRI head image should be the 'xxx.mri' file used by MRIViewer to extract the cortical surface.
- The channel file should be the 'xxx.res4' file within the 'xxx.ds' folder. This file also contains gradiometer-specific information, including sensor orientation, that is not in the EEG structure.
As for EEG data, you first need to scan a relavely coarse grid, to find an appropriate starting position for each dipole, by calling menu item Tools > Locate dipoles using DIPFIT > Coarse fit (grid scan). Note that CTF head models are in cm. Consequently, the grid used for gridsearch needs to be specified in cm (this is done automatically by pop_dipfit_gridsearch). Then as in EEG dipole fitting, use menu item Tools > Locate dipoles using DIPFIT > Fine fit (iterative) to optimize dipole positions nonlinearly.
Finally, you may plot dipoles on the subject MRI using menu item Tools > Locate dipoles using DIPFIT > Plot component dipoles as shown below. The corresponding scalp map is also shown on the right. Because of co-registration issues, it is not possible to plot the dipole positions on the scalp map as in EEG). It is strongly advisable to normalize dipole lengths.
You can also run dipfit2.1 for MEG data without a subject MR image if
you have extracted the subject head surface from another source, like a
head model acquired using a Polhemus 3-D location system.
However, it would then not be possible to visualise the dipole within EEGLAB.
Any comparison between or drawing of dipoles for different subjects requires a thourough understanding of the MEG coordinate system, since that is being used to express the dipole position. The default is in individual subjects' head coordinates, and given that head sizes differ between subject, the position of dipoles cannot be directly compared over subjects. It is therefore advisable to coregister and spatially normalize your subjects' MRIs to a common template (such as the MNI template) and to apply the same coregistration and normalization to the dipole positions before comparing them.
These operations need to be executed outside EEGLAB.
A test set is available here. Simply load the test file and
follow the instruction above. Thanks to Nicolas Robitaille for his efforts to make this
new feature work, in documenting it, and providing test data.
A3.8 Using DIPFIT to fit EEG or ERP scalp maps
Though the implementation of the DIPFIT plug-in has not been expressly designed
to fit dipoles to raw ERP or EEG scalp maps,
EEGLAB provides a command line function allowing DIPFIT to do so. Fitting
can only be performed at selected time points, not throughout a time window. First, you must specify the DIPFIT
settings on the selected dataset (see section A3.2. Setting up DIPFIT model and preferences).
Then, to fit a time point at 100 ms in an average ERP waveform (for example)
from the main tutorial data set
(eeglab_data_epochs_ica.set),
use the following Matlab commands.
% Find the 100-ms latency data frame
latency = 0.100;
pt100 = round((latency-EEG.xmin)*EEG.srate);
% Find the best-fitting dipole for the ERP scalp map at this timepoint
erp = mean(EEG.data(:,:,:), 3);
EEG = pop_dipfit_settings(EEG); % Follow GUI instructions to perform co-registration
[ dipole model TMPEEG] = dipfit_erpeeg(erp(:,pt100), EEG.chanlocs, 'settings', EEG.dipfit, 'threshold', 100);
% Show dipole information (spherical or MNI coordinate based on the model selected)
dipole
% plot the dipole in 3-D
pop_dipplot(TMPEEG, 1, 'normlen', 'on');
% Plot the dipole plus the scalp map
figure; pop_topoplot(TMPEEG,0,1, [ 'ERP 100ms, fit with a single dipole (RV ' num2str(dipole.rv*100,2) '%)'], 0, 1);
Click here to download the script above.
A3.9. DIPFIT structure and functions
Like other EEGLAB functions, DIPFIT functions are standalone and may
also be called from the command line. Note that whenever you call the DIPFIT
menu from EEGLAB, a text command is stored in the EEGLAB history as
for any other EEGLAB command. Type "h" on the Matlab command line to view the
command history.
DIPFIT creates a EEG.dipfit sub-structure within the main EEG dataset structure.
This structure has several fields:
| EEG.dipfit.chansel | Array of selected channel indices to fit (for instance ECG or EYE channels might be excluded). |
| EEG.dipfit.current | Index of the component currently being fitted. |
| EEG.dipfit.hdmfile | Model file name. Contains
informatin about the geometry and the conductivity of the head. This is a standard Matlab file and
may be edited as such; for the default spherical model the conductivities and radii are the
same as in BESA. |
| EEG.dipfit.mrifile | Contains the MRI file used for plotting dipole locations. |
| EEG.dipfit.chanfile | Contains the template channel
location file associated with the current head model. This is used for
electrode co-registration. |
| EEG.dipfit.coordformat | Contains the coordinate
format in the model structure. This is "spherical" for the spherical
model or "MNI" for the BEM model. |
| EEG.dipfit.coord_transform | Contains the
talairach transformation matrix to align the user dataset channel location
structure (EEG.chanlocs) with the selected head model.
This is a length-9 vector [shiftx shifty shiftz pitch roll yaw scalex scaley scalez].
Type ">> help traditional" in Matlab for more information. |
| EEG.dipfit.model | A structure array
containing dipole information. |
| EEG.dipfit.model.posxyz | Contains the 3-D position of the dipole(s).
If a two-dipole model is used for a given component,
each row of this array contains the location of one dipole. |
| EEG.dipfit.model.momxyz | Contains 3-D moment of the dipole(s).
If a two-dipole model is used for a given component,
each row of this array contains the moment of one dipole. |
| EEG.dipfit.model.rv | Gives the relative residual variance between the dipole potential distribution and th ecomponent potential distribution, ranging from 0 to 1. |
| EEG.dipfit.model.active | Remembers which dipole was active
when the component model was last fitted. |
| EEG.dipfit.model.select | Remembers which dipole was selected
when the component model was last fitted.
|
The main DIPFIT functions are:
| pop_dipfit_settings() |
Specify DIPFIT parameters, i.e. head model details. |
| pop_dipfit_gridsearch() |
Perform initial coarse DIPFIT grid scan to find an acceptable starting positions
for further fine fitting. |
|
pop_dipfit_nonlinear()
| Perform
fine model fitting via an interactive interface using non-linear optimization.
Note: This function cannot be called from the command line.
For automated processing, use the function below. |
| pop_multifit() |
Command the DIPFIT modeling process from a single function. This function
allows the user to set parameters, initialize locations on a coarse grid,
then perform non-linear optimization as in the separate functions above.
This function can be used to automate batch fitting of components from one or more subjects.
|
Auxiliary DIPFIT functions used by the functions above:
Note: There is no auxiliary "dipfit_settings" function,
since selecting DIPFIT settings mostly involves copying default values to
EEG.dipfit fields.
A3.10. DIPFIT validation study using the spherical head model
We (AD) checked the results of fitting equivalent dipoles with DIPFIT (spherical model)
against results of BESA(3.0) for independent components of
data recorded in a working memory task in our laboratory. There were 72 channels,
of which 69 were used for dipole fitting. In particular, we excluded tow EOG channels
and the reference channel.
We performed ICA on data from three subjects and fit all 69 resulting components
with single dipoles using BESA (see the BESAFIT plug-in Appendix) and DIPFIT.
We then compared only those dipoles whose residual variance was less than 15%
(see plot below).
Distances between equivalent dipole locations returned by DIPFIT1 and BESA(3.0)
were less than 3 mm (left panel)
Note: The outliers were infrequent failures to converge by either algorithm.
Dipole angle differences were below 2 degrees (middle panel). Residual
variances (mismatch between the component scalp map and the model pole projection)
were near equal (right panel). A few dipoles were localized with residual variance below
15% by DIPFIT but not by BESA (4 of 213 components); a few others
with residual variance below 15% by BESA but not by DIPFIT (8
of 213 components). Optimization nonlinearities may account for these
small differences between the two algorithms, as the solution
models used in the two programs are theoretically identical.
The main advantage of using DIPFIT over BESA for localization of equivalent dipoles
for independent component scalp maps is that it is integrated into Matlab and EEGLAB,
and can be used in batch processing scripts (see A3.8
and A3.9). BESA has additional capabilities not relevant
to DIPFIT. Succeeding versions of BESA did not allow a batch processing option.
A3.11. Literature references
M. Scherg, "Fundamentals of dipole source potential analysis". In: "Auditory evoked magnetic fields and electric potentials" (eds. F. Grandori, M. Hoke and G.L. Romani). Advances in Audiology, Vol. 6. Karger, Basel, pp 40-69, 1990.
R. Kavanagh, T. M. Darcey, D. Lehmann, and D. H. Fender. "Evaluation of methods for three-dimensional localization of electric sources in the human brain." IEEE Trans Biomed Eng, 25:421-429, 1978.
T.F. Oostendorp and A. van Oosterom, "Source parameter estimation in inhomogeneous volume conductors of arbitrary shape", IEEE Trans Biomed Eng. 36:382-91, 1989.
R. Oostenveld and P. Praamstra. The five percent electrode system for high-resolution EEG and ERP measurements. Clin Neurophysiol, 112:713-719, 2001.
