A06: EEGLAB option menu
This section is intended for users who use large EEGLAB data-sets and need to optimize their use of main memory (RAM) and disk space. To modify the relevant EEGLAB options, select File > Maximize memory. If you cannot modify the file eeg_options.m in the toolbox distribution, the window below will pop up.
Simply press Yes. A copy of eeg_options.m will be stored in the local directory and modified when you change any of the EEGLAB options. (Note: For EEGLAB to use this file, the current directory (.) must appear BEFORE the EEGLAB toolbox directory in the Matlab path; see path()). If the original eeg_option.m file is writable, EEGLAB will modify it instead.
If the original distribution copy of the options file is modified, the new options will affect all EEGLAB sessions using that file. If, however, EEGLAB (or the user from the Matlab or shell commandline) copies the options file to the current working directory, then only EEGLAB sessions having this directory in their Matlab path (before the EEGLAB distribution directory) will follow the modified options. It is advised to have only one such option file to avoid confusion. Now the following window will pop up.
Group processing options
The top options are for processing multiple datasets in EEGLAB STUDY. When the top option is set, EEGLAB can hold in memory more than one data-set at a time. New data-sets are created by most of the operations called under the Tools menu. With this option set, users can undo data-set changes immediately by going back to working with the parent (previous) data-set (unless they set the "overwrite data-set" option when they saved the new data-set). Processing multiple data-sets may be necessary when comparing data-sets from separate subjects or experimental conditions.
The second option allow to save the raw data in a separate file. This will be useful in future version of EEGLAB to read one channel at a time from disk. This also allow faster reading of data-set when they are part of a STUDY.
If the 3rd option is set, all the ICA activations (time courses of activity) will be saved on disk to save computation time.
The following options maximize memory usage.
The fist option forces EEGLAB to using single precision number. Unless you have a good reason to do so, you should leave that checkbox checked.
The second option allow to process EEG dataset directly on disk, as this is done for fMRI. This should remove any constraint on file size. However, unlike SPM which is used to process fMRI data, EEGLAB was not originally designed with this type of processing in mind. To be able to use all of the EEGLAB functions that use passage of parameters by value, we had to find a way to pass the data by reference, something that is usually not possible in Matlab. This means that we had to implement some hacks. We have a series of test (about 40) that check that the hack functions are doing what they are supposed to do. However, it is hard to guarantee that this implementation is bug free especially since it is not the default implementation and not heavily used by users which would help track potential bugs. Nevertheless this implementation passed the about 5000 EEGLAB independent test cases. It is quite unlikely that results of computation will be corrupted. Instead you can expect some rare functions will return an error while they should not.
The third option allow to use EEG objects instead of EEG structures. From the user perspective, it will not change anything. However, it allows EEGLAB to process objects that are not native to EEGLAB. For example, another software could use EEGLAB function by passing on an object as long as this objects behaves a specific way. This option should only be used by expert users.
The first option allow to pre-computed the ICA component activities. This may nearly double the main memory (RAM) required to work with the data-set when you have ICA components. Otherwise, ICA activations will be computed by EEGLAB only as needed, e.g. each time EEGLAB calls a function that requires one or more activations. In this case, only the activations needed will be computed, thus using less main memory.
The second option scales ICA component activities to RMS microvolt. This scaling does not change anything in terms of data processing. When scaling ICA component activities, ICA scalp topographies are scaled as well so the product of the two remains constant. This scaling was not performed in early version of EEGLAB. There is no reason to uncheck that option unless you want to preserve backward compatibility with early versions of EEGLAB.
Folder, Matlab toolboxes, and EEG connectivity and support options
The folder option is used to remember folder when reading data-sets. The next option about using Matlab toolboxes allow to ignore such toolboxes even if they are present in the path. This may be useful when your university has reached its quota in terms of toolbox users. In this case, the extra toolbox functions exist in the path but you may not use them. The last 2 options pertain to EEGLAB connectivity, checking for new version of EEGLAB and allowing to use the EEGLAB chat (currently beta).
The icadefs.m file
Using a text editor, you should edit file "icadefs.m" in the distribution before beginning to use EEGLAB. This file contains EEGLAB constants used by several functions. In this file, you may:
- Specify the filename directory path of the EEGLAB tutorial. Using a local copy of this EEGLAB tutorial (available online at the SCCN EEGLAB wiki requires a (recommended) Tutorial Download (obsolete).
TUTORIAL_URL = 'http://sccn.ucsd.edu/wiki/EEGLAB'; % online version
- Reference the fast binary version of the runica() ICA function ica (see the Binica Tutorial). This requires another (recommended) download from the SCCN EEGLAB site.
ICABINARY = 'ica_linux2.4'; % <=INSERT name of ica executable for binica.m
You can also change the colors of the EEGLAB interface using other options located in the icadefs file.
The dipfitdefs.m file
The dipfitdefs.m contains other constants pertaining to dipole localization, default models, default electrode files.