What is EEGLAB?
EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data using independent component analysis (ICA), time/frequency analysis, and other methods including artifact rejection. First developed on Matlab 6.1 under Linux, EEGLAB runs on Matlab versions 6 (R13) and 7 (R14) under Linux or Unix, Windows, and Mac OS (Matlab v6 or current v7 recommended).
EEGLAB provides an interactive graphic user interface (gui) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users' transition from gui-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics. For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG, MEG, or other electrophysiological data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by contributing EEGLAB 'plug-in' functions that appear automatically in the EEGLAB menu. For example, EEGLAB is also being used for analysis of MEG data in several laboratories; EEGLAB plug-in functions might be created and released to perform specialized import/export, plotting and inverse source modeling for MEG data.
This tutorial will demonstrate (hands-on) how to use EEGLAB to interactively preprocess, analyze, and visualize the dynamics of event-related EEG, MEG, or other electrophysiological data by operating on the tutorial EEG dataset eeglab_data.set which you may download here (4Mb). With this dataset, you should be able to reproduce the sample actions discussed in the tutorial and get the same (or equivalent) results as shown in the many results figures.
EEGLAB functions may be roughly divided into three layers designed to increase ease-of-use for different types of users. These three layers are described below.
The EEGLAB graphic user interface (gui)
The EEGLAB gui is designed to allow non-experienced Matlab users to apply advanced signal processing techniques to their data. However, more experienced users can also use the gui to save time in writing custom and/or batch analysis scripts in Matlab by incorporating menu shortcuts and EEGLAB history functions.
Text files containing event and epoch information can be imported via the EEGLAB menu. The user can also use the menu to import event and epoch information in any of several file formats (Presentation, Neuroscan, ASCII text file), or can read event marker information from the binary EEG data file (as in, e.g., EGI, Neuroscan, and Snapmaster data formats). The menu then allows users to review, edit or transform the event and epoch information. Event information can be used to extract data epochs from continuous EEG data, select epochs from EEG data epochs, or to sort data trials to create ERP-image plots (Jung et al., 1999; Makeig et al., 1999). EEGLAB also provides functions to compute and visualize epoch and event statistics.
New Matlab users may choose to interact only with the main EEGLAB window menu, first to import data into EEGLAB (in any of several supported formats), and then to call any of a large number of available data processing and visualization functions by selecting main-window menu items organized under seven headings:
- File menu functions read/load and store/save datasets and studysets.
- Edit menu functions allow editing a dataset, changing its properties, reviewing and modifying its event and channel information structures.
- Tools menu functions extract epochs from continuous data (or sub-epochs from data epochs), perform frequency filtering, baseline removal, and ICA, and can assist the user in performing semi-automated artifact data rejection based on a variety of statistical methods applied to activity in the raw electrode channels or their independent components.
- Plot menu functions allow users to visualize the data in a variety of formats, via (horizontally and vertically) scrolling displays or as trial (ERP), power spectrum, event-related time/frequency averages, etc. A large number of visualization functions are dedicated to the display and review of properties of scalp data channels and underlying independent data components. The user can make use of standard Matlab capabilities to edit, print, and/or save the resulting plots in a variety of formats.
- Study menu entries show the current studyset and which of its constituent datasets are currently loaded.
- Datasets menu entries list loaded datasets, and allows the user to swtich back and forth among them.
- Help menu functions allow users to call up documentation on EEGLAB functions and data structures, including function lists and scrolling function help messages.
EEGLAB structures and command history
EEGLAB uses a single structure (EEG) to store data, acquisition parameters, events, channel locations, and epoch information as an EEGLAB dataset. This structure can also be accessed directly from the Matlab command line. The EEG structure contains two key sub-structures: EEG.chanlocs, holding channel locations, and EEG.event storing dataset event information. In EEGLAB v5.0 (March, 2006), a new superordinate structure, the STUDY has been introduced to allow automated processing of a group of EEG datasets. The first such facility introduced is a set of study functions to perform and evaluate clustering of similar independent data components across subjects, conditions, and sessions.
Intermediate level users may first use the menu to perform a series of data loading, processing and visualization functions, and then may take advantage of the EEGLAB command history functions to easily produce batch scripts for processing similar data sets. Every EEGLAB menu item calls a Matlab function that may also be called from the Matlab command line. These interactive functions, called "pop" functions, work in two modes. Called without (or in some cases with few) arguments, an interactive data-entry window pops up to allow input of additional parameters. Called with additional arguments, "pop" functions simply call the eponymous data processing function, without creating a pop-up window. When a "pop" function is called by the user by selecting a menu item in the main EEGLAB window, the function is called without additional parameters, bringing up its gui pop-up window to allow the user to enter computation parameters. When the processing function is called by EEGLAB, its function call is added as a command string to the EEGLAB session history variable. By copying history commands to the Matlab command line or embedding them in Matlab text scripts, users can easily apply actions taken during a gui-based EEGLAB session to a different data set. A comprehensive help message for each of the "pop" functions allows users to adapt the commands to new types of data.
Custom EEGLAB and MATLAB scripting
More experienced Matlab users can take advantage of EEGLAB functions and dataset structures to perform computations directly on datasets using their own scripts that call EEGLAB and any other Matlab functions while referencing EEGLAB data structures. Most "pop_" functions describe above call signal processing functions. For example, the pop_erpimage() function calls signal processing and plotting function erpimage(). Since all the EEGLAB data processing functions are fully documented, they can be used directly. Experienced users should benefit from using all three modes of EEGLAB processing: gui-based, history-based, and autonomously scripted data analyses. Such users can take advantage of the data structure (EEG) in which EEGLAB datasets are stored. EEGLAB uses a single Matlab variable, a structure, EEG, that contains all dataset information and is always available at the Matlab command line. This variable can easily be used and/or modified to perform custom signal processing or visualizations. Finally, while EEGLAB "pop" functions (described above) assume that the data are stored in an EEG data structure, most EEGLAB signal processing functions accept standard Matlab array arguments. Thus, it is possible to bypass the EEGLAB interface and data structures entirely, and directly apply the signal processing functions to data matrices.
Distribution, documentation and support
The EEGLAB toolbox is distributed under the GNU General Public License (for details see http://www.gnu.org/licenses/gpl.txt). The source code, together with web tutorials and function description help pages, is freely available for download from http://sccn.ucsd.edu/eeglab/. The toolbox currently includes well over 300 Matlab functions comprising more than 50,000 lines of Matlab code. This user tutorial explains in detail how to import and process data using EEGLAB, including the derivation and evaluation of its independent components. SCCN also provides "Frequently Asked Questions (FAQ)" and "Known Bugs" web pages, a support email (firstname.lastname@example.org), a dedicated mailing list for software updates (email@example.com), and an email discussion mailing list (firstname.lastname@example.org), which currently reaches over two thousand EEG researchers.
Open-source EEGLAB functions are not precompiled; users can read and modify the source code of every function. Each EEGLAB function is also documented carefully using a standardized help-message format and each function argument is described in detail with links to related functions. We have attempted to follow recognized best practice in software design for developing EEGLAB. The source code of EEGLAB is extensively documented and is internally under the Linux revision control system (RCS), which allows us to easily collaborate with remote researchers on the development of new functions. Matlab allows incremental design of functions, so adding new features to a function can be easily accomplished while preserving backward compatibility.
(Adapted from, A Delorme & S Makeig. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods 134:9-21 (2004)).