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The EEGLAB Tutorial

The tutorial is its final phase for wiki conversion. Refer to the EEGLAB wiki page for latest changes in the documentation.

Tutorial overview

I. Analyzing data in EEGLAB
II. Importing/exporting data
III. Rejecting artifacts
IV. Writing EEGLAB scripts
V. Event processing
VI. Independent component clustering

A1. Dataset data structures
A2. Maximizing memory
A3. How to contribute to EEGLAB
A4. The DIPFIT plug-in: Equivalent dipole source localization
A5. The MI-clust plug-in: Clustering dataset ICs using mutual information


Detailed tutorial outline

Download the full tutorial in pdf on this page

I. Analyzing data in EEGLAB

What is EEGLAB?
Download tutorial dataset (4.1Mb)
EEGLAB overview

1. Loading data and visualizing data information

1.1. Getting started
    1.1.1. Learning Matlab
    1.1.2. Installing EEGLAB and tutorial files
    1.1.3. Starting Matlab and EEGLAB
1.2. Opening an existing dataset
1.3 Editing event values
     1.3.1. Sample dataset description
1.4. Annotating datasets
1.5. Scrolling through the data

2. Using channel locations

2.1. Importing channel locations - the tutorial dataset
2.2. Retrieving standard channel locations
2.3. Importing a measured channel location file

3. Plotting channel spectra and maps
4. Preprocessing tools

4.1. Changing the data sampling rate
4.2. Filtering the data
4.3. Re-referencing the data

5. Extracting data epochs

5.1. Extracting epochs
5.2. Removing baseline values

6. Data averaging

6.1. Plotting the ERP on a single axis with scalp maps
6.2. Plotting channel ERPs in a topographic array
6.3. Plotting channel ERPs in a rectangular array
6.4. Plotting an ERP scalp map series
       6.4.1. Plotting a series of 2-D ERP scalp maps
       6.4.2. Plotting a series of 3-D ERP scalp projections

7. Selecting data epochs and comparing data averages

7.1. Selecting events and epochs for two conditions
7.2. Computing grand mean ERPs
7.3. Finding ERP peak latencies
7.4. Comparing ERPs for two conditions

8. Plotting ERP images

8.1. Selecting a channel to plot
8.2. Plotting ERP images with pop_erpimage()
8.3. Sorting trials in ERP images
8.4. Plotting ERP images: spectral options
8.5. Plotting spectral amplitude in single trials: additional options

9. Performing independent component analysis

9.1. Running ICA decompositions
9.2. Plotting 2-D component scalp maps
9.3. Visualizing 3-D component scalp projections
9.4. Examining and removing ICA components
9.5. Subtracting ICA components from the data
9.6. Retaining several ICA weights in a dataset
9.7. Scrolling through component activations

10. Working with ICA components

10.1. Rejecting data epochs using ICA
10.2. Plotting component spectra and scalp topographies
10.3. Plotting component ERPs
10.4. Plotting component ERP contributions
10.5. Plotting component ERP images

11. Computing time/frequency transforms

11.1. Decomposing channel data
11.2. Computing component time/frequency transforms
11.3. Computing component cross-coherences
11.4. Plotting ERSP time courses and topographies

12. Processing multiple datasets

II. Importing/exporting data

1. Importing continuous data

1.1. Importing Matlab array or .mat file data
1.2. Importing Biosemi .BDF file data
1.3. Importing European data format (.EDF) data
1.4. Importing continuous EGI .RAW data
1.5. Importing .CNT Neuroscan continuous files
1.6. Importing .DAT Neuroscan data
1.7. Importing .SMA SnapMaster data
1.8. Importing ERSPP .RAW and .RDF data
1.9. Importing Brain Vision Analyser Matlab data
1.10. Importing data in other formats

2. Importing event information for continuous data

2.1. Importing events from a data channel
2.2. Importing events from a Matlab array or text file
2.3. Importing events from a Presentation file

3. Importing sets of single-trial epochs

3.1. Importing .RAW EGI data epoch files
3.2. Importing Neuroscan .EEG data epochs
3.3. Importing epoch information from a Matlab array or text file

4. Importing sets of data averages

4.1. Importing data into Matlab
4.2. Concatenating data averages
4.3. Importing concatenated data averages

5. Exporting data and ICA matrices

5.1. Exporting data to an ASCII text file
5.2. Exporting ICA weights and inverse weight matrices

III. Rejecting data artifacts

1. Rejecting artifacts from continuous data

1.1. Rejecting data by visual inspection
1.2. Rejecting data channels by channel statistics

2. Rejecting artifacts from epoched data

2.1. Rejecting data epochs by visual inspection
2.2. Rejecting extreme values
2.3. Rejecting abnormal trends
2.4. Rejecting improbable data
2.5. Rejecting abnormally distributed data
2.6. Rejecting abnormal spectra
2.7. Comparing current and previously proposed rejections
2.8. Inspecting results of all rejection measures
2.9. Notes and strategy

3. Rejecting data using independent components

IV. Writing EEGLAB scripts

1. Why write EEGLAB scripts?

2. Using dataset history

2.1. Dataset history
2.2. EEGLAB pop_ functions
2.3. Example scripts using dataset history
2.4. Updating the EEGLAB window

3. Using session history

3.1. The 'heeg' command
3.2. Example scripts using session history
3.3. Using EEGLAB structures

4. Basic scripting examples

5. Intermediate scripting examples

5.1. Processing multiple datasets
5.2. Performing time-frequency decomposition of all channels
5.3. Animating a scalp map

V. Event processing

1. Processing events from the menu

1.1. Event fields
1.2. Using the menu to process events

2. Processing events from the Matlab command line

2.1. Accessing events from the command line
2.2. The EEG.epoch structure
2.3. Example script using EEG.epoch

VI. Independent component clustering

1. Component clustering

1.1. Why cluster?
1.2. Before clustering
1.3. Clustering outline

2. Clustering from the graphic interface

2.1. Creating a new studyset
2.2. Loading an existing STUDY
2.3. Preparing to cluster (pre-clustering)
2.4. Finding clusters
2.5. Viewing clusters
2.6. Editing clusters
2.7. Hierarchic sub-clustering
2.8. Editing STUDY datasets

3. STUDY data visualization tools

4. Study statistics and visualization options

4.1. Parametric and non-parametric statistics
4.2. Options for computing statistics on and plotting results for scalp channel ERPs
4.3. Computing statistics for studies with multiple groups and conditions

5. EEGLAB study data structures

5.1. The STUDY structure
5.2. The STUDY.datasetinfo sub-structure
5.3. The STUDY.cluster sub-structure
5.4. The STUDY.changrp sub-structure

6. Command line STUDY functions

6.1. Creating a STUDY
6.2. Component clustering and pre-clustering
6.3. Visualizing component clusters
6.4. Computing and plotting channel measures
6.5. Plotting statistics and retrieving statistical results
6.6. Modeling condition ERP differences using std_envtopo()

A1. Dataset data structures

1. EEG and ALLEEG
2. EEG.chanlocs
3. EEG.event
4. EEG.epoch

A2. Options for maximizing memory and disk space

1. Maximize memory menu
2. The icadefs.m file

A3. How to contribute to EEGLAB

1. Open source policy
2. How to write an EEGLAB function
3. How to write an EEGLAB plugin

A4. The DIPFIT plug-in: Equivalent dipole source localization of independent components

1. Automated dipole fitting
2. Setting up DIPFIT parameter values
3. Initial coarse-grained fitting - Scanning on a grid
4. Interactive fine-grained fitting
5. Visualizing dipole models
6. Plotting dipole locations on scalp maps
7. Using DIPFIT to fit MEG components
8. Using DIPFIT to fit EEG or ERP scalp maps
9. DIPFIT structure and functions
10. DIPFIT Validation study
11. Literature References

A5. The MI_CLUST plug-in: Clustering dataset ICs using mutual information

This tutorial was written by

Arnaud Delorme, Hilit Serby, and  Scott Makeig

Many thanks to Payton Lin for capturing some images in earlier versions
and to Micah Richert, Yannick Marchand, and Stefen Debener for their detailed comments