Chapter 01: Multiple Subject Proccessing Overview

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Arrow.small.left.gif II.Multiple subject processing tutorial
Tutorial Outline
Chapter 02: STUDY Creation Arrow.small.right.gif


Contents

EEGLAB Studysets and Independent Component Clustering

This tutorial describes how to use a new (EEGLAB v5.0-) structure, the STUDY, to manage and process data recorded from multiple subjects, sessions, and/or conditions of an experimental study. EEGLAB uses studysets for performing statistical comparisons, for automated serial (and in future parallel) computation, and for clustering of independent signal components across subjects and sessions. It details the use of a new (v5.0-) set of EEGLAB component clustering functions that allow exploratory definition and visualization of clusters of equivalent or similar ICA data components across any number of subjects, subject groups, experimental conditions, and/or sessions. Clustering functions may be used to assess the consistency of ICA (or, other linearly filtered) decompositions across subjects and conditions, and to evaluate the separate contributions of identified clusters of these data components to the recorded EEG dynamics.

EEGLAB STUDY structures and studysets:

EEGLAB v5.0 introduced a new basic concept and data structure, the STUDY. Each STUDY, saved on disk as a studyset (.std) file, is a structure containing a set of epoched EEG datasets from one or more subjects, in one or more groups, recorded in one or more sessions, in one or more task conditions -- plus additional (e.g., component clustering) information. In future EEGLAB versions, STUDY structures and studysets will become primary EEGLAB data processing objects. As planned, operations now carried out from the EEGLAB menu or the Matlab command line on datasets will be equally applicable to studysets comprising any number of datasets.

Use of STUDY structures to process single-trial channel data:

EEGLAB studysets may be used to compute ERPs, spectrum, ERSP and other measures onto single-trial channel data across dozens or even hundreds of subjects. Missing data channels may be replaced if necessary using spherical interpolation. Parametric or bootstrap statistics may be used with correction for multiple comparisons to compare a given measure in any n x m design. The channel data may also be used to compute ICA component projections (see below).

Use of STUDY structures to cluster ICA components:

EEGLAB studysets may be used to cluster similar independent components from multiple sessions and to evaluate the results of clustering. As ICA component clustering is a powerful tool for electrophysiological data analysis, and a necessary tool for applying ICA to experimental studies involving planned comparisons between conditions, sessions, and/or subject groups, the STUDY concept has been applied first to independent component clustering. A small studyset of five datasets, released with EEGLAB v5.0b for tutorial use and available here, has been used to create the example screens below. We recommend that after following the tutorial using this small example studyset, users next explore component clustering by forming EEGLAB studies for one or more of their existing experimental studies testing the component clustering functions more fully on data they know well by repeating the steps outlined below.

Upgrades to several standard EEGLAB plotting functions also allow them to be applied simultaneously to whole studysets (either sequentially or in parallel) rather than to single datasets, for example allowing users to plot grand average channel data measures (ERPs, channel spectra, etc.) across multiple subjects, sessions, and/or conditions from the EEGLAB menu.

The dataset information contained in a STUDY structure allows straightforward statistical comparisons of component activities and/or source models for a variety of experimental designs. Currently, only a few two-condition comparisons are directly supported. Currently we are writing Matlab functions that will process information in more general STUDY structures and the EEG data structures they contain, potentially applying several types of statistical comparison (ANOVA, permutation-based, etc.) to many types of data measures.

Matlab toolboxes required for component clustering:

Currently, two clustering methods are available: 'kmeans' and 'neural network' clustering. 'Kmeans' clustering requires the Matlab Statistical Toolbox, while 'neural network' clustering uses a function from the Matlab Neural Network Toolbox. To learn whether these toolboxes are installed, type >> help on the Matlab command line and see if the line toolbox/stats - Statistical toolbox and/or the line nnet/nnet - Neural Network toolbox are present. In future, we plan to explore the possibility of providing alternate algorithms that do not require these toolboxes, as well as options to cluster components using other methods.

This tutorial assumes that readers are already familiar with the material covered in the single subject EEGLAB tutorial and also (for the later part of this chapter) in the EEGLAB script writing tutorial.


Arrow.small.left.gif II.Multiple subject processing tutorial
Tutorial Outline
Chapter 02: STUDY Creation Arrow.small.right.gif