| POP_PRECLUST | Prepare STUDY components' location and activity measures for later clustering. Collect information in an interactive pop-up query window. To pre-cluster from the commandline, use std_preclust(). After data entry into the pop window, selected measures (one or more from options: ERP, dipole locations, spectra, scalp maps, ERSP, and ITC) are computed for each dataset in the STUDY set, unless they already present. After all requested measures are computed and saved in the STUDY datasets, a PCA matrix (by runica() with 'pca' option) is constructed (this is the feature reduction step). This matrix will be used as input to the clustering algorithm in pop_clust. pop_preclust allows selection of a subset of components to cluster. This subset may either be user-specified, all components with dipole model residual variance lower than a defined threshold (see dipfit()), or components from an already existing cluster (for hierarchical clustering). The EEG datasets in the ALLEEG structure are updated; then the updated EEG sets are saved to disk. Calls std_preclust(). |
| Usage: | >> [STUDY, ALLEEG] = pop_preclust(STUDY, ALLEEG); % pop up interactive window >> [STUDY, ALLEEG] = pop_preclust(STUDY, ALLEEG, clustind); % sub-cluster |
| Inputs: | |
STUDY |
STUDY set structure containing (loaded) EEG dataset structures |
ALLEEG |
ALLEEG vector of EEG structures, else a single EEG dataset. |
clustind |
(single) cluster index to sub-cluster, Hhierarchical clustering may be useful, for example, to separate a bilteral cluster into left and right hemisphere sub-clusters. Should be empty for whole STUDY (top level) clustering {default: []} |
| Outputs: | |
STUDY |
the input STUDY set with added pre-clustering data for use by pop_clust |
ALLEEG |
the input ALLEEG vector of EEG dataset structures modified by adding pre-clustering data (pointers to .mat files that hold cluster measure information). |
| Authors: | Arnaud Delorme, Hilit Serby & Scott Makeig, SCCN, INC, UCSD, May 13, 2004- |
| See also: | std_preclust() |