Simply EEGLAB
AMICA & Picard
Station 4 · Lesson 7 · EEGLAB 2026.1
In the GUI
- FileManage EEGLAB extensions
- ToolsDecompose data by ICAICA algorithm to use (click to select)Infomax picard.mCommandline options (See help messages)
'maxiter', 500, 'mode', 'standard' - PlotComponent mapsIn 2-DOK
runica - ToolsRun AMICA
- In File:# PCA Dims.:
eeglab_data.fdt
Script
EEG_ica = pop_eegfiltnew(EEG, 'locutoff', 1);
expectedRank = EEG_ica.nbchan - numel(badChans) - 1;
tic;
EEG_runica = pop_runica(EEG_ica, 'icatype', 'runica', 'extended', 1);
tRunica = toc;
tic;
EEG_picard = pop_runica(EEG_ica, 'icatype', 'picard', 'maxiter', 500, ...
'mode', 'standard', 'pca', expectedRank);
tPicard = toc;
fprintf('runica: %.0f s, %d components\n', tRunica, size(EEG_runica.icaweights, 1));
fprintf('Picard: %.0f s, %d components\n', tPicard, size(EEG_picard.icaweights, 1));
pop_topoplot(EEG_runica, 0, 1:10);
pop_topoplot(EEG_picard, 0, 1:10);Which value should I choose?
| runica (extended Infomax) | Picard | AMICA | |
|---|---|---|---|
| Comes with EEGLAB | Yes, the default | No, an extension | No, an extension |
| Speed, as EEGLAB labels it | The default | FastICA picard.m (fastest) | AMICA (slowest; best) |
| What it gives | One ICA model | A plain Infomax (not extended) or FastICA solution, reached faster | One or more ICA models |
| Rank | pop_runica tries to detect it | Pass 'pca' yourself | Set the number of components to keep yourself |
| When to choose it | Most studies, and everything on this roadmap | Many datasets, or many channels, when time matters | When you want the slowest, most careful decomposition and have the time |
Check
- The algorithm appears
- The number of components
- The maps are similar
- You noted the time
For your Methods
Independent component analysis was computed with Picard (Infomax mode, maximum 500 iterations; picard plugin [version], EEGLAB 2026.1; [31] components after PCA reduction to the data rank) on a copy of the data high-pass filtered at 1 Hz, and the resulting weights were applied to the 0.1 Hz high-pass filtered data.