eeg_picard

eeg_picard(EEG, engine=None, posact='off', sortcomps='off', **kwargs)

Perform ICA decomposition using Picard algorithm.

This function can use either a Python implementation or an EEGLAB (via MATLAB or Octave) implementation. When option_scaleicarms is enabled, components are scaled so each mixing column has RMS 1, matching EEGLAB eeg_checkset.

Parameters

EEGdict

EEGLAB-like data structure.

engineobject, optional

MATLAB or Octave engine instance. If None (default), the Python implementation is used.

posactstr | bool, optional

If ‘on’ or True, normalize component signs so max(abs(activations)) is positive. Default is ‘off’.

sortcompsstr | bool, optional

If ‘on’ or True, sort components by descending activation variance. Default is ‘off’.

kwargsdict

Additional keyword arguments to pass to the Picard algorithm. For example, {"max_iter": 500}. The Python backend uses identity initialization unless random_state or w_init is supplied. An integer random_state selects a reproducible random start; an explicit w_init matrix takes precedence over the seed.

Returns

dict

The updated EEG structure with ICA fields.