def ica_convert2mne(unmixing, pca, info=None, method='fastica'):
# create MNE-type of ICA object
from mne.preprocessing.ica import ICA
n_comp = unmixing.shape[1]
if method == 'extended-infomax':
ica_method = 'infomax'
fit_params = dict(extended=True)
else:
ica_method = method
fit_params = None
ica = ICA(n_components=n_comp, method=ica_method, fit_params=fit_params)
# add PCA object
ica.pca = pca
# PCA info to be used bei MNE-Python
ica.pca_mean_ = pca.mean_
ica.pca_components_ = pca.components_
exp_var = pca.explained_variance_
ica.pca_explained_variance_ = exp_var
ica.pca_explained_variance_ratio_ = pca.explained_variance_ratio_
# ICA info
ica.n_components_ = n_comp
ica.n_components = n_comp
ica.components_ = unmixing # compatible with sklearn
ica.unmixing_ = ica.components_ # as used by sklearn
ica.mixing_ = pinv(ica.unmixing_) # as used by sklearn
ica.unmixing_matrix_ = ica.unmixing_ / np.sqrt(
exp_var[0:n_comp])[None, :] # as used by MNE-Python
ica.mixing_matrix_ = pinv(ica.unmixing_matrix_) # as used by MNE-Python
ica._ica_names = ['ICA%03d' % ii for ii in range(n_comp)]
ica.fun = method
if info:
ica.info = info
return ica
