mne.preprocessing.infomax¶

mne.preprocessing.
infomax
(data, weights=None, l_rate=None, block=None, w_change=1e12, anneal_deg=60.0, anneal_step=0.9, extended=True, n_subgauss=1, kurt_size=6000, ext_blocks=1, max_iter=200, random_state=None, blowup=10000.0, blowup_fac=0.5, n_small_angle=20, use_bias=True, verbose=None)[source]¶ Run (extended) Infomax ICA decomposition on raw data.
 Parameters
 data
np.ndarray
, shape (n_samples, n_features) The whitened data to unmix.
 weights
np.ndarray
, shape (n_features, n_features) The initialized unmixing matrix. Defaults to None, which means the identity matrix is used.
 l_rate
float
This quantity indicates the relative size of the change in weights. Defaults to
0.01 / log(n_features ** 2)
.Note
Smaller learning rates will slow down the ICA procedure.
 block
int
The block size of randomly chosen data segments. Defaults to floor(sqrt(n_times / 3.)).
 w_change
float
The change at which to stop iteration. Defaults to 1e12.
 anneal_deg
float
The angle (in degrees) at which the learning rate will be reduced. Defaults to 60.0.
 anneal_step
float
The factor by which the learning rate will be reduced once
anneal_deg
is exceeded:l_rate *= anneal_step.
Defaults to 0.9. extendedbool
Whether to use the extended Infomax algorithm or not. Defaults to True.
 n_subgauss
int
The number of subgaussian components. Only considered for extended Infomax. Defaults to 1.
 kurt_size
int
The window size for kurtosis estimation. Only considered for extended Infomax. Defaults to 6000.
 ext_blocks
int
Only considered for extended Infomax. If positive, denotes the number of blocks after which to recompute the kurtosis, which is used to estimate the signs of the sources. In this case, the number of subgaussian sources is automatically determined. If negative, the number of subgaussian sources to be used is fixed and equal to n_subgauss. In this case, the kurtosis is not estimated. Defaults to 1.
 max_iter
int
The maximum number of iterations. Defaults to 200.
 random_state
None
int
 instance ofRandomState
If
random_state
is anint
, it will be used as a seed forRandomState
. IfNone
, the seed will be obtained from the operating system (seeRandomState
for details). Default isNone
. blowup
float
The maximum difference allowed between two successive estimations of the unmixing matrix. Defaults to 10000.
 blowup_fac
float
The factor by which the learning rate will be reduced if the difference between two successive estimations of the unmixing matrix exceededs
blowup
:l_rate *= blowup_fac
. Defaults to 0.5. n_small_angle
int
None
The maximum number of allowed steps in which the angle between two successive estimations of the unmixing matrix is less than
anneal_deg
. If None, this parameter is not taken into account to stop the iterations. Defaults to 20. use_biasbool
This quantity indicates if the bias should be computed. Defaults to True.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 data
 Returns
 unmixing_matrix
np.ndarray
, shape (n_features, n_features) The linear unmixing operator.
 unmixing_matrix
References
 1
A. J. Bell, T. J. Sejnowski. An informationmaximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 11291159, 1995.
 2
T. W. Lee, M. Girolami, T. J. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 11(2), 417441, 1999.