This documentation is for development version 0.18.dev0.

mne.preprocessing.infomax

mne.preprocessing.infomax(data, weights=None, l_rate=None, block=None, w_change=1e-12, 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 1e-12.

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.

extended : bool

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 sub-gaussian sources is automatically determined. If negative, the number of sub-gaussian 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 : int | np.random.RandomState

If random_state is an int, use random_state to seed the random number generator. If random_state is already a np.random.RandomState instance, use random_state as random number generator.

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_bias : bool

This quantity indicates if the bias should be computed. Defaults to True.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:
unmixing_matrix : np.ndarray, shape (n_features, n_features)

The linear unmixing operator.

References

[1]A. J. Bell, T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-1159, 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), 417-441, 1999.