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, return_n_iter=False)[source]#

Run (extended) Infomax ICA decomposition on raw data.

Parameters

datanp.ndarray, shape (n_samples, n_features): The whitened data to unmix.
weightsnp.ndarray, shape (n_features, n_features): The initialized unmixing matrix. Defaults to None, which means the identity matrix is used.
l_ratefloat: 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.
blockint: The block size of randomly chosen data segments. Defaults to floor(sqrt(n_times / 3.)).
w_changefloat: The change at which to stop iteration. Defaults to 1e-12.
anneal_degfloat: The angle (in degrees) at which the learning rate will be reduced. Defaults to 60.0.
anneal_stepfloat: 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_subgaussint: The number of subgaussian components. Only considered for extended Infomax. Defaults to 1.
kurt_sizeint: The window size for kurtosis estimation. Only considered for extended Infomax. Defaults to 6000.
ext_blocksint: 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_iterint: The maximum number of iterations. Defaults to 200.
random_stateNone | int | instance of RandomState: A seed for the NumPy random number generator (RNG). If None (default), the seed will be obtained from the operating system (see RandomState for details), meaning it will most likely produce different output every time this function or method is run. To achieve reproducible results, pass a value here to explicitly initialize the RNG with a defined state.
blowupfloat: The maximum difference allowed between two successive estimations of the unmixing matrix. Defaults to 10000.
blowup_facfloat: 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_angleint | 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 | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.
return_n_iterbool: Whether to return the number of iterations performed. Defaults to False.

Returns

unmixing_matrixnp.ndarray, shape (n_features, n_features): The linear unmixing operator.
n_iterint: The number of iterations. Only returned if return_max_iter=True.

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.

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mne.preprocessing.equalize_bads