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)
weights : np.ndarray, shape (n_features, n_features)
l_rate : float
block : int
w_change : float
anneal_deg : float
anneal_step : float
extended : bool
n_subgauss : int
kurt_size : int
ext_blocks : int
max_iter : int
random_state : int | np.random.RandomState
blowup : float
blowup_fac : float
n_small_angle : int | None
use_bias : bool
verbose : bool, str, int, or None
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Returns: | unmixing_matrix : np.ndarray, shape (n_features, n_features)
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References
[R156157] | A. J. Bell, T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-1159, 1995. |
[R157157] | 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. |