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Class ReceptiveField

mne/decoding/receptive_field.py:26–453  ·  view source on GitHub ↗

Fit a receptive field model. This allows you to fit an encoding model (stimulus to brain) or a decoding model (brain to stimulus) using time-lagged input features (for example, a spectro- or spatio-temporal receptive field, or STRF) :footcite:`TheunissenEtAl2001,WillmoreSmyth2003,Cr

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24
25@fill_doc
26class ReceptiveField(MetaEstimatorMixin, BaseEstimator):
27 """Fit a receptive field model.
28
29 This allows you to fit an encoding model (stimulus to brain) or a decoding
30 model (brain to stimulus) using time-lagged input features (for example, a
31 spectro- or spatio-temporal receptive field, or STRF)
32 :footcite:`TheunissenEtAl2001,WillmoreSmyth2003,CrosseEtAl2016,HoldgrafEtAl2016`.
33
34 Parameters
35 ----------
36 tmin : float
37 The starting lag, in seconds (or samples if ``sfreq`` == 1).
38 tmax : float
39 The ending lag, in seconds (or samples if ``sfreq`` == 1).
40 Must be >= tmin.
41 sfreq : float
42 The sampling frequency used to convert times into samples.
43 feature_names : array, shape (n_features,) | None
44 Names for input features to the model. If None, feature names will
45 be auto-generated from the shape of input data after running `fit`.
46 estimator : instance of sklearn.base.BaseEstimator | float | None
47 The model used in fitting inputs and outputs. This can be any
48 scikit-learn-style model that contains a fit and predict method. If a
49 float is passed, it will be interpreted as the ``alpha`` parameter
50 to be passed to a Ridge regression model. If `None`, then a Ridge
51 regression model with an alpha of 0 will be used.
52 fit_intercept : bool | None
53 If True (default), the sample mean is removed before fitting.
54 If ``estimator`` is a :class:`sklearn.base.BaseEstimator`,
55 this must be None or match ``estimator.fit_intercept``.
56 scoring : ['r2', 'corrcoef']
57 Defines how predictions will be scored. Currently must be one of
58 'r2' (coefficient of determination) or 'corrcoef' (the correlation
59 coefficient).
60 patterns : bool
61 If True, inverse coefficients will be computed upon fitting using the
62 covariance matrix of the inputs, and the cross-covariance of the
63 inputs/outputs, according to :footcite:`HaufeEtAl2014`. Defaults to
64 False.
65 n_jobs : int | str
66 Number of jobs to run in parallel. Can be 'cuda' if CuPy
67 is installed properly and ``estimator is None``.
68
69 .. versionadded:: 0.18
70 edge_correction : bool
71 If True (default), correct the autocorrelation coefficients for
72 non-zero delays for the fact that fewer samples are available.
73 Disabling this speeds up performance at the cost of accuracy
74 depending on the relationship between epoch length and model
75 duration. Only used if ``estimator`` is float or None.
76
77 .. versionadded:: 0.18
78
79 Attributes
80 ----------
81 coef_ : array, shape ([n_outputs, ]n_features, n_delays)
82 The coefficients from the model fit, reshaped for easy visualization.
83 During :meth:`mne.decoding.ReceptiveField.fit`, if ``y`` has one

Callers 8

test_rank_deficiencyFunction · 0.90
test_receptive_field_1dFunction · 0.90
test_receptive_field_ndFunction · 0.90
test_inverse_coefFunction · 0.90
test_linalg_warningFunction · 0.90
30_strf.pyFile · 0.90

Calls

no outgoing calls

Tested by 6

test_rank_deficiencyFunction · 0.72
test_receptive_field_1dFunction · 0.72
test_receptive_field_ndFunction · 0.72
test_inverse_coefFunction · 0.72
test_linalg_warningFunction · 0.72