mne.decoding.TimeDecoding¶

class mne.decoding.TimeDecoding(picks=None, cv=5, clf=None, times=None, predict_mode='cross-validation', scorer=None, n_jobs=1)¶

Train and test a series of classifiers at each time point to obtain a score across time.

Parameters:

picks : array-like of int | None The channels indices to include. If None the data channels in info, except bad channels, are used. cv : int | object If an integer is passed, it is the number of folds. Specific cross-validation objects can be passed, see scikit-learn.cross_validation module for the list of possible objects. Defaults to 5. clf : object | None An estimator compliant with the scikit-learn API (fit & predict). If None the classifier will be a standard pipeline including StandardScaler and a Logistic Regression with default parameters. times : dict | None A dictionary to configure the training times: slices : ndarray, shape (n_clfs,) Array of time slices (in indices) used for each classifier. If not given, computed from ‘start’, ‘stop’, ‘length’, ‘step’. start : float Time at which to start decoding (in seconds). By default, min(epochs.times). stop : float Maximal time at which to stop decoding (in seconds). By default, max(times). step : float Duration separating the start of subsequent classifiers (in seconds). By default, equals one time sample. length : float Duration of each classifier (in seconds). By default, equals one time sample. If None, empty dict. predict_mode : {‘cross-validation’, ‘mean-prediction’} Indicates how predictions are achieved with regards to the cross- validation procedure: cross-validation : estimates a single prediction per sample based on the unique independent classifier fitted in the cross-validation. mean-prediction : estimates k predictions per sample, based on each of the k-fold cross-validation classifiers, and average these predictions into a single estimate per sample. Default: ‘cross-validation’ scorer : object | None scikit-learn Scorer instance. If None, set to accuracy_score. n_jobs : int Number of jobs to run in parallel. Defaults to 1.

Attributes:

picks_ : array-like of int | None The channels indices to include. ch_names : list, array-like, shape (n_channels,) Names of the channels used for training. y_train_ : ndarray, shape (n_samples,) The categories used for training. times_ : dict A dictionary that configures the training times: slices : ndarray, shape (n_clfs,) Array of time slices (in indices) used for each classifier. If not given, computed from ‘start’, ‘stop’, ‘length’, ‘step’. times : ndarray, shape (n_clfs,) The training times (in seconds). cv_ : CrossValidation object The actual CrossValidation input depending on y. estimators_ : list of list of scikit-learn.base.BaseEstimator subclasses. The estimators for each time point and each fold. y_pred_ : ndarray, shape (n_times, n_epochs, n_prediction_dims) Class labels for samples in X. y_true_ : list | ndarray, shape (n_samples,) The categories used for scoring y_pred_. scorer_ : object scikit-learn Scorer instance. scores_ : list of float, shape (n_times,) The scores (mean accuracy of self.predict(X) wrt. y.).

See also

GeneralizationAcrossTime

Notes

The function is equivalent to the diagonal of GeneralizationAcrossTime()

New in version 0.10.

Methods

fit(epochs[, y])	Train a classifier on each specified time slice.
plot([title, xmin, xmax, ymin, ymax, ax, ...])	Plotting function
predict(epochs)	Test each classifier on each specified testing time slice.
score([epochs, y])	Score Epochs

__init__(picks=None, cv=5, clf=None, times=None, predict_mode='cross-validation', scorer=None, n_jobs=1)¶

fit(epochs, y=None)¶

Train a classifier on each specified time slice.

Note. This function sets the picks_, ch_names, cv_, y_train, train_times_ and estimators_ attributes.

Parameters:	epochs : instance of Epochs The epochs. y : list or ndarray of int, shape (n_samples,) or None, optional To-be-fitted model values. If None, y = epochs.events[:, 2].
Returns:	self : TimeDecoding Returns fitted TimeDecoding object.

Notes

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

plot(title=None, xmin=None, xmax=None, ymin=None, ymax=None, ax=None, show=True, color=None, xlabel=True, ylabel=True, legend=True, chance=True, label='Classif. score')¶

Plotting function

Predict each classifier. If multiple classifiers are passed, average prediction across all classifiers to result in a single prediction per classifier.

Parameters:

title : str | None Figure title. xmin : float | None, optional, Min time value. xmax : float | None, optional, Max time value. ymin : float Min score value. Defaults to 0. ymax : float Max score value. Defaults to 1. ax : object | None Instance of mataplotlib.axes.Axis. If None, generate new figure. show : bool If True, the figure will be shown. Defaults to True. color : str Score line color. Defaults to ‘steelblue’. xlabel : bool If True, the xlabel is displayed. Defaults to True. ylabel : bool If True, the ylabel is displayed. Defaults to True. legend : bool If True, a legend is displayed. Defaults to True. chance : bool | float. Defaults to None Plot chance level. If True, chance level is estimated from the type of scorer. label : str Score label used in the legend. Defaults to ‘Classif. score’.

Returns:

fig : instance of matplotlib.figure.Figure The figure.

predict(epochs)¶

Test each classifier on each specified testing time slice.

Note

This function sets the y_pred_ and test_times_ attributes.

Parameters:	epochs : instance of Epochs The epochs. Can be similar to fitted epochs or not. See predict_mode parameter.
Returns:	y_pred : list of lists of arrays of floats, shape (n_times, n_epochs, n_prediction_dims) The single-trial predictions at each time sample.

score(epochs=None, y=None)¶

Score Epochs

Estimate scores across trials by comparing the prediction estimated for each trial to its true value.

Calls predict() if it has not been already.

Note. The function updates the scorer_, scores_, and y_true_ attributes.

Parameters:	epochs : instance of Epochs | None, optional The epochs. Can be similar to fitted epochs or not. If None, it needs to rely on the predictions y_pred_ generated with predict(). y : list | ndarray, shape (n_epochs,) | None, optional True values to be compared with the predictions y_pred_ generated with predict() via scorer_. If None and predict_mode``=='cross-validation' y = ``y_train_.
Returns:	scores : list of float, shape (n_times,) The scores estimated by scorer_ at each time sample (e.g. mean accuracy of predict(X)).