obob_mne.decoding.GeneralizedTemporalFromCollection

class obob_mne.decoding.GeneralizedTemporalFromCollection(data_list, raw_scores, weights)[source]

Base class for Temporal Generalization data from multiple subjects.

The individual elements must match in number of classes, times etc.

Parameters:
  • data_list (iterable of GeneralizedTemporalArray) – A list (or any other iterable) of GeneralizedTemporalArray
  • raw_scores (numpy.ndarray shape (n_channels, n_times) or (n_folds, n_channels, n_times)) – The already processed (i.e., averaged, statistically tested) scores.
  • weights (numpy.ndarray shape (n_channels, n_times)) – The already processed (i.e., averaged, statistically tested) weights.
__init__(data_list, raw_scores, weights)[source]

Methods

__init__(data_list, raw_scores, weights)
diagonal_as_temporal() Return the non-generalized results.
get_temporal_from_training_interval(tmin, tmax) Average the scores of a training interval.
plot_scores([axes, show, cmap, colorbar, …]) Plot the scores as a Matrix.

Attributes

chance_level float – The chance level of the classifier.
info Measurement info
must_be_equal
nave int – Number of epochs in the training set.
nave_testing int – Number of epochs in the testing set.
scores numpy.ndarray – The scores of the classification
tmin float – tmin
weights numpy.ndarray – The classifier weights