obob_mne.decoding.TemporalArray

class obob_mne.decoding.TemporalArray(raw_scores, weights, n_classes, info, tmin, scoring_name, c_factors_training, nave=1, nave_testing=None, c_factors_testing=None)[source]

Base class for temporal decoding.

Parameters:
  • raw_scores (numpy.ndarray shape (n_times) or (n_folds, n_times)) – The scores of the classification
  • weights (numpy.ndarray shape (n_channels, n_times)) – Classifier weights
  • n_classes (int) – The number of classes
  • info (dict) – Info dict
  • tmin (float) – Time of the first sample in seconds
  • scoring_name (str) – Name of the scoring function (i.e. Accuracy…)
  • c_factors_training (str) – Name of the factors over which was collapsed in the training set
  • nave (int, optional) – Number of epochs in the training set.
  • nave_testing (int or None) – Number of epochs in the testing set. If None, it is copied from nave.
  • c_factors_testing (None or str) – Name of the factors over which was collapsed in the testing set. If None, it is copied from c_factors_training.
__init__(raw_scores, weights, n_classes, info, tmin, scoring_name, c_factors_training, nave=1, nave_testing=None, c_factors_testing=None)[source]

Methods

__init__(raw_scores, weights, n_classes, …)
add_channels(add_list[, force_update_info]) Append new channels to the instance.
add_proj(projs[, remove_existing, verbose]) Add SSP projection vectors.
animate_topomap([ch_type, times, …]) Make animation of evoked data as topomap timeseries.
anonymize() Anonymize measurement information in place.
apply_baseline([baseline, verbose]) Baseline correct evoked data.
apply_hilbert([picks, envelope, n_jobs, …]) Compute analytic signal or envelope for a subset of channels.
apply_proj() Apply the signal space projection (SSP) operators to the data.
as_type([ch_type, mode]) Compute virtual evoked using interpolated fields.
copy() Copy the instance of evoked.
crop([tmin, tmax]) Crop data to a given time interval.
decimate(decim[, offset]) Decimate the evoked data.
del_proj([idx]) Remove SSP projection vector.
detrend([order, picks]) Detrend data.
drop_channels(ch_names) Drop channel(s).
filter(l_freq, h_freq[, picks, …]) Filter a subset of channels.
get_peak([ch_type, tmin, tmax, mode, …]) Get location and latency of peak amplitude.
interpolate_bads([reset_bads, mode, origin, …]) Interpolate bad MEG and EEG channels.
pick(picks[, exclude]) Pick a subset of channels.
pick_channels(ch_names) Pick some channels.
pick_types([meg, eeg, stim, eog, ecg, emg, …]) Pick some channels by type and names.
plot([picks, exclude, unit, show, ylim, …]) Plot evoked data using butterfly plots.
plot_field(surf_maps[, time, time_label, n_jobs]) Plot MEG/EEG fields on head surface and helmet in 3D.
plot_image([picks, exclude, unit, show, …]) Plot evoked data as images.
plot_joint([times, title, picks, exclude, …]) Plot evoked data as butterfly plot and add topomaps for time points.
plot_projs_topomap([ch_type, layout, axes]) Plot SSP vector.
plot_scores([axes, show]) Plot the scores as a line plot.
plot_sensors([kind, ch_type, title, …]) Plot sensor positions.
plot_topo([layout, layout_scale, color, …]) Plot 2D topography of evoked responses.
plot_topomap([times, ch_type, layout, vmin, …]) Plot topographic maps of specific time points of evoked data.
plot_white(noise_cov[, show, rank, …]) Plot whitened evoked response.
rename_channels(mapping) Rename channels.
reorder_channels(ch_names) Reorder channels.
resample(sfreq[, npad, window, n_jobs, pad, …]) Resample data.
save(fname) Save dataset to file.
savgol_filter(h_freq[, verbose]) Filter the data using Savitzky-Golay polynomial method.
set_channel_types(mapping) Define the sensor type of channels.
set_eeg_reference([ref_channels, …]) Specify which reference to use for EEG data.
set_montage(montage[, set_dig, verbose]) Set EEG sensor configuration and head digitization.
shift_time(tshift[, relative]) Shift time scale in evoked data.
time_as_index(times[, use_rounding]) Convert time to indices.
to_data_frame([picks, index, scaling_time, …]) Export data in tabular structure as a pandas DataFrame.

Attributes

ch_names Channel names.
chance_level The chance level of the classifier.
compensation_grade The current gradient compensation grade.
data The data matrix.
nclasses
proj Whether or not projections are active.
scores The scores of the classification