obob_mne.decoding.Temporal

class obob_mne.decoding.Temporal(epochs, pipeline, epochs_test=None, cv=2, scoring='accuracy', n_jobs=-1)[source]

Apply a decoding pipeline to every sample.

Use this class to perform Temporal decoding. This means that a classifier is trained on every sample and tested on that very sample.

The minimum requirements are the data as mne.Epochs and the pipeline as sklearn.pipeline.Pipeline, which is most commonly generated using sklearn.pipeline.make_pipeline().

This class treats every individual event_id as an individual target. If you want to combine multiple events_id into one target, you can use epochs.collapse_conditions().

If you want the training and testing data to be different, you can supply the training data to epochs and the testing data to epochs_test.

Cross validation will be run only if training and testing data are equal (i.e., when epochs_test=None).

Parameters:
  • epochs (mne.Epochs) – The epochs to apply the classifier pipeline to.
  • pipeline (sklearn.pipeline.Pipeline) – The classifier pipeline to use. Most likely created with sklearn.pipeline.make_pipeline()
  • epochs_test (mne.Epochs or None, optional) – If set, the classifier gets tested on this data. The event_ids must be equal. No crossvalidation will be run in this case.
  • cv (int, optional) – The amount of folds for cross validation
  • scoring (str or callable, optional) – The scoring function to use.
  • n_jobs (int, optional) – Number of CPU cores to use
__init__(epochs, pipeline, epochs_test=None, cv=2, scoring='accuracy', n_jobs=-1)[source]

Methods

__init__(epochs, pipeline[, epochs_test, …])
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_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 some channels.
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, verbose]) Interpolate bad MEG and EEG 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 float – The chance level of the classifier.
compensation_grade The current gradient compensation grade.
data The data matrix.
decoder The decoder.
nave int – Number of epochs in the training set.
nave_testing int – Number of epochs in the testing set.
nclasses
proj Whether or not projections are active.
scores numpy.ndarray – The scores of the classification