mne.time_frequency.EpochsTFR

class mne.time_frequency.EpochsTFR(info, data, times, freqs, comment=None, method=None, events=None, event_id=None, metadata=None, verbose=None)[source]

Container for Time-Frequency data on epochs.

Can for example store induced power at sensor level.

Parameters
infoInfo

The measurement info.

datandarray, shape (n_epochs, n_channels, n_freqs, n_times)

The data.

timesndarray, shape (n_times,)

The time values in seconds.

freqsndarray, shape (n_freqs,)

The frequencies in Hz.

commentstr | None, default None

Comment on the data, e.g., the experimental condition.

methodstr | None, default None

Comment on the method used to compute the data, e.g., morlet wavelet.

eventsndarray, shape (n_events, 3) | None

The events as stored in the Epochs class. If None (default), all event values are set to 1 and event time-samples are set to range(n_epochs).

event_iddict | None

Example: dict(auditory=1, visual=3). They keys can be used to access associated events. If None, all events will be used and a dict is created with string integer names corresponding to the event id integers.

metadatainstance of pandas.DataFrame | None

A pandas.DataFrame containing pertinent information for each trial. See mne.Epochs for further details.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Attributes
infoinstance of Info

Measurement info.

ch_nameslist

Channel names.

datandarray, shape (n_epochs, n_channels, n_freqs, n_times)

The data array.

timesndarray, shape (n_times,)

The time values in seconds.

freqsndarray, shape (n_freqs,)

The frequencies in Hz.

commentstr

Comment on dataset. Can be the condition.

methodstr | None, default None

Comment on the method used to compute the data, e.g., morlet wavelet.

eventsndarray, shape (n_events, 3) | None

Array containing sample information as event_id

event_iddict | None

Names of conditions correspond to event_ids

metadatapandas.DataFrame, shape (n_events, n_cols) | None

Get the metadata.

Notes
—–
.. versionadded:: 0.13.0

Methods

__contains__(ch_type)

Check channel type membership.

__getitem__(item)

Return an Epochs object with a copied subset of epochs.

__hash__()

Hash the object.

__iter__()

Facilitate iteration over epochs.

__len__()

Return the number of epochs.

add_channels(add_list[, force_update_info])

Append new channels to the instance.

apply_baseline(baseline[, mode, verbose])

Baseline correct the data.

average()

Average the data across epochs.

copy()

Return a copy of the instance.

crop([tmin, tmax, fmin, fmax, include_tmax])

Crop data to a given time interval in place.

drop_channels(ch_names)

Drop channel(s).

get_channel_types([picks, unique, only_data_chs])

Get a list of channel type for each channel.

get_montage()

Get a DigMontage from instance.

next([return_event_id])

Iterate over epoch data.

pick(picks[, exclude])

Pick a subset of channels.

pick_channels(ch_names[, ordered])

Pick some channels.

pick_types([meg, eeg, stim, eog, ecg, emg, …])

Pick some channels by type and names.

reorder_channels(ch_names)

Reorder channels.

save(fname[, overwrite])

Save TFR object to hdf5 file.

__contains__(ch_type)[source]

Check channel type membership.

Parameters
ch_typestr

Channel type to check for. Can be e.g. ‘meg’, ‘eeg’, ‘stim’, etc.

Returns
inbool

Whether or not the instance contains the given channel type.

Examples

Channel type membership can be tested as:

>>> 'meg' in inst  
True
>>> 'seeg' in inst  
False
__getitem__(item)[source]

Return an Epochs object with a copied subset of epochs.

Parameters
itemslice, array_like, str, or list

See below for use cases.

Returns
epochsinstance of Epochs

See below for use cases.

Notes

Epochs can be accessed as epochs[...] in several ways:

  1. Integer or slice: epochs[idx] will return an Epochs object with a subset of epochs chosen by index (supports single index and Python-style slicing).

  2. String: epochs['name'] will return an Epochs object comprising only the epochs labeled 'name' (i.e., epochs created around events with the label 'name').

    If there are no epochs labeled 'name' but there are epochs labeled with /-separated tags (e.g. 'name/left', 'name/right'), then epochs['name'] will select the epochs with labels that contain that tag (e.g., epochs['left'] selects epochs labeled 'audio/left' and 'visual/left', but not 'audio_left').

    If multiple tags are provided as a single string (e.g., epochs['name_1/name_2']), this selects epochs containing all provided tags. For example, epochs['audio/left'] selects 'audio/left' and 'audio/quiet/left', but not 'audio/right'. Note that tag-based selection is insensitive to order: tags like 'audio/left' and 'left/audio' will be treated the same way when selecting via tag.

  3. List of strings: epochs[['name_1', 'name_2', ... ]] will return an Epochs object comprising epochs that match any of the provided names (i.e., the list of names is treated as an inclusive-or condition). If none of the provided names match any epoch labels, a KeyError will be raised.

    If epoch labels are /-separated tags, then providing multiple tags as separate list entries will likewise act as an inclusive-or filter. For example, epochs[['audio', 'left']] would select 'audio/left', 'audio/right', and 'visual/left', but not 'visual/right'.

  4. Pandas query: epochs['pandas query'] will return an Epochs object with a subset of epochs (and matching metadata) selected by the query called with self.metadata.eval, e.g.:

    epochs["col_a > 2 and col_b == 'foo'"]
    

    would return all epochs whose associated col_a metadata was greater than two, and whose col_b metadata was the string ‘foo’. Query-based indexing only works if Pandas is installed and self.metadata is a pandas.DataFrame.

    New in version 0.16.

__hash__()[source]

Hash the object.

Returns
hashint

The hash

__iter__()[source]

Facilitate iteration over epochs.

This method resets the object iteration state to the first epoch.

Notes

This enables the use of this Python pattern:

>>> for epoch in epochs:  
>>>     print(epoch)  

Where epoch is given by successive outputs of mne.Epochs.next().

__len__()[source]

Return the number of epochs.

Returns
n_epochsint

The number of remaining epochs.

Notes

This function only works if bad epochs have been dropped.

Examples

This can be used as:

>>> epochs.drop_bad()  
>>> len(epochs)  
43
>>> len(epochs.events)  
43
add_channels(add_list, force_update_info=False)[source]

Append new channels to the instance.

Parameters
add_listlist

A list of objects to append to self. Must contain all the same type as the current object.

force_update_infobool

If True, force the info for objects to be appended to match the values in self. This should generally only be used when adding stim channels for which important metadata won’t be overwritten.

New in version 0.12.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

drop_channels

Notes

If self is a Raw instance that has been preloaded into a numpy.memmap instance, the memmap will be resized.

apply_baseline(baseline, mode='mean', verbose=None)[source]

Baseline correct the data.

Parameters
baselinearray_like, shape (2,)

The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used.

mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’

Perform baseline correction by

  • subtracting the mean of baseline values (‘mean’)

  • dividing by the mean of baseline values (‘ratio’)

  • dividing by the mean of baseline values and taking the log (‘logratio’)

  • subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)

  • subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’)

  • dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’)

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.

Returns
instinstance of AverageTFR

The modified instance.

Examples using apply_baseline:

average()[source]

Average the data across epochs.

Returns
aveinstance of AverageTFR

The averaged data.

Examples using average:

property ch_names

Channel names.

property compensation_grade

The current gradient compensation grade.

copy()[source]

Return a copy of the instance.

Returns
copyinstance of EpochsTFR | instance of AverageTFR

A copy of the instance.

crop(tmin=None, tmax=None, fmin=None, fmax=None, include_tmax=True)[source]

Crop data to a given time interval in place.

Parameters
tminfloat | None

Start time of selection in seconds.

tmaxfloat | None

End time of selection in seconds.

fminfloat | None

Lowest frequency of selection in Hz.

New in version 0.18.0.

fmaxfloat | None

Highest frequency of selection in Hz.

New in version 0.18.0.

include_tmaxbool

If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).

New in version 0.19.

Returns
instinstance of AverageTFR

The modified instance.

Examples using crop:

drop_channels(ch_names)[source]

Drop channel(s).

Parameters
ch_namesiterable or str

Iterable (e.g. list) of channel name(s) or channel name to remove.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

New in version 0.9.0.

get_channel_types(picks=None, unique=False, only_data_chs=False)[source]

Get a list of channel type for each channel.

Parameters
picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

uniquebool

Whether to return only unique channel types. Default is False.

only_data_chsbool

Whether to ignore non-data channels. Default is False.

Returns
channel_typeslist

The channel types.

get_montage()[source]

Get a DigMontage from instance.

Returns
montageNone | str | DigMontage

A montage containing channel positions. If str or DigMontage is specified, the channel info will be updated with the channel positions. Default is None. See also the documentation of mne.channels.DigMontage for more information.

property metadata

Get the metadata.

next(return_event_id=False)[source]

Iterate over epoch data.

Parameters
return_event_idbool

If True, return both the epoch data and an event_id.

Returns
epocharray of shape (n_channels, n_times)

The epoch data.

event_idint

The event id. Only returned if return_event_id is True.

pick(picks, exclude=())[source]

Pick a subset of channels.

Parameters
picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

excludelist | str

Set of channels to exclude, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”).

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

pick_channels(ch_names, ordered=False)[source]

Pick some channels.

Parameters
ch_nameslist

The list of channels to select.

orderedbool

If True (default False), ensure that the order of the channels in the modified instance matches the order of ch_names.

New in version 0.20.0.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

The channel names given are assumed to be a set, i.e. the order does not matter. The original order of the channels is preserved. You can use reorder_channels to set channel order if necessary.

New in version 0.9.0.

pick_types(meg=False, eeg=False, stim=False, eog=False, ecg=False, emg=False, ref_meg='auto', misc=False, resp=False, chpi=False, exci=False, ias=False, syst=False, seeg=False, dipole=False, gof=False, bio=False, ecog=False, fnirs=False, csd=False, include=(), exclude='bads', selection=None, verbose=None)[source]

Pick some channels by type and names.

Parameters
megbool | str

If True include MEG channels. If string it can be ‘mag’, ‘grad’, ‘planar1’ or ‘planar2’ to select only magnetometers, all gradiometers, or a specific type of gradiometer.

eegbool

If True include EEG channels.

stimbool

If True include stimulus channels.

eogbool

If True include EOG channels.

ecgbool

If True include ECG channels.

emgbool

If True include EMG channels.

ref_megbool | str

If True include CTF / 4D reference channels. If ‘auto’, reference channels are included if compensations are present and meg is not False. Can also be the string options for the meg parameter.

miscbool

If True include miscellaneous analog channels.

respbool

If True include response-trigger channel. For some MEG systems this is separate from the stim channel.

chpibool

If True include continuous HPI coil channels.

excibool

Flux excitation channel used to be a stimulus channel.

iasbool

Internal Active Shielding data (maybe on Triux only).

systbool

System status channel information (on Triux systems only).

seegbool

Stereotactic EEG channels.

dipolebool

Dipole time course channels.

gofbool

Dipole goodness of fit channels.

biobool

Bio channels.

ecogbool

Electrocorticography channels.

fnirsbool | str

Functional near-infrared spectroscopy channels. If True include all fNIRS channels. If False (default) include none. If string it can be ‘hbo’ (to include channels measuring oxyhemoglobin) or ‘hbr’ (to include channels measuring deoxyhemoglobin).

csdbool

EEG-CSD channels.

includelist of str

List of additional channels to include. If empty do not include any.

excludelist of str | str

List of channels to exclude. If ‘bads’ (default), exclude channels in info['bads'].

selectionlist of str

Restrict sensor channels (MEG, EEG) to this list of channel names.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only. Defaults to self.verbose.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

pick_channels

Notes

New in version 0.9.0.

reorder_channels(ch_names)[source]

Reorder channels.

Parameters
ch_nameslist

The desired channel order.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

Channel names must be unique. Channels that are not in ch_names are dropped.

New in version 0.16.0.

save(fname, overwrite=False)[source]

Save TFR object to hdf5 file.

Parameters
fnamestr

The file name, which should end with -tfr.h5.

overwritebool

If True, overwrite file (if it exists). Defaults to False.

See also

read_tfrs, write_tfrs