mne.time_frequency.EpochsTFR#

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

Container for Time-Frequency data on epochs.

Can for example store induced power at sensor level.

Parameters:

infomne.Info: The mne.Info object with information about the sensors and methods of measurement.
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.
selectioniterable | None: Iterable of indices of selected epochs. If None, will be automatically generated, corresponding to all non-zero events.

New in version 0.23.
drop_logtuple | None: Tuple of tuple of strings indicating which epochs have been marked to be ignored.

New in version 0.23.
metadatainstance of pandas.DataFrame | None: A pandas.DataFrame containing pertinent information for each trial. See mne.Epochs for further details.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Attributes:

infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

ch_nameslist

Channel names.

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

The data array.

timesndarray, shape (n_times,)

Time vector 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

selectionarray

List of indices of selected events (not dropped or ignored etc.). For example, if the original event array had 4 events and the second event has been dropped, this attribute would be np.array([0, 2, 3]).

drop_logtuple of tuple

A tuple of the same length as the event array used to initialize the EpochsTFR object. If the i-th original event is still part of the selection, drop_log[i] will be an empty tuple; otherwise it will be a tuple of the reasons the event is not longer in the selection, e.g.:

'IGNORED'
If it isn’t part of the current subset defined by the user
'NO_DATA' or 'TOO_SHORT'
If epoch didn’t contain enough data names of channels that exceeded the amplitude threshold
'EQUALIZED_COUNTS'
See equalize_event_counts()
'USER'
For user-defined reasons (see drop()).

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.
`__iter__`()	Facilitate iteration over epochs.
`__len__`()	Return the number of epochs.
`add_channels`(add_list[, force_update_info])	Append new channels to the instance.
`add_reference_channels`(ref_channels)	Add reference channels to data that consists of all zeros.
`apply_baseline`(baseline[, mode, verbose])	Baseline correct the data.
`average`([method, dim, copy])	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.
`decimate`(decim[, offset, verbose])	Decimate the time-series data.
`drop_channels`(ch_names[, on_missing])	Drop channel(s).
`get_channel_types`([picks, unique, only_data_chs])	Get a list of channel type for each channel.
`next`([return_event_id])	Iterate over epoch data.
`pick`(picks[, exclude, verbose])	Pick a subset of channels.
`pick_channels`(ch_names[, ordered, verbose])	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, verbose])	Save TFR object to hdf5 file.
`shift_time`(tshift[, relative])	Shift time scale in epoched or evoked data.
`time_as_index`(times[, use_rounding])	Convert time to indices.
`to_data_frame`([picks, index, long_format, ...])	Export data in tabular structure as a pandas DataFrame.

__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:

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).
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.
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'.
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.

__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.

add_reference_channels(ref_channels)[source]#

Add reference channels to data that consists of all zeros.

Adds reference channels to data that were not included during recording. This is useful when you need to re-reference your data to different channels. These added channels will consist of all zeros.

Parameters:

ref_channelsstr | list of str: Name of the electrode(s) which served as the reference in the recording. If a name is provided, a corresponding channel is added and its data is set to 0. This is useful for later re-referencing.

Returns:

instinstance of Raw | Epochs | Evoked: The modified instance.

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.

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 | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of AverageTFR: The modified instance.

Examples using apply_baseline:

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power

Spatiotemporal permutation F-test on full sensor data

average(method='mean', dim='epochs', copy=False)[source]#

Average the data across epochs.

Parameters:

methodstr | callable(): How to combine the data. If “mean”/”median”, the mean/median are returned. Otherwise, must be a callable which, when passed an array of shape (n_epochs, n_channels, n_freqs, n_time) returns an array of shape (n_channels, n_freqs, n_time). Note that due to file type limitations, the kind for all these will be “average”.
dim‘epochs’ | ‘freqs’ | ‘times’: The dimension along which to combine the data.
copybool: Whether to return a copy of the modified instance, or modify in place. Ignored when dim='epochs' because a new instance must be returned.

Returns:

aveinstance of AverageTFR | EpochsTFR: The averaged data.

Notes

Passing in np.median is considered unsafe when there is complex data because NumPy doesn’t compute the marginal median. Numpy currently sorts the complex values by real part and return whatever value is computed. Use with caution. We use the marginal median in the complex case (i.e. the median of each component separately) if one passes in median. See a discussion in scipy:

scipy/scipy#12676

Examples using average:

Compute and visualize ERDS maps

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:

Non-parametric 1 sample cluster statistic on single trial power

Compute and visualize ERDS maps

decimate(decim, offset=0, verbose=None)[source]#

Decimate the time-series data.

Parameters:

decimint: Factor by which to subsample the data.

Warning

Low-pass filtering is not performed, this simply selects every Nth sample (where N is the value passed to decim), i.e., it compresses the signal (see Notes). If the data are not properly filtered, aliasing artifacts may occur.
offsetint: Apply an offset to where the decimation starts relative to the sample corresponding to t=0. The offset is in samples at the current sampling rate.

New in version 0.12.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instMNE-object: The decimated object.

See also

mne.Epochs.resample
mne.io.Raw.resample

Notes

For historical reasons, decim / “decimation” refers to simply subselecting samples from a given signal. This contrasts with the broader signal processing literature, where decimation is defined as (quoting [1], p. 172; which cites [2]):

“… a general system for downsampling by a factor of M is the one shown in Figure 4.23. Such a system is called a decimator, and downsampling by lowpass filtering followed by compression [i.e, subselecting samples] has been termed decimation (Crochiere and Rabiner, 1983).”

Hence “decimation” in MNE is what is considered “compression” in the signal processing community.

Decimation can be done multiple times. For example, inst.decimate(2).decimate(2) will be the same as inst.decimate(4).

If decim is 1, this method does not copy the underlying data.

New in version 0.10.0.

References

drop_channels(ch_names, on_missing='raise')[source]#

Drop channel(s).

Parameters:

ch_namesiterable or str: Iterable (e.g. list) of channel name(s) or channel name to remove.
on_missing‘raise’ | ‘warn’ | ‘ignore’: Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when entries in ch_names are not present in the raw instance.

New in version 0.23.0.

Returns:

instinstance of Raw, Epochs, or Evoked: The modified instance.

See also

reorder_channels
pick_channels
pick_types

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 | array_like | 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.

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=(), *, verbose=None)[source]#

Pick a subset of channels.

Parameters:

picksstr | array_like | 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”).
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

New in version 0.24.0.

Returns:

instinstance of Raw, Epochs, or Evoked: The modified instance.

pick_channels(ch_names, ordered=False, *, verbose=None)[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.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

New in version 1.1.

Returns:

instinstance of Raw, Epochs, or Evoked: The modified instance.

See also

drop_channels
pick_types
reorder_channels

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, dbs=False, temperature=False, gsr=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 respiratory channels.
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.
dbsbool: Deep brain stimulation channels.
temperaturebool: Temperature channels.
gsrbool: Galvanic skin response 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, etc.) to this list of channel names.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

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.

See also

drop_channels
pick_types
pick_channels

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, *, verbose=None)[source]#

Save TFR object to hdf5 file.

Parameters:

fnamestr: The file name, which should end with -tfr.h5.
overwritebool: If True (default False), overwrite the destination file if it exists.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

See also

read_tfrs, write_tfrs

shift_time(tshift, relative=True)[source]#

Shift time scale in epoched or evoked data.

Parameters:

tshiftfloat: The (absolute or relative) time shift in seconds. If relative is True, positive tshift increases the time value associated with each sample, while negative tshift decreases it.
relativebool: If True, increase or decrease time values by tshift seconds. Otherwise, shift the time values such that the time of the first sample equals tshift.

Returns:

epochsMNE-object: The modified instance.

Notes

This method allows you to shift the time values associated with each data sample by an arbitrary amount. It does not resample the signal or change the data values in any way.

time_as_index(times, use_rounding=False)[source]#

Convert time to indices.

Parameters:

timeslist-like | float | int: List of numbers or a number representing points in time.
use_roundingbool: If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices.

Returns:

indexndarray: Indices corresponding to the times supplied.

property times#: Time vector in seconds.

property tmax#: Last time point.

property tmin#: First time point.

to_data_frame(picks=None, index=None, long_format=False, time_format=None, *, verbose=None)[source]#

Export data in tabular structure as a pandas DataFrame.

Channels are converted to columns in the DataFrame. By default, additional columns 'time', 'freq', 'epoch', and 'condition' (epoch event description) are added, unless index is not None (in which case the columns specified in index will be used to form the DataFrame’s index instead). 'epoch', and 'condition' are not supported for AverageTFR.

Parameters:

picksstr | array_like | 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.
indexstr | list of str | None: Kind of index to use for the DataFrame. If None, a sequential integer index (pandas.RangeIndex) will be used. If 'time', a pandas.Float64Index, pandas.Int64Index, or pandas.TimedeltaIndex will be used (depending on the value of time_format). If a list of two or more string values, a pandas.MultiIndex will be created. Valid string values are 'time', 'freq', 'epoch', and 'condition' for EpochsTFR and 'time' and 'freq' for AverageTFR. Defaults to None.
long_formatbool: If True, the DataFrame is returned in long format where each row is one observation of the signal at a unique combination of time point, channel, epoch number, and condition. For convenience, a ch_type column is added to facilitate subsetting the resulting DataFrame. Defaults to False.
time_formatstr | None: Desired time format. If None, no conversion is applied, and time values remain as float values in seconds. If 'ms', time values will be rounded to the nearest millisecond and converted to integers. If 'timedelta', time values will be converted to pandas.Timedelta values. Default is None.

New in version 0.23.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

dfinstance of pandas.DataFrame: A dataframe suitable for usage with other statistical/plotting/analysis packages.

Examples using to_data_frame:

Compute and visualize ERDS maps

Examples using `mne.time_frequency.EpochsTFR`#

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power

Spatiotemporal permutation F-test on full sensor data

Compute and visualize ERDS maps

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

mne.time_frequency.EpochsTFR#

Examples using mne.time_frequency.EpochsTFR#

Examples using `mne.time_frequency.EpochsTFR`#