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:
- info
mne.Info
The
mne.Info
object with information about the sensors and methods of measurement.- data
ndarray
, shape (n_epochs, n_channels, n_freqs, n_times) The data.
- times
ndarray
, shape (n_times,) The time values in seconds.
- freqs
ndarray
, shape (n_freqs,) The frequencies in Hz.
- comment
str
|None
, defaultNone
Comment on the data, e.g., the experimental condition.
- method
str
|None
, defaultNone
Comment on the method used to compute the data, e.g., morlet wavelet.
- events
ndarray
, 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_id
dict
|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_log
tuple
|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. Seemne.Epochs
for further details.- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- info
- Attributes:
- info
mne.Info
The
mne.Info
object with information about the sensors and methods of measurement.ch_names
list
Channel names.
- data
ndarray
, shape (n_epochs, n_channels, n_freqs, n_times) The data array.
times
ndarray
, shape (n_times,)Time vector in seconds.
- freqs
ndarray
, shape (n_freqs,) The frequencies in Hz.
- comment
str
Comment on dataset. Can be the condition.
- method
str
|None
, defaultNone
Comment on the method used to compute the data, e.g., morlet wavelet.
- events
ndarray
, shape (n_events, 3) |None
Array containing sample information as event_id
- event_id
dict
|None
Names of conditions correspond to event_ids
- selection
array
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_log
tuple
oftuple
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'
'USER'
For user-defined reasons (see
drop()
).
metadata
pandas.DataFrame
, shape (n_events, n_cols) |None
Get the metadata.
- Notes
- —–
- .. versionadded:: 0.13.0
- info
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])Warning
LEGACY: New code should use inst.pick(...).
pick_types
([meg, eeg, stim, eog, ecg, emg, ...])Warning
LEGACY: New code should use inst.pick(...).
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_type
str
Channel type to check for. Can be e.g.
'meg'
,'eeg'
,'stim'
, etc.
- ch_type
- Returns:
- in
bool
Whether or not the instance contains the given channel type.
- in
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:
- item
slice
, array_like,str
, orlist
See below for use cases.
- item
- Returns:
- epochsinstance of
Epochs
See below for use cases.
- epochsinstance of
Notes
Epochs can be accessed as
epochs[...]
in several ways:Integer or slice:
epochs[idx]
will return anEpochs
object with a subset of epochs chosen by index (supports single index and Python-style slicing).String:
epochs['name']
will return anEpochs
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'
), thenepochs['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 anEpochs
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, aKeyError
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 anEpochs
object with a subset of epochs (and matching metadata) selected by the query called withself.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 whosecol_b
metadata was the string ‘foo’. Query-based indexing only works if Pandas is installed andself.metadata
is apandas.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 ofmne.Epochs.next()
.
- __len__()[source]#
Return the number of epochs.
- Returns:
- n_epochs
int
The number of remaining epochs.
- n_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_list
list
A list of objects to append to self. Must contain all the same type as the current object.
- force_update_info
bool
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.
- add_list
- Returns:
See also
Notes
If
self
is a Raw instance that has been preloaded into anumpy.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:
- Returns:
- 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’)
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- Returns:
- instinstance of
AverageTFR
The modified instance.
- instinstance of
Examples using
apply_baseline
:Non-parametric 1 sample cluster statistic on single trial power
Non-parametric 1 sample cluster statistic on single trial powerNon-parametric between conditions cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial powerMass-univariate twoway repeated measures ANOVA on single trial power
Mass-univariate twoway repeated measures ANOVA on single trial powerSpatiotemporal permutation F-test on full sensor data
Spatiotemporal permutation F-test on full sensor data
- average(method='mean', dim='epochs', copy=False)[source]#
Average the data across epochs.
- Parameters:
- method
str
|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.
- copy
bool
Whether to return a copy of the modified instance, or modify in place. Ignored when
dim='epochs'
because a new instance must be returned.
- method
- Returns:
- aveinstance of
AverageTFR
|EpochsTFR
The averaged data.
- aveinstance of
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 inmedian
. See a discussion in scipy:Examples using
average
:Compute and visualize ERDS maps
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 ofAverageTFR
A copy of the instance.
- copyinstance of
- crop(tmin=None, tmax=None, fmin=None, fmax=None, include_tmax=True)[source]#
Crop data to a given time interval in place.
- Parameters:
- tmin
float
|None
Start time of selection in seconds.
- tmax
float
|None
End time of selection in seconds.
- fmin
float
|None
Lowest frequency of selection in Hz.
New in version 0.18.0.
- fmax
float
|None
Highest frequency of selection in Hz.
New in version 0.18.0.
- include_tmax
bool
If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).
New in version 0.19.
- tmin
- Returns:
- instinstance of
AverageTFR
The modified instance.
- instinstance of
Examples using
crop
:Non-parametric 1 sample cluster statistic on single trial power
Non-parametric 1 sample cluster statistic on single trial powerCompute and visualize ERDS maps
Compute and visualize ERDS mapsCompute source level time-frequency timecourses using a DICS beamformer
Compute source level time-frequency timecourses using a DICS beamformer
- decimate(decim, offset=0, verbose=None)[source]#
Decimate the time-series data.
- Parameters:
- decim
int
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.- offset
int
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.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- decim
- Returns:
- instMNE-object
The decimated object.
See also
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 asinst.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.
- ch_namesiterable or
- Returns:
See also
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:
- picks
str
| 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 ininfo['bads']
will be included if their names or indices are explicitly provided.- unique
bool
Whether to return only unique channel types. Default is
False
.- only_data_chs
bool
Whether to ignore non-data channels. Default is
False
.
- picks
- Returns:
- channel_types
list
The channel types.
- channel_types
- property metadata#
Get the metadata.
- pick(picks, exclude=(), *, verbose=None)[source]#
Pick a subset of channels.
- Parameters:
- picks
str
| 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 ininfo['bads']
will be included if their names or indices are explicitly provided.- exclude
list
|str
Set of channels to exclude, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”).
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.New in version 0.24.0.
- picks
- Returns:
- pick_channels(ch_names, ordered=None, *, verbose=None)[source]#
Warning
LEGACY: New code should use inst.pick(…).
Pick some channels.
- Parameters:
- ch_names
list
The list of channels to select.
- ordered
bool
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.
Changed in version 1.5: The default changed from False in 1.4 to True in 1.5.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.New in version 1.1.
- ch_names
- Returns:
See also
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, eyetrack=False, include=(), exclude='bads', selection=None, verbose=None)[source]#
Warning
LEGACY: New code should use inst.pick(…).
Pick some channels by type and names.
- Parameters:
- meg
bool
|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.
- eeg
bool
If True include EEG channels.
- stim
bool
If True include stimulus channels.
- eog
bool
If True include EOG channels.
- ecg
bool
If True include ECG channels.
- emg
bool
If True include EMG channels.
- ref_meg
bool
|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 themeg
parameter.- misc
bool
If True include miscellaneous analog channels.
- resp
bool
If
True
include respiratory channels.- chpi
bool
If True include continuous HPI coil channels.
- exci
bool
Flux excitation channel used to be a stimulus channel.
- ias
bool
Internal Active Shielding data (maybe on Triux only).
- syst
bool
System status channel information (on Triux systems only).
- seeg
bool
Stereotactic EEG channels.
- dipole
bool
Dipole time course channels.
- gof
bool
Dipole goodness of fit channels.
- bio
bool
Bio channels.
- ecog
bool
Electrocorticography channels.
- fnirs
bool
|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).
- csd
bool
EEG-CSD channels.
- dbs
bool
Deep brain stimulation channels.
- temperature
bool
Temperature channels.
- gsr
bool
Galvanic skin response channels.
- eyetrack
bool
|str
Eyetracking channels. If True include all eyetracking channels. If False (default) include none. If string it can be ‘eyegaze’ (to include eye position channels) or ‘pupil’ (to include pupil-size channels).
- include
list
ofstr
List of additional channels to include. If empty do not include any.
- exclude
list
ofstr
|str
List of channels to exclude. If ‘bads’ (default), exclude channels in
info['bads']
.- selection
list
ofstr
Restrict sensor channels (MEG, EEG, etc.) to this list of channel names.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- meg
- Returns:
See also
Notes
New in version 0.9.0.
- reorder_channels(ch_names)[source]#
Reorder channels.
- Parameters:
- ch_names
list
The desired channel order.
- ch_names
- Returns:
See also
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:
- fnamepath-like
The file name, which should end with
-tfr.h5
.- overwrite
bool
If True (default False), overwrite the destination file if it exists.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
See also
- shift_time(tshift, relative=True)[source]#
Shift time scale in epoched or evoked data.
- Parameters:
- tshift
float
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.- relative
bool
If True, increase or decrease time values by
tshift
seconds. Otherwise, shift the time values such that the time of the first sample equalstshift
.
- 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.
- 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, unlessindex
is notNone
(in which case the columns specified inindex
will be used to form the DataFrame’s index instead).'epoch'
, and'condition'
are not supported forAverageTFR
.- Parameters:
- picks
str
| 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 ininfo['bads']
will be included if their names or indices are explicitly provided.- index
str
|list
ofstr
|None
Kind of index to use for the DataFrame. If
None
, a sequential integer index (pandas.RangeIndex
) will be used. If'time'
, apandas.Index
orpandas.TimedeltaIndex
will be used (depending on the value oftime_format
). If a list of two or more string values, apandas.MultiIndex
will be created. Valid string values are'time'
,'freq'
,'epoch'
, and'condition'
forEpochsTFR
and'time'
and'freq'
forAverageTFR
. Defaults toNone
.- long_format
bool
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 toFalse
.- time_format
str
|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 topandas.Timedelta
values. Default isNone
.New in version 0.23.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- picks
- Returns:
- dfinstance of
pandas.DataFrame
A dataframe suitable for usage with other statistical/plotting/analysis packages.
- dfinstance of
Examples using
to_data_frame
:Compute and visualize ERDS maps
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
Compute source level time-frequency timecourses using a DICS beamformer