mne.EpochsArray

class mne.EpochsArray(data, info, events=None, tmin=0.0, event_id=None, reject=None, flat=None, reject_tmin=None, reject_tmax=None, baseline=None, proj=True, on_missing='raise', metadata=None, selection=None, *, drop_log=None, raw_sfreq=None, verbose=None)

Epochs object from numpy array.

Parameters:

dataarray, shape (n_epochs, n_channels, n_times)

The channels’ time series for each epoch. See notes for proper units of measure.

infomne.Info

The mne.Info object with information about the sensors and methods of measurement. Consider using mne.create_info() to populate this structure.

eventsndarray of int, shape (n_events, 3)

The identity and timing of experimental events, around which the epochs were created. See events for more information.Events that don’t match the events of interest as specified by event_id will be marked as IGNORED in the drop log.

tminfloat

Start time before event. If nothing provided, defaults to 0.

event_idint | list of int | dict | str | list of str | None

The id of the events to consider. If dict, the keys can later be used to access associated events. Example: dict(auditory=1, visual=3). If int, a dict will be created with the id as string. If a list of int, all events with the IDs specified in the list are used. If a str or list of str, events must be None to use annotations and then the IDs must be the name(s) of the annotations to use. If None, all events will be used and a dict is created with string integer names corresponding to the event id integers.

rejectdict | None

Reject epochs based on maximum peak-to-peak signal amplitude (PTP), i.e. the absolute difference between the lowest and the highest signal value. In each individual epoch, the PTP is calculated for every channel. If the PTP of any one channel exceeds the rejection threshold, the respective epoch will be dropped.

The dictionary keys correspond to the different channel types; valid keys can be any channel type present in the object.

Example:

reject = dict(grad=4000e-13,  # unit: T / m (gradiometers)
              mag=4e-12,      # unit: T (magnetometers)
              eeg=40e-6,      # unit: V (EEG channels)
              eog=250e-6      # unit: V (EOG channels)
              )

Note

Since rejection is based on a signal difference calculated for each channel separately, applying baseline correction does not affect the rejection procedure, as the difference will be preserved.

Note

To constrain the time period used for estimation of signal quality, pass the reject_tmin and reject_tmax parameters.

If reject is None (default), no rejection is performed.

flatdict | None

Reject epochs based on minimum peak-to-peak signal amplitude (PTP). Valid keys can be any channel type present in the object. The values are floats that set the minimum acceptable PTP. If the PTP is smaller than this threshold, the epoch will be dropped. If None then no rejection is performed based on flatness of the signal.

Note

To constrain the time period used for estimation of signal quality, pass the reject_tmin and reject_tmax parameters.

reject_tmin, reject_tmaxfloat | None

Start and end of the time window used to reject epochs based on peak-to-peak (PTP) amplitudes as specified via reject and flat. The default None corresponds to the first and last time points of the epochs, respectively.

Note

This parameter controls the time period used in conjunction with both, reject and flat.

baselineNone | tuple of length 2

The time interval to consider as “baseline” when applying baseline correction. If None, do not apply baseline correction. If a tuple (a, b), the interval is between a and b (in seconds), including the endpoints. If a is None, the beginning of the data is used; and if b is None, it is set to the end of the data. If (None, None), the entire time interval is used.

Note

The baseline (a, b) includes both endpoints, i.e. all timepoints t such that a <= t <= b.

Correction is applied to each epoch and channel individually in the following way:

1. Calculate the mean signal of the baseline period.

2. Subtract this mean from the entire epoch.

Defaults to None, i.e. no baseline correction.

projbool | ‘delayed’

Apply SSP projection vectors. If proj is ‘delayed’ and reject is not None the single epochs will be projected before the rejection decision, but used in unprojected state if they are kept. This way deciding which projection vectors are good can be postponed to the evoked stage without resulting in lower epoch counts and without producing results different from early SSP application given comparable parameters. Note that in this case baselining, detrending and temporal decimation will be postponed. If proj is False no projections will be applied which is the recommended value if SSPs are not used for cleaning the data.

on_missing‘raise’ | ‘warn’ | ‘ignore’

What to do if one or several event ids are not found in the recording. Valid keys are ‘raise’ | ‘warn’ | ‘ignore’ Default is 'raise'. If 'warn', it will proceed but warn; if 'ignore', it will proceed silently.

Note

If none of the event ids are found in the data, an error will be automatically generated irrespective of this parameter.

metadatainstance of pandas.DataFrame | None

A pandas.DataFrame specifying metadata about each epoch. If not None, len(metadata) must equal len(events). For save/load compatibility, the DataFrame may only contain str, int, float, and bool values. If not None, then pandas-style queries may be used to select subsets of data, see mne.Epochs.__getitem__(). When the Epochs object is subsetted, the metadata is subsetted accordingly, and the row indices will be modified to match Epochs.selection.

New in v0.16.

selectioniterable | None

Iterable of indices of selected epochs. If None, will be automatically generated, corresponding to all non-zero events.

drop_logtuple | None

Tuple of tuple of strings indicating which epochs have been marked to be ignored.

New in v1.3.

raw_sfreqfloat

The original Raw object sampling rate. If None, then it is set to info['sfreq'].

New in v1.3.

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:

annotations

ch_names

Channel names.

compensation_grade

The current gradient compensation grade.

filename

The filename if the epochs are loaded from disk.

metadata

Get the metadata.

proj

Whether or not projections are active.

times

Time vector in seconds.

tmax

Last time point.

tmin

First time point.

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_annotations_to_metadata([overwrite])	Add raw annotations into the Epochs metadata data frame.
add_channels(add_list[, force_update_info])	Append new channels to the instance.
add_proj(projs[, remove_existing, verbose])	Add SSP projection vectors.
add_reference_channels(ref_channels)	Add reference channels to data that consists of all zeros.
anonymize([daysback, keep_his, verbose])	Anonymize measurement information in place.
apply_baseline([baseline, verbose])	Baseline correct epochs.
apply_function(fun[, picks, dtype, n_jobs, ...])	Apply a function to a subset of channels.
apply_hilbert([picks, envelope, n_jobs, ...])	Compute analytic signal or envelope for a subset of channels/vertices.
apply_proj([verbose])	Apply the signal space projection (SSP) operators to the data.
as_type([ch_type, mode])	Compute virtual epochs using interpolated fields.
average([picks, method, by_event_type])	Compute an average over epochs.
compute_psd([method, fmin, fmax, tmin, ...])	Perform spectral analysis on sensor data.
compute_tfr(method, freqs, *[, tmin, tmax, ...])	Compute a time-frequency representation of epoched data.
copy()	Return copy of Epochs instance.
crop([tmin, tmax, include_tmax, verbose])	Crop a time interval from the epochs.
decimate(decim[, offset, verbose])	Decimate the time-series data.
del_proj([idx])	Remove SSP projection vector.
drop(indices[, reason, verbose])	Drop epochs based on indices or boolean mask.
drop_bad([reject, flat, verbose])	Drop bad epochs without retaining the epochs data.
drop_channels(ch_names[, on_missing])	Drop channel(s).
drop_log_stats([ignore])	Compute the channel stats based on a drop_log from Epochs.
equalize_event_counts([event_ids, method, ...])	Equalize the number of trials in each condition.
export(fname[, fmt, overwrite, verbose])	Export Epochs to external formats.
filter(l_freq, h_freq[, picks, ...])	Filter a subset of channels/vertices.
get_annotations_per_epoch()	Get a list of annotations that occur during each epoch.
get_channel_types([picks, unique, only_data_chs])	Get a list of channel type for each channel.
get_data([picks, item, units, tmin, tmax, ...])	Get all epochs as a 3D array.
get_montage()	Get a DigMontage from instance.
interpolate_bads([reset_bads, mode, origin, ...])	Interpolate bad MEG and EEG channels.
iter_evoked([copy])	Iterate over epochs as a sequence of Evoked objects.
load_data()	Load the data if not already preloaded.
next([return_event_id])	Iterate over epoch data.
pick(picks[, exclude, verbose])	Pick a subset of channels.
pick_channels(ch_names[, ordered, verbose])
pick_types([meg, eeg, stim, eog, ecg, emg, ...])
plot([picks, scalings, n_epochs, ...])	Visualize epochs.
plot_drop_log([threshold, n_max_plot, ...])	Show the channel stats based on a drop_log from Epochs.
plot_image([picks, sigma, vmin, vmax, ...])	Plot Event Related Potential / Fields image.
plot_projs_topomap([ch_type, sensors, ...])	Plot SSP vector.
plot_psd([fmin, fmax, tmin, tmax, picks, ...])	Plot power or amplitude spectra.
plot_psd_topo([tmin, tmax, fmin, fmax, ...])
plot_psd_topomap([bands, tmin, tmax, ...])
plot_sensors([kind, ch_type, title, ...])	Plot sensor positions.
plot_topo_image([layout, sigma, vmin, vmax, ...])	Plot Event Related Potential / Fields image on topographies.
rename_channels(mapping[, allow_duplicates, ...])	Rename channels.
reorder_channels(ch_names)	Reorder channels.
resample(sfreq, *[, npad, window, n_jobs, ...])	Resample data.
reset_drop_log_selection()	Reset the drop_log and selection entries.
save(fname[, split_size, fmt, overwrite, ...])	Save epochs in a fif file.
savgol_filter(h_freq[, verbose])	Filter the data using Savitzky-Golay polynomial method.
set_annotations(annotations[, on_missing, ...])	Setter for Epoch annotations from Raw.
set_channel_types(mapping, *[, ...])	Specify the sensor types of channels.
set_eeg_reference([ref_channels, ...])	Specify which reference to use for EEG data.
set_meas_date(meas_date)	Set the measurement start date.
set_montage(montage[, match_case, ...])	Set EEG/sEEG/ECoG/DBS/fNIRS channel positions and digitization points.
shift_time(tshift[, relative])	Shift time scale in epoched or evoked data.
standard_error([picks, by_event_type])	Compute standard error over epochs.
subtract_evoked([evoked])	Subtract an evoked response from each epoch.
time_as_index(times[, use_rounding])	Convert time to indices.
to_data_frame([picks, index, scalings, ...])	Export data in tabular structure as a pandas DataFrame.

See also

create_info

EvokedArray

io.RawArray

Notes

Proper units of measure:

• V: eeg, eog, seeg, dbs, emg, ecg, bio, ecog

• T: mag

• T/m: grad

• M: hbo, hbr

• Am: dipole

• AU: misc

EpochsArray does not set Annotations. If you would like to create simulated data with Annotations that are then preserved in the Epochs object, you would use mne.io.RawArray first and then create an mne.Epochs object.

__contains__(ch_type)

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) → Self

Return an Epochs object with a copied subset of epochs.

Parameters:

itemint | slice | array_like | str

See Notes for use cases.

Returns:

epochsinstance of Epochs

The subset of epochs.

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 v0.16.

__iter__()

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__()

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_annotations_to_metadata(overwrite=False)

Add raw annotations into the Epochs metadata data frame.

Adds three columns to the metadata consisting of a list in each row: - annot_onset: the onset of each Annotation within the Epoch relative to the start time of the Epoch (in seconds). - annot_duration: the duration of each Annotation within the Epoch in seconds. - annot_description: the free-form text description of each Annotation.

Parameters:

overwritebool

Whether to overwrite existing columns in metadata or not. Default is False.

Returns:

selfinstance of Epochs

The modified instance (instance is also modified inplace).

Notes

New in v1.0.

add_channels(add_list, force_update_info=False)

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 v0.12.

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_proj(projs, remove_existing=False, verbose=None)

Add SSP projection vectors.

Parameters:

projslist

List with projection vectors.

remove_existingbool

Remove the projection vectors currently in the file.

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:

selfinstance of Raw | Epochs | Evoked

The data container.

add_reference_channels(ref_channels)

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.

anonymize(daysback=None, keep_his=False, verbose=None)

Anonymize measurement information in place.

Parameters:

daysbackint | None

Number of days to subtract from all dates. If None (default), the acquisition date, info['meas_date'], will be set to January 1ˢᵗ, 2000. This parameter is ignored if info['meas_date'] is None (i.e., no acquisition date has been set).

keep_hisbool

If True, his_id of subject_info will not be overwritten. Defaults to False.

Warning

This could mean that info is not fully anonymized. Use with caution.

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 | Evoked

The modified instance.

Notes

Removes potentially identifying information if it exists in info. Specifically for each of the following we use:

• meas_date, file_id, meas_id

  A default value, or as specified by daysback.

• subject_info

  Default values, except for ‘birthday’ which is adjusted to maintain the subject age.

• experimenter, proj_name, description

  Default strings.

• utc_offset

  None.

• proj_id

  Zeros.

• proc_history

  Dates use the meas_date logic, and experimenter a default string.

• helium_info, device_info

  Dates use the meas_date logic, meta info uses defaults.

If info['meas_date'] is None, it will remain None during processing the above fields.

Operates in place.

New in v0.13.0.

apply_baseline(baseline=(None, 0), *, verbose=None)

Baseline correct epochs.

Parameters:

baselineNone | tuple of length 2

The time interval to consider as “baseline” when applying baseline correction. If None, do not apply baseline correction. If a tuple (a, b), the interval is between a and b (in seconds), including the endpoints. If a is None, the beginning of the data is used; and if b is None, it is set to the end of the data. If (None, None), the entire time interval is used.

Note

The baseline (a, b) includes both endpoints, i.e. all timepoints t such that a <= t <= b.

Correction is applied to each epoch and channel individually in the following way:

1. Calculate the mean signal of the baseline period.

2. Subtract this mean from the entire epoch.

Defaults to (None, 0), i.e. beginning of the the data until time point zero.

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:

epochsinstance of Epochs

The baseline-corrected Epochs object.

Notes

Baseline correction can be done multiple times, but can never be reverted once the data has been loaded.

New in v0.10.0.

apply_function(fun, picks=None, dtype=None, n_jobs=None, channel_wise=True, verbose=None, **kwargs)

Apply a function to a subset of channels.

The function fun is applied to the channels defined in picks. The epochs object’s data is modified in-place. If the function returns a different data type (e.g. numpy.complex128) it must be specified using the dtype parameter, which causes the data type of all the data to change (even if the function is only applied to channels in picks). The object has to have the data loaded e.g. with preload=True or self.load_data().

Note

If n_jobs > 1, more memory is required as len(picks) * n_times additional time points need to be temporarily stored in memory.

Note

If the data type changes (dtype != None), more memory is required since the original and the converted data needs to be stored in memory.

Parameters:

funcallable()

A function to be applied to the channels. The first argument of fun has to be a timeseries (numpy.ndarray). The function must operate on an array of shape (n_times,) if channel_wise=True and (len(picks), n_times) otherwise. The function must return an ndarray shaped like its input.

Note

If channel_wise=True, one can optionally access the index and/or the name of the currently processed channel within the applied function. This can enable tailored computations for different channels. To use this feature, add ch_idx and/or ch_name as additional argument(s) to your function definition.

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 data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

dtypenumpy.dtype

Data type to use after applying the function. If None (default) the data type is not modified.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs. Ignored if channel_wise=False as the workload is split across channels.

channel_wisebool

Whether to apply the function to each channel in each epoch individually. If False, the function will be applied to all epochs and channels at once. Default True.

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.

**kwargsdict

Additional keyword arguments to pass to fun.

Returns:

selfinstance of Epochs

The epochs object with transformed data.

apply_hilbert(picks=None, envelope=False, n_jobs=None, n_fft='auto', *, verbose=None)

Compute analytic signal or envelope for a subset of channels/vertices.

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 data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

envelopebool

Compute the envelope signal of each channel/vertex. Default False. See Notes.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

n_fftint | None | str

Points to use in the FFT for Hilbert transformation. The signal will be padded with zeros before computing Hilbert, then cut back to original length. If None, n == self.n_times. If ‘auto’, the next highest fast FFT length will be use.

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:

selfinstance of Raw, Epochs, Evoked or SourceEstimate

The raw object with transformed data.

Notes

Parameters

If envelope=False, the analytic signal for the channels/vertices defined in picks is computed and the data of the Raw object is converted to a complex representation (the analytic signal is complex valued).

If envelope=True, the absolute value of the analytic signal for the channels/vertices defined in picks is computed, resulting in the envelope signal.

If envelope=False, more memory is required since the original raw data as well as the analytic signal have temporarily to be stored in memory. If n_jobs > 1, more memory is required as len(picks) * n_times additional time points need to be temporarily stored in memory.

Also note that the n_fft parameter will allow you to pad the signal with zeros before performing the Hilbert transform. This padding is cut off, but it may result in a slightly different result (particularly around the edges). Use at your own risk.

Analytic signal

The analytic signal “x_a(t)” of “x(t)” is:

x_a = F^{-1}(F(x) 2U) = x + i y

where “F” is the Fourier transform, “U” the unit step function, and “y” the Hilbert transform of “x”. One usage of the analytic signal is the computation of the envelope signal, which is given by “e(t) = abs(x_a(t))”. Due to the linearity of Hilbert transform and the MNE inverse solution, the enevlope in source space can be obtained by computing the analytic signal in sensor space, applying the MNE inverse, and computing the envelope in source space.

apply_proj(verbose=None)

Apply the signal space projection (SSP) operators to the data.

Parameters:

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:

selfinstance of Raw | Epochs | Evoked

The instance.

Notes

Once the projectors have been applied, they can no longer be removed. It is usually not recommended to apply the projectors at too early stages, as they are applied automatically later on (e.g. when computing inverse solutions). Hint: using the copy method individual projection vectors can be tested without affecting the original data. With evoked data, consider the following example:

projs_a = mne.read_proj('proj_a.fif')
projs_b = mne.read_proj('proj_b.fif')
# add the first, copy, apply and see ...
evoked.add_proj(a).copy().apply_proj().plot()
# add the second, copy, apply and see ...
evoked.add_proj(b).copy().apply_proj().plot()
# drop the first and see again
evoked.copy().del_proj(0).apply_proj().plot()
evoked.apply_proj()  # finally keep both

as_type(ch_type='grad', mode='fast')

Compute virtual epochs using interpolated fields.

Warning

Using virtual epochs to compute inverse can yield unexpected results. The virtual channels have '_v' appended at the end of the names to emphasize that the data contained in them are interpolated.

Parameters:

ch_typestr

The destination channel type. It can be ‘mag’ or ‘grad’.

modestr

Either 'accurate' or 'fast', determines the quality of the Legendre polynomial expansion used. 'fast' should be sufficient for most applications.

Returns:

epochsinstance of mne.EpochsArray

The transformed epochs object containing only virtual channels.

Notes

This method returns a copy and does not modify the data it operates on. It also returns an EpochsArray instance.

New in v0.20.0.

average(picks=None, method='mean', by_event_type=False)

Compute an average over epochs.

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 data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

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_time) returns an array of shape (n_channels, n_time). Note that due to file type limitations, the kind for all these will be “average”.

by_event_typebool

When False (the default) all epochs are processed together and a single Evoked object is returned. When True, epochs are first grouped by event type (as specified using the event_id parameter) and a list is returned containing a separate Evoked object for each event type. The .comment attribute is set to the label of the event type.

New in v0.24.0.

Returns:

evokedinstance of Evoked | list of Evoked

The averaged epochs. When by_event_type=True was specified, a list is returned containing a separate Evoked object for each event type. The list has the same order as the event types as specified in the event_id dictionary.

Notes

Computes an average of all epochs in the instance, even if they correspond to different conditions. To average by condition, do epochs[condition].average() for each condition separately.

When picks is None and epochs contain only ICA channels, no channels are selected, resulting in an error. This is because ICA channels are not considered data channels (they are of misc type) and only data channels are selected when picks is None.

The method parameter allows e.g. robust averaging. For example, one could do:

>>> from scipy.stats import trim_mean  
>>> trim = lambda x: trim_mean(x, 0.1, axis=0)  
>>> epochs.average(method=trim)  

This would compute the trimmed mean.

property ch_names

Channel names.

property compensation_grade

The current gradient compensation grade.

compute_psd(method='multitaper', fmin=0, fmax=inf, tmin=None, tmax=None, picks=None, proj=False, remove_dc=True, exclude=(), *, n_jobs=1, verbose=None, **method_kw)

Perform spectral analysis on sensor data.

Parameters:

method'welch' | 'multitaper'

Spectral estimation method. 'welch' uses Welch’s method [1], 'multitaper' uses DPSS tapers [2]. Default is 'multitaper'.

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is fmin=0, fmax=np.inf (spans all frequencies present in the data).

tmin, tmaxfloat | None

First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).

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 good data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

projbool

Whether to apply SSP projection vectors before spectral estimation. Default is False.

remove_dcbool

If True, the mean is subtracted from each segment before computing its spectrum.

excludelist of str | ‘bads’

Channel names to exclude. If 'bads', channels in info['bads'] are excluded; pass an empty list to include all channels (including “bad” channels, if any).

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

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.

**method_kw

Additional keyword arguments passed to the spectral estimation function (e.g., n_fft, n_overlap, n_per_seg, average, window for Welch method, or bandwidth, adaptive, low_bias, normalization for multitaper method). See psd_array_welch() and psd_array_multitaper() for details. Note that for Welch method if n_fft is unspecified its default will be the smaller of 2048 or the number of available time samples (taking into account tmin and tmax), not 256 as in psd_array_welch().

Returns:

spectruminstance of EpochsSpectrum

The spectral representation of each epoch.

Notes

New in v1.2.

References

[1] (1,2,3,4)

Peter D. Welch. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2):70–73, 1967. doi:10.1109/TAU.1967.1161901.

[2] (1,2,3,4,5)

David S. Slepian. Prolate spheroidal wave functions, fourier analysis, and uncertainty-V: the discrete case. Bell System Technical Journal, 57(5):1371–1430, 1978. doi:10.1002/j.1538-7305.1978.tb02104.x.

compute_tfr(method, freqs, *, tmin=None, tmax=None, picks=None, proj=False, output='power', average=False, return_itc=False, decim=1, n_jobs=None, verbose=None, **method_kw)

Compute a time-frequency representation of epoched data.

Parameters:

method'morlet' | 'multitaper' | 'stockwell' | None

Spectrotemporal power estimation method. 'morlet' uses Morlet wavelets, 'multitaper' uses DPSS tapers [2], and 'stockwell' uses the S-transform [3][4][5][6]. None (the default) only works when using __setstate__ and will raise an error otherwise.

freqsarray_like | ‘auto’ | None

The frequencies at which to compute the power estimates. If method='stockwell' this must be a length 2 iterable specifying lowest and highest frequencies, or 'auto' (to use all available frequencies). For other methods, must be an array of shape (n_freqs,). None (the default) only works when using __setstate__ and will raise an error otherwise.

tmin, tmaxfloat | None

First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).

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 good data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

projbool

Whether to apply SSP projection vectors before spectral estimation. Default is False.

outputstr

What kind of estimate to return. Allowed values are "complex", "phase", and "power". Default is "power".

averagebool

Whether to return average power across epochs (instead of single-trial power). average=True is not compatible with output="complex" or output="phase". Ignored if method="stockwell" (Stockwell method requires averaging). Default is False.

return_itcbool

Whether to return inter-trial coherence (ITC) as well as power estimates. If True then must specify average=True (or method="stockwell", average="auto"). Default is False.

decimint | slice

Decimation factor, applied after time-frequency decomposition.

• if int, returns tfr[..., ::decim] (keep only every Nth sample along the time axis).

• if slice, returns tfr[..., decim] (keep only the specified slice along the time axis).

Note

Decimation is done after convolutions and may create aliasing artifacts.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

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.

**method_kw

Additional keyword arguments passed to the spectrotemporal estimation function (e.g., n_cycles, use_fft, zero_mean for Morlet method, n_fft, width for Stockwell method, or n_cycles, use_fft, zero_mean, time_bandwidth for multitaper method). See tfr_array_morlet(), tfr_array_stockwell(), and tfr_array_multitaper() for additional details.

Returns:

tfrinstance of EpochsTFR or AverageTFR

The time-frequency-resolved power estimates.

itcinstance of AverageTFR

The inter-trial coherence (ITC). Only returned if return_itc=True.

Notes

If average=True (or method="stockwell", average="auto") the result will be an AverageTFR instead of an EpochsTFR.

New in v1.7.

References

[3]

R. G. Stockwell. Why use the S-transform? In Luigi Rodino, Bert-Wolfgang Schulze, and M. W. Wong, editors, Pseudo-Differential Operators: Partial Differential Equations and Time-Frequency Analysis, number 52 in Fields Institute Communications, pages 279–309. American Mathematical Society, Providence, RI, 2007. doi:10.1090/fic/052.

[4]

Ali Moukadem, Zied Bouguila, Djaffar Ould Abdeslam, and Alain Dieterlen. Stockwell transform optimization applied on the detection of split in heart sounds. In Proceedings of EUSIPCO-2014, 2015–2019. Lisbon, 2014. IEEE. URL: https://ieeexplore.ieee.org/document/6952743.

[5]

Katherine L. Wheat, Piers L. Cornelissen, Stephen J. Frost, and Peter C. Hansen. During visual word recognition, phonology is accessed within 100 ms and may be mediated by a speech production code: evidence from magnetoencephalography. Journal of Neuroscience, 30(15):5229–5233, 2010. doi:10.1523/JNEUROSCI.4448-09.2010.

[6]

Kevin A. Jones, Bernice Porjesz, David Chorlian, Madhavi Rangaswamy, Chella Kamarajan, Ajayan Padmanabhapillai, Arthur Stimus, and Henri Begleiter. S-transform time-frequency analysis of P300 reveals deficits in individuals diagnosed with alcoholism. Clinical Neurophysiology, 117(10):2128–2143, 2006. doi:10.1016/j.clinph.2006.02.028.

copy()

Return copy of Epochs instance.

Returns:

epochsinstance of Epochs

A copy of the object.

crop(tmin=None, tmax=None, include_tmax=True, verbose=None)

Crop a time interval from the epochs.

Parameters:

tminfloat | None

Start time of selection in seconds.

tmaxfloat | None

End time of selection in seconds.

include_tmaxbool

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

New in v0.19.

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:

epochsinstance of Epochs

The cropped epochs object, modified in-place.

Notes

Unlike Python slices, MNE time intervals by default include both their end points; crop(tmin, tmax) returns the interval tmin <= t <= tmax. Pass include_tmax=False to specify the half-open interval tmin <= t < tmax instead.

decimate(decim, offset=0, *, verbose=None)

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. See Resampling and decimating data for more information.

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 v0.12.

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 [7], p. 172; which cites [8]):

“… 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 v0.10.0.

References

[7]

Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Prentice Hall, Upper Saddle River, NJ, 2 edition edition, 1999. ISBN 978-0-13-754920-7.

[8]

Ronald E. Crochiere and Lawrence R. Rabiner. Multirate Digital Signal Processing. Pearson, Englewood Cliffs, NJ, 1 edition edition, 1983. ISBN 978-0-13-605162-6.

del_proj(idx='all')

Remove SSP projection vector.

Note

The projection vector can only be removed if it is inactive (has not been applied to the data).

Parameters:

idxint | list of int | str

Index of the projector to remove. Can also be “all” (default) to remove all projectors.

Returns:

selfinstance of Raw | Epochs | Evoked

The instance.

drop(indices, reason='USER', verbose=None)

Drop epochs based on indices or boolean mask.

Note

The indices refer to the current set of undropped epochs rather than the complete set of dropped and undropped epochs. They are therefore not necessarily consistent with any external indices (e.g., behavioral logs). To drop epochs based on external criteria, do not use the preload=True flag when constructing an Epochs object, and call this method before calling the mne.Epochs.drop_bad() or mne.Epochs.load_data() methods.

Parameters:

indicesarray of int or bool

Set epochs to remove by specifying indices to remove or a boolean mask to apply (where True values get removed). Events are correspondingly modified.

reasonlist | tuple | str

Reason(s) for dropping the epochs (‘ECG’, ‘timeout’, ‘blink’ etc). Reason(s) are applied to all indices specified. Default: ‘USER’.

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:

epochsinstance of Epochs

The epochs with indices dropped. Operates in-place.

drop_bad(reject='existing', flat='existing', verbose=None)

Drop bad epochs without retaining the epochs data.

Should be used before slicing operations.

Warning

This operation is slow since all epochs have to be read from disk. To avoid reading epochs from disk multiple times, use mne.Epochs.load_data().

Note

To constrain the time period used for estimation of signal quality, set epochs.reject_tmin and epochs.reject_tmax, respectively.

Parameters:

rejectdict | str | None

Reject epochs based on maximum peak-to-peak signal amplitude (PTP) or custom functions. Peak-to-peak signal amplitude is defined as the absolute difference between the lowest and the highest signal value. In each individual epoch, the PTP is calculated for every channel. If the PTP of any one channel exceeds the rejection threshold, the respective epoch will be dropped.

The dictionary keys correspond to the different channel types; valid keys can be any channel type present in the object.

Example:

reject = dict(grad=4000e-13,  # unit: T / m (gradiometers)
              mag=4e-12,      # unit: T (magnetometers)
              eeg=40e-6,      # unit: V (EEG channels)
              eog=250e-6      # unit: V (EOG channels)
              )

Custom rejection criteria can be also be used by passing a callable, e.g., to check for 99th percentile of absolute values of any channel across time being bigger than 1 mV. The callable must return a (good, reason) tuple: good must be bool and reason must be str, list, or tuple where each entry is a str:

reject = dict(
    eeg=lambda x: (
        (np.percentile(np.abs(x), 99, axis=1) > 1e-3).any(),
        "signal > 1 mV somewhere",
    )
)

Note

If rejection is based on a signal difference calculated for each channel separately, applying baseline correction does not affect the rejection procedure, as the difference will be preserved.

Note

If reject is a callable, than any criteria can be used to reject epochs (including maxima and minima).

If reject is None, no rejection is performed. If 'existing' (default), then the rejection parameters set at instantiation are used.

flatdict | str | None

Reject epochs based on minimum peak-to-peak signal amplitude (PTP) or a custom function. Valid keys can be any channel type present in the object. If using PTP, values are floats that set the minimum acceptable PTP. If the PTP is smaller than this threshold, the epoch will be dropped. If None then no rejection is performed based on flatness of the signal. If a custom function is used than flat can be used to reject epochs based on any criteria (including maxima and minima). If 'existing', then the flat parameters set during epoch creation are used.

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:

epochsinstance of Epochs

The epochs with bad epochs dropped. Operates in-place.

Notes

Dropping bad epochs can be done multiple times with different reject and flat parameters. However, once an epoch is dropped, it is dropped forever, so if more lenient thresholds may subsequently be applied, epochs.copy should be used.

drop_channels(ch_names, on_missing='raise')

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 v0.23.0.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

reorder_channels

pick_channels

pick_types

Notes

New in v0.9.0.

Examples using drop_channels:

Single trial linear regression analysis with the LIMO dataset

Single trial linear regression analysis with the LIMO dataset

drop_log_stats(ignore=('IGNORED',))

Compute the channel stats based on a drop_log from Epochs.

Parameters:

ignorelist

The drop reasons to ignore.

Returns:

percfloat

Total percentage of epochs dropped.

See also

plot_drop_log

equalize_event_counts(event_ids=None, method='mintime', *, random_state=None)

Equalize the number of trials in each condition.

It tries to make the remaining epochs occurring as close as possible in time. This method works based on the idea that if there happened to be some time-varying (like on the scale of minutes) noise characteristics during a recording, they could be compensated for (to some extent) in the equalization process. This method thus seeks to reduce any of those effects by minimizing the differences in the times of the events within a Epochs instance. For example, if one event type occurred at time points [1, 2, 3, 4, 120, 121] and the another one at [3.5, 4.5, 120.5, 121.5], this method would remove the events at times [1, 2] for the first event type – and not the events at times [120, 121].

Parameters:

event_idsNone | list | dict

The event types to equalize.

If None (default), equalize the counts of all event types present in the Epochs instance.

If a list, each element can either be a string (event name) or a list of strings. In the case where one of the entries is a list of strings, event types in that list will be grouped together before equalizing trial counts across conditions.

If a dictionary, the keys are considered as the event names whose counts to equalize, i.e., passing dict(A=1, B=2) will have the same effect as passing ['A', 'B']. This is useful if you intend to pass an event_id dictionary that was used when creating Epochs.

In the case where partial matching is used (using / in the event names), the event types will be matched according to the provided tags, that is, processing works as if the event_ids matched by the provided tags had been supplied instead. The event_ids must identify non-overlapping subsets of the epochs.

method'truncate' | 'mintime' | 'random'

If 'truncate', events will be truncated from the end of each event list. If 'mintime', timing differences between each event list will be minimized. If 'random', events will be randomly selected from each event list.

New in v1.8.

random_stateNone | int | instance of RandomState

A seed for the NumPy random number generator (RNG). If None (default), the seed will be obtained from the operating system (see RandomState for details), meaning it will most likely produce different output every time this function or method is run. To achieve reproducible results, pass a value here to explicitly initialize the RNG with a defined state. Used only if method='random'.

Returns:

epochsinstance of Epochs

The modified instance. It is modified in-place.

indicesarray of int

Indices from the original events list that were dropped.

Notes

For example (if epochs.event_id was {'Left': 1, 'Right': 2, 'Nonspatial':3}:

epochs.equalize_event_counts([[‘Left’, ‘Right’], ‘Nonspatial’])

would equalize the number of trials in the 'Nonspatial' condition with the total number of trials in the 'Left' and 'Right' conditions combined.

If multiple indices are provided (e.g. 'Left' and 'Right' in the example above), it is not guaranteed that after equalization the conditions will contribute equally. E.g., it is possible to end up with 70 'Nonspatial' epochs, 69 'Left' and 1 'Right'.

Changed in version 0.23: Default to equalizing all events in the passed instance if no event names were specified explicitly.

export(fname, fmt='auto', *, overwrite=False, verbose=None)

Export Epochs to external formats.

Supported formats:

• EEGLAB (.set, uses eeglabio)

Warning

Since we are exporting to external formats, there’s no guarantee that all the info will be preserved in the external format. See Notes for details.

Parameters:

fnamestr

Name of the output file.

fmt‘auto’ | ‘eeglab’

Format of the export. Defaults to 'auto', which will infer the format from the filename extension. See supported formats above for more information.

overwritebool

If True (default False), overwrite the destination file if it exists.

New in v0.24.1.

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.

Notes

New in v0.24.

Export to external format may not preserve all the information from the instance. To save in native MNE format (.fif) without information loss, use mne.Epochs.save() instead. Export does not apply projector(s). Unapplied projector(s) will be lost. Consider applying projector(s) before exporting with mne.Epochs.apply_proj().

For EEGLAB exports, channel locations are expanded to full EEGLAB format. For more details see eeglabio.utils.cart_to_eeglab().

property filename: Path | None

The filename if the epochs are loaded from disk.

Type:

pathlib.Path | None

filter(l_freq, h_freq, picks=None, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=None, method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design='firwin', skip_by_annotation=('edge', 'bad_acq_skip'), pad='edge', *, verbose=None)

Filter a subset of channels/vertices.

Parameters:

l_freqfloat | None

For FIR filters, the lower pass-band edge; for IIR filters, the lower cutoff frequency. If None the data are only low-passed.

h_freqfloat | None

For FIR filters, the upper pass-band edge; for IIR filters, the upper cutoff frequency. If None the data are only high-passed.

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 data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

filter_lengthstr | int

Length of the FIR filter to use (if applicable):

• ‘auto’ (default): The filter length is chosen based on the size of the transition regions (6.6 times the reciprocal of the shortest transition band for fir_window=’hamming’ and fir_design=”firwin2”, and half that for “firwin”).

• str: A human-readable time in units of “s” or “ms” (e.g., “10s” or “5500ms”) will be converted to that number of samples if phase="zero", or the shortest power-of-two length at least that duration for phase="zero-double".

• int: Specified length in samples. For fir_design=”firwin”, this should not be used.

l_trans_bandwidthfloat | str

Width of the transition band at the low cut-off frequency in Hz (high pass or cutoff 1 in bandpass). Can be “auto” (default) to use a multiple of l_freq:

min(max(l_freq * 0.25, 2), l_freq)

Only used for method='fir'.

h_trans_bandwidthfloat | str

Width of the transition band at the high cut-off frequency in Hz (low pass or cutoff 2 in bandpass). Can be “auto” (default in 0.14) to use a multiple of h_freq:

min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq)

Only used for method='fir'.

n_jobsint | str

Number of jobs to run in parallel. Can be 'cuda' if cupy is installed properly and method='fir'.

methodstr

'fir' will use overlap-add FIR filtering, 'iir' will use IIR forward-backward filtering (via filtfilt()).

iir_paramsdict | None

Dictionary of parameters to use for IIR filtering. If iir_params=None and method="iir", 4th order Butterworth will be used. For more information, see mne.filter.construct_iir_filter().

phasestr

Phase of the filter. When method='fir', symmetric linear-phase FIR filters are constructed with the following behaviors when method="fir":

"zero" (default)

The delay of this filter is compensated for, making it non-causal.

"minimum"

A minimum-phase filter will be constructed by decomposing the zero-phase filter into a minimum-phase and all-pass systems, and then retaining only the minimum-phase system (of the same length as the original zero-phase filter) via scipy.signal.minimum_phase().

"zero-double"

This is a legacy option for compatibility with MNE <= 0.13. The filter is applied twice, once forward, and once backward (also making it non-causal).

"minimum-half"

This is a legacy option for compatibility with MNE <= 1.6. A minimum-phase filter will be reconstructed from the zero-phase filter with half the length of the original filter.

When method='iir', phase='zero' (default) or equivalently 'zero-double' constructs and applies IIR filter twice, once forward, and once backward (making it non-causal) using filtfilt(); phase='forward' will apply the filter once in the forward (causal) direction using lfilter().

New in v0.13.

Changed in version 1.7: The behavior for phase="minimum" was fixed to use a filter of the requested length and improved suppression.

fir_windowstr

The window to use in FIR design, can be “hamming” (default), “hann” (default in 0.13), or “blackman”.

New in v0.15.

fir_designstr

Can be “firwin” (default) to use scipy.signal.firwin(), or “firwin2” to use scipy.signal.firwin2(). “firwin” uses a time-domain design technique that generally gives improved attenuation using fewer samples than “firwin2”.

New in v0.15.

skip_by_annotationstr | list of str

If a string (or list of str), any annotation segment that begins with the given string will not be included in filtering, and segments on either side of the given excluded annotated segment will be filtered separately (i.e., as independent signals). The default (('edge', 'bad_acq_skip') will separately filter any segments that were concatenated by mne.concatenate_raws() or mne.io.Raw.append(), or separated during acquisition. To disable, provide an empty list. Only used if inst is raw.

New in v0.16..

padstr

The type of padding to use. Supports all numpy.pad() mode options. Can also be "reflect_limited", which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. Only used for method='fir'.

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 Epochs, Evoked, SourceEstimate, or Raw

The filtered data.

See also

mne.filter.create_filter

mne.Evoked.savgol_filter

mne.io.Raw.notch_filter

mne.io.Raw.resample

mne.filter.create_filter

mne.filter.filter_data

mne.filter.construct_iir_filter

Notes

Applies a zero-phase low-pass, high-pass, band-pass, or band-stop filter to the channels selected by picks. The data are modified inplace.

The object has to have the data loaded e.g. with preload=True or self.load_data().

l_freq and h_freq are the frequencies below which and above which, respectively, to filter out of the data. Thus the uses are:

• l_freq < h_freq: band-pass filter

• l_freq > h_freq: band-stop filter

• l_freq is not None and h_freq is None: high-pass filter

• l_freq is None and h_freq is not None: low-pass filter

self.info['lowpass'] and self.info['highpass'] are only updated with picks=None.

Note

If n_jobs > 1, more memory is required as len(picks) * n_times additional time points need to be temporarily stored in memory.

When working on SourceEstimates the sample rate of the original data is inferred from tstep.

For more information, see the tutorials Background information on filtering and Filtering and resampling data and mne.filter.create_filter().

New in v0.15.

get_annotations_per_epoch()

Get a list of annotations that occur during each epoch.

Returns:

epoch_annotslist

A list of lists (with length equal to number of epochs) where each inner list contains any annotations that overlap the corresponding epoch. Annotations are stored as a tuple of onset, duration, description (not as a Annotations object), where the onset is now relative to time=0 of the epoch, rather than time=0 of the original continuous (raw) data.

get_channel_types(picks=None, unique=False, only_data_chs=False)

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.

get_data(picks=None, item=None, units=None, tmin=None, tmax=None, *, copy=True, verbose=None)

Get all epochs as a 3D array.

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.

itemslice | array_like | str | list | None

The items to get. See mne.Epochs.__getitem__() for a description of valid options. This can be substantially faster for obtaining an ndarray than __getitem__() for repeated access on large Epochs objects. None (default) is an alias for slice(None).

New in v0.20.

unitsstr | dict | None

Specify the unit(s) that the data should be returned in. If None (default), the data is returned in the channel-type-specific default units, which are SI units (see Internal representation (units) and data channels). If a string, must be a sub-multiple of SI units that will be used to scale the data from all channels of the type associated with that unit. This only works if the data contains one channel type that has a unit (unitless channel types are left unchanged). For example if there are only EEG and STIM channels, units='uV' will scale EEG channels to micro-Volts while STIM channels will be unchanged. Finally, if a dictionary is provided, keys must be channel types, and values must be units to scale the data of that channel type to. For example dict(grad='fT/cm', mag='fT') will scale the corresponding types accordingly, but all other channel types will remain in their channel-type-specific default unit.

New in v0.24.

tminint | float | None

Start time of data to get in seconds.

New in v0.24.0.

tmaxint | float | None

End time of data to get in seconds.

New in v0.24.0.

copybool

Whether to return a copy of the object’s data, or (if possible) a view. See the NumPy docs for an explanation. Default is False in 1.6 but will change to True in 1.7, set it explicitly to avoid a warning in some cases. A view is only possible when item is None, picks is None, units is None, and data are preloaded.

Warning

Using copy=False and then modifying the returned data will in turn modify the Epochs object. Use with caution!

Changed in version 1.7: The default changed from False to True.

New in v1.6.

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:

dataarray of shape (n_epochs, n_channels, n_times)

The epochs data. Will be a copy when copy=True and will be a view when possible when copy=False.

get_montage()

Get a DigMontage from instance.

Returns:

montageNone | DigMontage

A copy of the channel positions, if available, otherwise None.

interpolate_bads(reset_bads=True, mode='accurate', origin='auto', method=None, exclude=(), verbose=None)

Interpolate bad MEG and EEG channels.

Operates in place.

Parameters:

reset_badsbool

If True, remove the bads from info.

modestr

Either 'accurate' or 'fast', determines the quality of the Legendre polynomial expansion used for interpolation of channels using the minimum-norm method.

originarray_like, shape (3,) | str

Origin of the sphere in the head coordinate frame and in meters. Can be 'auto' (default), which means a head-digitization-based origin fit.

New in v0.17.

methoddict | str | None

Method to use for each channel type.

• "meg" channels support "MNE" (default) and "nan"

• "eeg" channels support "spline" (default), "MNE" and "nan"

• "fnirs" channels support "nearest" (default) and "nan"

• "ecog" channels support "spline" (default) and "nan"

• "seeg" channels support "spline" (default) and "nan"

None is an alias for:

method=dict(meg="MNE", eeg="spline", fnirs="nearest")

If a str is provided, the method will be applied to all channel types supported and available in the instance. The method "nan" will replace the channel data with np.nan.

Warning

Be careful when using method="nan"; the default value reset_bads=True may not be what you want.

New in v0.21.

excludelist | tuple

The channels to exclude from interpolation. If excluded a bad channel will stay in bads.

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.

Notes

The "MNE" method uses minimum-norm projection to a sphere and back.

New in v0.9.0.

Examples using interpolate_bads:

Single trial linear regression analysis with the LIMO dataset

Single trial linear regression analysis with the LIMO dataset

iter_evoked(copy=False)

Iterate over epochs as a sequence of Evoked objects.

The Evoked objects yielded will each contain a single epoch (i.e., no averaging is performed).

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

Parameters:

copybool

If False copies of data and measurement info will be omitted to save time.

load_data()

Load the data if not already preloaded.

Returns:

epochsinstance of Epochs

The epochs object.

Notes

This function operates in-place.

New in v0.10.0.

property metadata

Get the metadata.

next(return_event_id=False)

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)

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 v0.24.0.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

pick_channels(ch_names, ordered=True, *, verbose=None)

Warning

LEGACY: New code should use inst.pick(…).

Pick some channels.

Parameters:

ch_nameslist

The list of channels to select.

orderedbool

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

New in v0.20.0.

Changed in version 1.7: The default changed from False in 1.6 to True in 1.7.

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 v1.1.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

drop_channels

pick_types

reorder_channels

Notes

If ordered is False, the channel names given via ch_names are assumed to be a set, that is, their order does not matter. In that case, the original order of the channels in the data is preserved. Apart from using ordered=True, you may also use reorder_channels to set channel order, if necessary.

New in v0.9.0.

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)

Warning

LEGACY: New code should use inst.pick(…).

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.

eyetrackbool | 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).

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 v0.9.0.

plot(picks=None, scalings=None, n_epochs=20, n_channels=20, title=None, events=False, event_color=None, order=None, show=True, block=False, decim='auto', noise_cov=None, butterfly=False, show_scrollbars=True, show_scalebars=True, epoch_colors=None, event_id=None, group_by='type', precompute=None, use_opengl=None, *, theme=None, overview_mode=None, splash=True)

Visualize epochs.

Bad epochs can be marked with a left click on top of the epoch. Bad channels can be selected by clicking the channel name on the left side of the main axes. Calling this function drops all the selected bad epochs as well as bad epochs marked beforehand with rejection parameters.

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 good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

scalings‘auto’ | dict | None

Scaling factors for the traces. If a dictionary where any value is 'auto', the scaling factor is set to match the 99.5th percentile of the respective data. If 'auto', all scalings (for all channel types) are set to 'auto'. If any values are 'auto' and the data is not preloaded, a subset up to 100 MB will be loaded. If None, defaults to:

dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4,
     emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1,
     resp=1, chpi=1e-4, whitened=1e2)

Note

A particular scaling value s corresponds to half of the visualized signal range around zero (i.e. from 0 to +s or from 0 to -s). For example, the default scaling of 20e-6 (20µV) for EEG signals means that the visualized range will be 40 µV (20 µV in the positive direction and 20 µV in the negative direction).

n_epochsint

The number of epochs per view. Defaults to 20.

n_channelsint

The number of channels per view. Defaults to 20.

titlestr | None

The title of the window. If None, the event names (from epochs.event_id) will be displayed. Defaults to None.

eventsbool | array, shape (n_events, 3)

Events to show with vertical bars. You can use plot_events as a legend for the colors. By default, the coloring scheme is the same. True plots epochs.events. Defaults to False (do not plot events).

Warning

If the epochs have been resampled, the events no longer align with the data.

New in v0.14.0.

Changed in version 1.6: Passing events=None was disallowed. The new equivalent is events=False.

event_colorcolor object | dict | None

Color(s) to use for events. To show all events in the same color, pass any matplotlib-compatible color. To color events differently, pass a dict that maps event names or integer event numbers to colors (must include entries for all events, or include a “fallback” entry with key -1). If None, colors are chosen from the current Matplotlib color cycle. Defaults to None.

orderarray of str | None

Order in which to plot channel types.

New in v0.18.0.

showbool

Show figure if True. Defaults to True.

blockbool

Whether to halt program execution until the figure is closed. Useful for rejecting bad trials on the fly by clicking on an epoch. Defaults to False.

decimint | ‘auto’

Amount to decimate the data during display for speed purposes. You should only decimate if the data are sufficiently low-passed, otherwise aliasing can occur. The ‘auto’ mode (default) uses the decimation that results in a sampling rate at least three times larger than info['lowpass'] (e.g., a 40 Hz lowpass will result in at least a 120 Hz displayed sample rate).

New in v0.15.0.

noise_covinstance of Covariance | str | None

Noise covariance used to whiten the data while plotting. Whitened data channels are scaled by scalings['whitened'], and their channel names are shown in italic. Can be a string to load a covariance from disk. See also mne.Evoked.plot_white() for additional inspection of noise covariance properties when whitening evoked data. For data processed with SSS, the effective dependence between magnetometers and gradiometers may introduce differences in scaling, consider using mne.Evoked.plot_white().

New in v0.16.0.

butterflybool

Whether to directly call the butterfly view.

New in v0.18.0.

show_scrollbarsbool

Whether to show scrollbars when the plot is initialized. Can be toggled after initialization by pressing z (“zen mode”) while the plot window is focused. Default is True.

New in v0.19.0.

show_scalebarsbool

Whether to show scale bars when the plot is initialized. Can be toggled after initialization by pressing s while the plot window is focused. Default is True.

New in v0.24.0.

epoch_colorslist of (n_epochs) list (of n_channels) | None

Colors to use for individual epochs. If None, use default colors.

event_idbool | dict

Determines to label the event markers on the plot. If True, uses epochs.event_id. If False, uses integer event codes instead of IDs. If a dict is passed, uses its keys as event labels on the plot for entries whose values are integer codes for events being drawn. Ignored if events=False.

New in v0.20.

group_bystr

How to group channels. 'type' groups by channel type, 'original' plots in the order of ch_names, 'selection' uses Elekta’s channel groupings (only works for Neuromag data), 'position' groups the channels by the positions of the sensors. 'selection' and 'position' modes allow custom selections by using a lasso selector on the topomap. In butterfly mode, 'type' and 'original' group the channels by type, whereas 'selection' and 'position' use regional grouping. 'type' and 'original' modes are ignored when order is not None. Defaults to 'type'.

precomputebool | str

Whether to load all data (not just the visible portion) into RAM and apply preprocessing (e.g., projectors) to the full data array in a separate processor thread, instead of window-by-window during scrolling. The default None uses the MNE_BROWSER_PRECOMPUTE variable, which defaults to 'auto'. 'auto' compares available RAM space to the expected size of the precomputed data, and precomputes only if enough RAM is available. This is only used with the Qt backend.

New in v0.24.

Changed in version 1.0: Support for the MNE_BROWSER_PRECOMPUTE config variable.

use_openglbool | None

Whether to use OpenGL when rendering the plot (requires pyopengl). May increase performance, but effect is dependent on system CPU and graphics hardware. Only works if using the Qt backend. Default is None, which will use False unless the user configuration variable MNE_BROWSER_USE_OPENGL is set to 'true', see mne.set_config().

New in v0.24.

themestr | path-like

Can be “auto”, “light”, or “dark” or a path-like to a custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection, qdarkstyle and darkdetect, respectively, are required. If None (default), the config option MNE_BROWSER_THEME will be used, defaulting to “auto” if it’s not found. Only supported by the 'qt' backend.

New in v1.0.

overview_modestr | None

Can be “channels”, “empty”, or “hidden” to set the overview bar mode for the 'qt' backend. If None (default), the config option MNE_BROWSER_OVERVIEW_MODE will be used, defaulting to “channels” if it’s not found.

New in v1.1.

splashbool

If True (default), a splash screen is shown during the application startup. Only applicable to the qt backend.

New in v1.6.

Returns:

figmatplotlib.figure.Figure | mne_qt_browser.figure.MNEQtBrowser

Browser instance.

Notes

The arrow keys (up/down/left/right) can be used to navigate between channels and epochs and the scaling can be adjusted with - and + (or =) keys, but this depends on the backend matplotlib is configured to use (e.g., mpl.use(TkAgg) should work). Full screen mode can be toggled with f11 key. The amount of epochs and channels per view can be adjusted with home/end and page down/page up keys. h key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled with b key. Left mouse click adds a vertical line to the plot. Click ‘help’ button at bottom left corner of the plotter to view all the options.

MNE-Python provides two different backends for browsing plots (i.e., raw.plot(), epochs.plot(), and ica.plot_sources()). One is based on matplotlib, and the other is based on PyQtGraph. You can set the backend temporarily with the context manager mne.viz.use_browser_backend(), you can set it for the duration of a Python session using mne.viz.set_browser_backend(), and you can set the default for your computer via mne.set_config('MNE_BROWSER_BACKEND', 'matplotlib') (or 'qt').

Note

For the PyQtGraph backend to run in IPython with block=False you must run the magic command %gui qt5 first.

Note

To report issues with the PyQtGraph backend, please use the issues of mne-qt-browser.

New in v0.10.0.

Examples using plot:

Creating MNE-Python data structures from scratch

Creating MNE-Python data structures from scratch

plot_drop_log(threshold=0, n_max_plot=20, subject=None, color=(0.9, 0.9, 0.9), width=0.8, ignore=('IGNORED',), show=True)

Show the channel stats based on a drop_log from Epochs.

Parameters:

thresholdfloat

The percentage threshold to use to decide whether or not to plot. Default is zero (always plot).

n_max_plotint

Maximum number of channels to show stats for.

subjectstr | None

The subject name to use in the title of the plot. If None, do not display a subject name.

Changed in version 0.23: Added support for None.

Changed in version 1.0: Defaults to None.

colortuple | str

Color to use for the bars.

widthfloat

Width of the bars.

ignorelist

The drop reasons to ignore.

showbool

Show figure if True.

Returns:

figinstance of matplotlib.figure.Figure

The figure.

plot_image(picks=None, sigma=0.0, vmin=None, vmax=None, colorbar=True, order=None, show=True, units=None, scalings=None, cmap=None, fig=None, axes=None, overlay_times=None, combine=None, group_by=None, evoked=True, ts_args=None, title=None, clear=False)

Plot Event Related Potential / Fields image.

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 good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided. picks interacts with group_by and combine to determine the number of figures generated; see Notes.

sigmafloat

The standard deviation of a Gaussian smoothing window applied along the epochs axis of the image. If 0, no smoothing is applied. Defaults to 0.

vminNone | float | callable()

The min value in the image (and the ER[P/F]). The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. If vmin is None and multiple plots are returned, the limit is equalized within channel types. Hint: to specify the lower limit of the data, use vmin=lambda data: data.min().

vmaxNone | float | callable()

The max value in the image (and the ER[P/F]). The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers. If vmin is None and multiple plots are returned, the limit is equalized within channel types.

colorbarbool

Display or not a colorbar.

orderNone | array of int | callable()

If not None, order is used to reorder the epochs along the y-axis of the image. If it is an array of int, its length should match the number of good epochs. If it is a callable it should accept two positional parameters (times and data, where data.shape == (len(good_epochs), len(times))) and return an array of indices that will sort data along its first axis.

showbool

Show figure if True.

unitsdict | None

The units of the channel types used for axes labels. If None, defaults to units=dict(eeg='µV', grad='fT/cm', mag='fT').

scalingsdict | None

The scalings of the channel types to be applied for plotting. If None, defaults to scalings=dict(eeg=1e6, grad=1e13, mag=1e15, eog=1e6).

cmapNone | colormap | (colormap, bool) | ‘interactive’

Colormap. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the scale. Up and down arrows can be used to change the colormap. If ‘interactive’, translates to (‘RdBu_r’, True). If None, “RdBu_r” is used, unless the data is all positive, in which case “Reds” is used.

figFigure | None

Figure instance to draw the image to. Figure must contain the correct number of axes for drawing the epochs image, the evoked response, and a colorbar (depending on values of evoked and colorbar). If None a new figure is created. Defaults to None.

axeslist of Axes | dict of list of Axes | None

List of Axes objects in which to draw the image, evoked response, and colorbar (in that order). Length of list must be 1, 2, or 3 (depending on values of colorbar and evoked parameters). If a dict, each entry must be a list of Axes objects with the same constraints as above. If both axes and group_by are dicts, their keys must match. Providing non-None values for both fig and axes results in an error. Defaults to None.

overlay_timesarray_like, shape (n_epochs,) | None

Times (in seconds) at which to draw a line on the corresponding row of the image (e.g., a reaction time associated with each epoch). Note that overlay_times should be ordered to correspond with the Epochs object (i.e., overlay_times[0] corresponds to epochs[0], etc).

combine‘mean’ | ‘median’ | ‘std’ | ‘gfp’ | callable() | None

How to aggregate across channels. If None, channels are combined by computing GFP/RMS, unless group_by is also None and picks is a list of specific channels (not channel types), in which case no combining is performed and each channel gets its own figure. If a string, "mean" uses numpy.mean(), "median" computes the marginal median, "std" uses numpy.std(), and "gfp" computes global field power for EEG channels and RMS amplitude for MEG channels. If callable(), it must operate on an array of shape (n_epochs, n_channels, n_times) and return an array of shape (n_epochs, n_times). For example:

combine = lambda data: np.median(data, axis=1)

See Notes for further details. Defaults to None.

group_byNone | dict

Specifies which channels are aggregated into a single figure, with aggregation method determined by the combine parameter. If not None, one Figure is made per dict entry; the dict key will be used as the figure title and the dict values must be lists of picks (either channel names or integer indices of epochs.ch_names). For example:

group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8])

Note that within a dict entry all channels must have the same type. group_by interacts with picks and combine to determine the number of figures generated; see Notes. Defaults to None.

evokedbool

Draw the ER[P/F] below the image or not.

ts_argsNone | dict

Arguments passed to a call to plot_compare_evokeds to style the evoked plot below the image. Defaults to an empty dictionary, meaning plot_compare_evokeds will be called with default parameters.

titleNone | str

If str, will be plotted as figure title. Otherwise, the title will indicate channel(s) or channel type being plotted. Defaults to None.

clearbool

Whether to clear the axes before plotting (if fig or axes are provided). Defaults to False.

Returns:

figslist of Figure

One figure per channel, channel type, or group, depending on values of picks, group_by, and combine. See Notes.

Notes

You can control how channels are aggregated into one figure or plotted in separate figures through a combination of the picks, group_by, and combine parameters. If group_by is a dict, the result is one Figure per dictionary key (for any valid values of picks and combine). If group_by is None, the number and content of the figures generated depends on the values of picks and combine, as summarized in this table:

group_by	picks	combine	result
dict	None, int, list of int, ch_name, list of ch_names, ch_type, list of ch_types	None, string, or callable	1 figure per dict key
None	None, ch_type, list of ch_types	None, string, or callable	1 figure per ch_type
	int, ch_name, list of int, list of ch_names	None	1 figure per pick
		string or callable	1 figure

plot_projs_topomap(ch_type=None, *, sensors=True, show_names=False, contours=6, outlines='head', sphere=None, image_interp='cubic', extrapolate='auto', border='mean', res=64, size=1, cmap=None, vlim=(None, None), cnorm=None, colorbar=False, cbar_fmt='%3.1f', units=None, axes=None, show=True)

Plot SSP vector.

Parameters:

ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None | list

The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None it will return all channel types present.. If a list of ch_types is provided, it will return multiple figures. Defaults to None.

sensorsbool | str

Whether to add markers for sensor locations. If str, should be a valid matplotlib format string (e.g., 'r+' for red plusses, see the Notes section of plot()). If True (the default), black circles will be used.

show_namesbool | callable()

If True, show channel names next to each sensor marker. If callable, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the function lambda x: x.replace('MEG ', ''). If mask is not None, only non-masked sensor names will be shown.

New in v1.2.

contoursint | array_like

The number of contour lines to draw. If 0, no contours will be drawn. If a positive integer, that number of contour levels are chosen using the matplotlib tick locator (may sometimes be inaccurate, use array for accuracy). If array-like, the array values are used as the contour levels. The values should be in µV for EEG, fT for magnetometers and fT/m for gradiometers. If colorbar=True, the colorbar will have ticks corresponding to the contour levels. Default is 6.

outlines‘head’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.

spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’

The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

image_interpstr

The image interpolation to be used. Options are 'cubic' (default) to use scipy.interpolate.CloughTocher2DInterpolator, 'nearest' to use scipy.spatial.Voronoi or 'linear' to use scipy.interpolate.LinearNDInterpolator.

extrapolatestr

Options:

• 'box'

  Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.

• 'local' (default for MEG sensors)

  Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.

• 'head' (default for non-MEG sensors)

  Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.

New in v0.20.

Changed in version 0.21:

• The default was changed to 'local' for MEG sensors.

• 'local' was changed to use a convex hull mask

• 'head' was changed to extrapolate out to the clipping circle.

borderfloat | ‘mean’

Value to extrapolate to on the topomap borders. If 'mean' (default), then each extrapolated point has the average value of its neighbours.

New in v0.20.

resint

The resolution of the topomap image (number of pixels along each side).

sizefloat

Side length of each subplot in inches. Only applies when plotting multiple topomaps at a time.

cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ | None

Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, 'Reds' is used for data that is either all-positive or all-negative, and 'RdBu_r' is used otherwise. 'interactive' is equivalent to (None, True). Defaults to None.

Warning

Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.

vlimtuple of length 2 | “joint”

Lower and upper bounds of the colormap, typically a numeric value in the same units as the data. Elements of the tuple may also be callable functions which take in a NumPy array and return a scalar.

If both entries are None, the bounds are set at ± the maximum absolute value of the data (yielding a colormap with midpoint at 0), or (0, max(abs(data))) if the (possibly baselined) data are all-positive. Providing None for just one entry will set the corresponding boundary at the min/max of the data. If vlim="joint", will compute the colormap limits jointly across all projectors of the same channel type (instead of separately for each projector), using the min/max of the data for that channel type. If vlim is "joint", info must not be None. Defaults to (None, None).

cnormmatplotlib.colors.Normalize | None

How to normalize the colormap. If None, standard linear normalization is performed. If not None, vmin and vmax will be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.

New in v1.2.

colorbarbool

Plot a colorbar in the rightmost column of the figure.

cbar_fmtstr

Formatting string for colorbar tick labels. See Format Specification Mini-Language for details.

New in v1.2.

unitsstr | None

The units to use for the colorbar label. Ignored if colorbar=False. If None the label will be “AU” indicating arbitrary units. Default is None.

New in v1.2.

axesinstance of Axes | list of Axes | None

The axes to plot into. If None, a new Figure will be created with the correct number of axes. If Axes are provided (either as a single instance or a list of axes), the number of axes provided must match the number of projectors. Default is None.

showbool

Show the figure if True.

Returns:

figinstance of Figure

Figure distributing one image per channel across sensor topography.

plot_psd(fmin=0, fmax=inf, tmin=None, tmax=None, picks=None, proj=False, *, method='auto', average=False, dB=True, estimate='power', xscale='linear', area_mode='std', area_alpha=0.33, color='black', line_alpha=None, spatial_colors=True, sphere=None, exclude='bads', ax=None, show=True, n_jobs=1, verbose=None, **method_kw)

Plot power or amplitude spectra.

Separate plots are drawn for each channel type. When the data have been processed with a bandpass, lowpass or highpass filter, dashed lines (╎) indicate the boundaries of the filter. The line noise frequency is also indicated with a dashed line (⋮). If average=False, the plot will be interactive, and click-dragging on the spectrum will generate a scalp topography plot for the chosen frequency range in a new figure.

Parameters:

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is fmin=0, fmax=np.inf (spans all frequencies present in the data).

tmin, tmaxfloat | None

First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).

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 good data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

projbool

Whether to apply SSP projection vectors before spectral estimation. Default is False.

method'welch' | 'multitaper' | 'auto'

Spectral estimation method. 'welch' uses Welch’s method [1], 'multitaper' uses DPSS tapers [2]. 'auto' (default) uses Welch’s method for continuous data and multitaper for Epochs or Evoked data.

averagebool

If False, the PSDs of all channels is displayed. No averaging is done and parameters area_mode and area_alpha are ignored. When False, it is possible to paint an area (hold left mouse button and drag) to plot a topomap.

dBbool

Plot Power Spectral Density (PSD), in units (amplitude**2/Hz (dB)) if dB=True, and estimate='power' or estimate='auto'. Plot PSD in units (amplitude**2/Hz) if dB=False and, estimate='power'. Plot Amplitude Spectral Density (ASD), in units (amplitude/sqrt(Hz)), if dB=False and estimate='amplitude' or estimate='auto'. Plot ASD, in units (amplitude/sqrt(Hz) (dB)), if dB=True and estimate='amplitude'.

estimatestr, {‘power’, ‘amplitude’}

Can be “power” for power spectral density (PSD; default), “amplitude” for amplitude spectrum density (ASD).

xscale‘linear’ | ‘log’

Scale of the frequency axis. Default is 'linear'.

area_modestr | None

Mode for plotting area. If ‘std’, the mean +/- 1 STD (across channels) will be plotted. If ‘range’, the min and max (across channels) will be plotted. Bad channels will be excluded from these calculations. If None, no area will be plotted. If average=False, no area is plotted.

area_alphafloat

Alpha for the area.

colorstr | tuple

A matplotlib-compatible color to use. Has no effect when spatial_colors=True.

line_alphafloat | None

Alpha for the PSD line. Can be None (default) to use 1.0 when average=True and 0.1 when average=False.

spatial_colorsbool

Whether to color spectrum lines by channel location. Ignored if average=True.

spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’

The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

New in v0.22.0.

excludelist of str | ‘bads’

Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded. Pass an empty list to plot all channels (including channels marked “bad”, if any).

New in v0.24.0.

axinstance of Axes | list of Axes | None

The axes to plot into. If None, a new Figure will be created with the correct number of axes. If Axes are provided (either as a single instance or a list of axes), the number of axes provided must match the number of channel types present in the object.. Default is None.

showbool

Show the figure if True.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

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.

**method_kw

Additional keyword arguments passed to the spectral estimation function (e.g., n_fft, n_overlap, n_per_seg, average, window for Welch method, or bandwidth, adaptive, low_bias, normalization for multitaper method). See psd_array_welch() and psd_array_multitaper() for details. Note that for Welch method if n_fft is unspecified its default will be the smaller of 2048 or the number of available time samples (taking into account tmin and tmax), not 256 as in psd_array_welch().

Returns:

figinstance of Figure

Figure with frequency spectra of the data channels.

Notes

This method exists to support legacy code; for new code the preferred idiom is inst.compute_psd().plot() (where inst is an instance of Raw, Epochs, or Evoked).

plot_psd_topo(tmin=None, tmax=None, fmin=0, fmax=100, proj=False, *, method='auto', dB=True, layout=None, color='w', fig_facecolor='k', axis_facecolor='k', axes=None, block=False, show=True, n_jobs=None, verbose=None, **method_kw)

Warning

LEGACY: New code should use .compute_psd().plot_topo().

Plot power spectral density, separately for each channel.

Parameters:

tmin, tmaxfloat | None

First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is fmin=0, fmax=100.

projbool

Whether to apply SSP projection vectors before spectral estimation. Default is False.

method'welch' | 'multitaper' | 'auto'

Spectral estimation method. 'welch' uses Welch’s method [1], 'multitaper' uses DPSS tapers [2]. 'auto' (default) uses Welch’s method for continuous data and multitaper for Epochs or Evoked data.

dBbool

Whether to plot on a decibel-like scale. If True, plots 10 × log₁₀(spectral power). Ignored if normalize=True.

layoutinstance of Layout | None

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If None (default), the layout is inferred from the data (if possible).

colorstr | tuple

A matplotlib-compatible color to use for the curves. Defaults to white.

fig_facecolorstr | tuple

A matplotlib-compatible color to use for the figure background. Defaults to black.

axis_facecolorstr | tuple

A matplotlib-compatible color to use for the axis background. Defaults to black.

axesinstance of Axes | list of Axes | None

The axes to plot into. If None, a new Figure will be created with the correct number of axes. If Axes are provided (either as a single instance or a list of axes), the number of axes provided must be length 1 (for efficiency, subplots for each channel are simulated within a single Axes object). Default is None.

blockbool

Whether to halt program execution until the figure is closed. May not work on all systems / platforms. Defaults to False.

showbool

Show the figure if True.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

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.

**method_kw

Additional keyword arguments passed to the spectral estimation function (e.g., n_fft, n_overlap, n_per_seg, average, window for Welch method, or bandwidth, adaptive, low_bias, normalization for multitaper method). See psd_array_welch() and psd_array_multitaper() for details. Note that for Welch method if n_fft is unspecified its default will be the smaller of 2048 or the number of available time samples (taking into account tmin and tmax), not 256 as in psd_array_welch(). Defaults to dict(n_fft=2048).

Returns:

figinstance of matplotlib.figure.Figure

Figure distributing one image per channel across sensor topography.

plot_psd_topomap(bands=None, tmin=None, tmax=None, ch_type=None, *, proj=False, method='auto', normalize=False, agg_fun=None, dB=False, sensors=True, show_names=False, mask=None, mask_params=None, contours=0, outlines='head', sphere=None, image_interp='cubic', extrapolate='auto', border='mean', res=64, size=1, cmap=None, vlim=(None, None), cnorm=None, colorbar=True, cbar_fmt='auto', units=None, axes=None, show=True, n_jobs=None, verbose=None, **method_kw)

Warning

LEGACY: New code should use .compute_psd().plot_topomap().

Plot scalp topography of PSD for chosen frequency bands.

Parameters:

bandsNone | dict | list of tuple

The frequencies or frequency ranges to plot. If a dict, keys will be used as subplot titles and values should be either a single frequency (e.g., {'presentation rate': 6.5}) or a length-two sequence of lower and upper frequency band edges (e.g., {'theta': (4, 8)}). If a single frequency is provided, the plot will show the frequency bin that is closest to the requested value. If None (the default), expands to:

bands = {'Delta (0-4 Hz)': (0, 4), 'Theta (4-8 Hz)': (4, 8),
         'Alpha (8-12 Hz)': (8, 12), 'Beta (12-30 Hz)': (12, 30),
         'Gamma (30-45 Hz)': (30, 45)}

Note

For backwards compatibility, tuples of length 2 or 3 are also accepted, where the last element of the tuple is the subplot title and the other entries are frequency values (a single value or band edges). New code should use dict or None.

Changed in version 1.2: Allow passing a dict and discourage passing tuples.

tmin, tmaxfloat | None

First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).

ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None

The channel type to plot. For 'grad', the gradiometers are collected in pairs and the mean for each pair is plotted. If None the first available channel type from order shown above is used. Defaults to None.

projbool

Whether to apply SSP projection vectors before spectral estimation. Default is False.

method'welch' | 'multitaper' | 'auto'

Spectral estimation method. 'welch' uses Welch’s method [1], 'multitaper' uses DPSS tapers [2]. 'auto' (default) uses Welch’s method for continuous data and multitaper for Epochs or Evoked data.

normalizebool

If True, each band will be divided by the total power. Defaults to False.

agg_funcallable()

The function used to aggregate over frequencies. Defaults to numpy.sum() if normalize=True, else numpy.mean().

dBbool

Whether to plot on a decibel-like scale. If True, plots 10 × log₁₀(spectral power) following the application of agg_fun. Ignored if normalize=True.

sensorsbool | str

Whether to add markers for sensor locations. If str, should be a valid matplotlib format string (e.g., 'r+' for red plusses, see the Notes section of plot()). If True (the default), black circles will be used.

show_namesbool | callable()

If True, show channel names next to each sensor marker. If callable, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the function lambda x: x.replace('MEG ', ''). If mask is not None, only non-masked sensor names will be shown.

maskndarray of bool, shape (n_channels, n_times) | None

Array indicating channel-time combinations to highlight with a distinct plotting style (useful for, e.g. marking which channels at which times a statistical test of the data reaches significance). Array elements set to True will be plotted with the parameters given in mask_params. Defaults to None, equivalent to an array of all False elements.

mask_paramsdict | None

Additional plotting parameters for plotting significant sensors. Default (None) equals:

dict(marker='o', markerfacecolor='w', markeredgecolor='k',
        linewidth=0, markersize=4)

contoursint | array_like

The number of contour lines to draw. If 0, no contours will be drawn. If a positive integer, that number of contour levels are chosen using the matplotlib tick locator (may sometimes be inaccurate, use array for accuracy). If array-like, the array values are used as the contour levels. The values should be in µV for EEG, fT for magnetometers and fT/m for gradiometers. If colorbar=True, the colorbar will have ticks corresponding to the contour levels. Default is 6.

outlines‘head’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.

spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’

The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

image_interpstr

The image interpolation to be used. Options are 'cubic' (default) to use scipy.interpolate.CloughTocher2DInterpolator, 'nearest' to use scipy.spatial.Voronoi or 'linear' to use scipy.interpolate.LinearNDInterpolator.

extrapolatestr

Options:

• 'box'

  Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.

• 'local' (default for MEG sensors)

  Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.

• 'head' (default for non-MEG sensors)

  Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.

borderfloat | ‘mean’

Value to extrapolate to on the topomap borders. If 'mean' (default), then each extrapolated point has the average value of its neighbours.

resint

The resolution of the topomap image (number of pixels along each side).

sizefloat

Side length of each subplot in inches.

cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ | None

Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, 'Reds' is used for data that is either all-positive or all-negative, and 'RdBu_r' is used otherwise. 'interactive' is equivalent to (None, True). Defaults to None.

Warning

Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.

vlimtuple of length 2 | “joint”

Lower and upper bounds of the colormap, typically a numeric value in the same units as the data. Elements of the tuple may also be callable functions which take in a NumPy array and return a scalar.

If both entries are None, the bounds are set at ± the maximum absolute value of the data (yielding a colormap with midpoint at 0), or (0, max(abs(data))) if the (possibly baselined) data are all-positive. Providing None for just one entry will set the corresponding boundary at the min/max of the data. If vlim="joint", will compute the colormap limits jointly across all topomaps of the same channel type (instead of separately for each topomap), using the min/max of the data for that channel type. Defaults to (None, None).

cnormmatplotlib.colors.Normalize | None

How to normalize the colormap. If None, standard linear normalization is performed. If not None, vmin and vmax will be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.

New in v1.2.

colorbarbool

Plot a colorbar in the rightmost column of the figure.

cbar_fmtstr

Formatting string for colorbar tick labels. See Format Specification Mini-Language for details. If 'auto', is equivalent to ‘%0.3f’ if dB=False and ‘%0.1f’ if dB=True. Defaults to 'auto'.

unitsstr | None

The units to use for the colorbar label. Ignored if colorbar=False. If None the label will be “AU” indicating arbitrary units. Default is None.

axesinstance of Axes | list of Axes | None

The axes to plot into. If None, a new Figure will be created with the correct number of axes. If Axes are provided (either as a single instance or a list of axes), the number of axes provided must match the length of bands. Default is None.

showbool

Show the figure if True.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

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.

**method_kw

Additional keyword arguments passed to the spectral estimation function (e.g., n_fft, n_overlap, n_per_seg, average, window for Welch method, or bandwidth, adaptive, low_bias, normalization for multitaper method). See psd_array_welch() and psd_array_multitaper() for details. Note that for Welch method if n_fft is unspecified its default will be the smaller of 2048 or the number of available time samples (taking into account tmin and tmax), not 256 as in psd_array_welch().

Returns:

figinstance of Figure

Figure showing one scalp topography per frequency band.

plot_sensors(kind='topomap', ch_type=None, title=None, show_names=False, ch_groups=None, to_sphere=True, axes=None, block=False, show=True, sphere=None, *, verbose=None)

Plot sensor positions.

Parameters:

kindstr

Whether to plot the sensors as 3d, topomap or as an interactive sensor selection dialog. Available options ‘topomap’, ‘3d’, ‘select’. If ‘select’, a set of channels can be selected interactively by using lasso selector or clicking while holding control key. The selected channels are returned along with the figure instance. Defaults to ‘topomap’.

ch_typeNone | str

The channel type to plot. Available options 'mag', 'grad', 'eeg', 'seeg', 'dbs', 'ecog', 'all'. If 'all', all the available mag, grad, eeg, seeg, dbs, and ecog channels are plotted. If None (default), then channels are chosen in the order given above.

titlestr | None

Title for the figure. If None (default), equals to 'Sensor positions (%s)' % ch_type.

show_namesbool | array of str

Whether to display all channel names. If an array, only the channel names in the array are shown. Defaults to False.

ch_groups‘position’ | array of shape (n_ch_groups, n_picks) | None

Channel groups for coloring the sensors. If None (default), default coloring scheme is used. If ‘position’, the sensors are divided into 8 regions. See order kwarg of mne.viz.plot_raw(). If array, the channels are divided by picks given in the array.

New in v0.13.0.

to_spherebool

Whether to project the 3d locations to a sphere. When False, the sensor array appears similar as to looking downwards straight above the subject’s head. Has no effect when kind=’3d’. Defaults to True.

New in v0.14.0.

axesinstance of Axes | instance of Axes3D | None

Axes to draw the sensors to. If kind='3d', axes must be an instance of Axes3D. If None (default), a new axes will be created.

New in v0.13.0.

blockbool

Whether to halt program execution until the figure is closed. Defaults to False.

New in v0.13.0.

showbool

Show figure if True. Defaults to True.

spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’

The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

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:

figinstance of Figure

Figure containing the sensor topography.

selectionlist

A list of selected channels. Only returned if kind=='select'.

See also

mne.viz.plot_layout

Notes

This function plots the sensor locations from the info structure using matplotlib. For drawing the sensors using PyVista see mne.viz.plot_alignment().

New in v0.12.0.

plot_topo_image(layout=None, sigma=0.0, vmin=None, vmax=None, colorbar=None, order=None, cmap='RdBu_r', layout_scale=0.95, title=None, scalings=None, border='none', fig_facecolor='k', fig_background=None, font_color='w', show=True)

Plot Event Related Potential / Fields image on topographies.

Parameters:

layoutinstance of Layout

System specific sensor positions.

sigmafloat

The standard deviation of the Gaussian smoothing to apply along the epoch axis to apply in the image. If 0., no smoothing is applied.

vminfloat

The min value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers.

vmaxfloat

The max value in the image. The unit is µV for EEG channels, fT for magnetometers and fT/cm for gradiometers.

colorbarbool | None

Whether to display a colorbar or not. If None a colorbar will be shown only if all channels are of the same type. Defaults to None.

orderNone | array of int | callable()

If not None, order is used to reorder the epochs on the y-axis of the image. If it’s an array of int it should be of length the number of good epochs. If it’s a callable the arguments passed are the times vector and the data as 2d array (data.shape[1] == len(times)).

cmapcolormap

Colors to be mapped to the values.

layout_scalefloat

Scaling factor for adjusting the relative size of the layout on the canvas.

titlestr

Title of the figure.

scalingsdict | None

The scalings of the channel types to be applied for plotting. If None, defaults to dict(eeg=1e6, grad=1e13, mag=1e15).

borderstr

Matplotlib borders style to be used for each sensor plot.

fig_facecolorcolor

The figure face color. Defaults to black.

fig_backgroundNone | array

A background image for the figure. This must be a valid input to matplotlib.pyplot.imshow(). Defaults to None.

font_colorcolor

The color of tick labels in the colorbar. Defaults to white.

showbool

Whether to show the figure. Defaults to True.

Returns:

figinstance of matplotlib.figure.Figure

Figure distributing one image per channel across sensor topography.

Notes

In an interactive Python session, this plot will be interactive; clicking on a channel image will pop open a larger view of the image; this image will always have a colorbar even when the topo plot does not (because it shows multiple sensor types).

property proj

Whether or not projections are active.

rename_channels(mapping, allow_duplicates=False, *, verbose=None)

Rename channels.

Parameters:

mappingdict | callable()

A dictionary mapping the old channel to a new channel name e.g. {'EEG061' : 'EEG161'}. Can also be a callable function that takes and returns a string.

Changed in version 0.10.0: Support for a callable function.

allow_duplicatesbool

If True (default False), allow duplicates, which will automatically be renamed with -N at the end.

New in v0.22.0.

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.

Returns:

instinstance of Raw | Epochs | Evoked

The instance (modified in place).

Changed in version 0.20: Return the instance.

Notes

New in v0.9.0.

reorder_channels(ch_names)

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 v0.16.0.

resample(sfreq, *, npad='auto', window='auto', n_jobs=None, pad='edge', method='fft', verbose=None)

Resample data.

If appropriate, an anti-aliasing filter is applied before resampling. See Resampling and decimating data for more information.

Note

Data must be loaded.

Parameters:

sfreqfloat

New sample rate to use.

npadint | str

Amount to pad the start and end of the data. Can also be "auto" to use a padding that will result in a power-of-two size (can be much faster).

windowstr | tuple

When method="fft", this is the frequency-domain window to use in resampling, and should be the same length as the signal; see scipy.signal.resample() for details. When method="polyphase", this is the time-domain linear-phase window to use after upsampling the signal; see scipy.signal.resample_poly() for details. The default "auto" will use "boxcar" for method="fft" and ("kaiser", 5.0) for method="polyphase".

n_jobsint | str

Number of jobs to run in parallel. Can be 'cuda' if cupy is installed properly.

padstr

The type of padding to use. When method="fft", supports all numpy.pad() mode options. Can also be "reflect_limited", which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. When method="polyphase", supports all modes of scipy.signal.upfirdn().

New in v0.15.

methodstr

Resampling method to use. Can be "fft" (default) or "polyphase" to use FFT-based on polyphase FIR resampling, respectively. These wrap to scipy.signal.resample() and scipy.signal.resample_poly(), respectively.

New in v1.7.

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 Epochs or Evoked

The resampled object.

See also

mne.io.Raw.resample

Notes

For some data, it may be more accurate to use npad=0 to reduce artifacts. This is dataset dependent – check your data!

reset_drop_log_selection()

Reset the drop_log and selection entries.

This method will simplify self.drop_log and self.selection so that they are meaningless (tuple of empty tuples and increasing integers, respectively). This can be useful when concatenating many Epochs instances, as drop_log can accumulate many entries which can become problematic when saving.

save(fname, split_size='2GB', fmt='single', overwrite=False, split_naming='neuromag', verbose=None)

Save epochs in a fif file.

Parameters:

fnamepath-like

The name of the file, which should end with -epo.fif or -epo.fif.gz.

split_sizestr | int

Large raw files are automatically split into multiple pieces. This parameter specifies the maximum size of each piece. If the parameter is an integer, it specifies the size in Bytes. It is also possible to pass a human-readable string, e.g., 100MB. Note: Due to FIFF file limitations, the maximum split size is 2GB.

New in v0.10.0.

fmtstr

Format to save data. Valid options are ‘double’ or ‘single’ for 64- or 32-bit float, or for 128- or 64-bit complex numbers respectively. Note: Data are processed with double precision. Choosing single-precision, the saved data will slightly differ due to the reduction in precision.

New in v0.17.

overwritebool

If True (default False), overwrite the destination file if it exists. To overwrite original file (the same one that was loaded), data must be preloaded upon reading. This defaults to True in 0.18 but will change to False in 0.19.

New in v0.18.

split_naming‘neuromag’ | ‘bids’

When splitting files, append a filename partition with the appropriate naming schema. For 'neuromag', a split file fname.fif will be named fname.fif, fname-1.fif, fname-2.fif, and so on. For 'bids', a filename is expected to consist of parts separated by underscores, like <part-1>_<part-N>_<suffix>.fif, and the according split naming will return filenames like <part-1>_<part-N>_split-01_<suffix>.fif, <part-1>_<part-N>_split-02_<suffix>.fif, and so on.

New in v0.24.

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:

fnamesList of path-like

List of path-like objects containing the path to each file split. .. versionadded:: 1.9

Notes

Bad epochs will be dropped before saving the epochs to disk.

savgol_filter(h_freq, verbose=None)

Filter the data using Savitzky-Golay polynomial method.

Parameters:

h_freqfloat

Approximate high cut-off frequency in Hz. Note that this is not an exact cutoff, since Savitzky-Golay filtering [9] is done using polynomial fits instead of FIR/IIR filtering. This parameter is thus used to determine the length of the window over which a 5th-order polynomial smoothing is used.

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 Epochs, Evoked or SourceEstimate

The object with the filtering applied.

See also

mne.io.Raw.filter

Notes

For Savitzky-Golay low-pass approximation, see:

https://gist.github.com/larsoner/bbac101d50176611136b

When working on SourceEstimates the sample rate of the original data is inferred from tstep.

New in v0.9.0.

References

[9]

Abraham Savitzky and Marcel J. E. Golay. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8):1627–1639, 1964. doi:10.1021/ac60214a047.

Examples

>>> import mne
>>> from os import path as op
>>> evoked_fname = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample', 'sample_audvis-ave.fif')  
>>> evoked = mne.read_evokeds(evoked_fname, baseline=(None, 0))[0]  
>>> evoked.savgol_filter(10.)  # low-pass at around 10 Hz 
>>> evoked.plot()  

set_annotations(annotations, on_missing='raise', *, verbose=None)

Setter for Epoch annotations from Raw.

This method does not handle offsetting the times based on first_samp or measurement dates, since that is expected to occur in Raw.set_annotations().

Parameters:

annotationsinstance of mne.Annotations | None

Annotations to set.

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 v0.23.0.

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.

Returns:

selfinstance of Epochs

The epochs object with annotations.

Notes

Annotation onsets and offsets are stored as time in seconds (not as sample numbers).

If you have an -epo.fif file saved to disk created before 1.0, annotations can be added correctly only if no decimation or resampling was performed. We thus suggest to regenerate your mne.Epochs from raw and re-save to disk with 1.0+ if you want to safely work with Annotations in epochs.

Since this method does not handle offsetting the times based on first_samp or measurement dates, the recommended way to add Annotations is:

raw.set_annotations(annotations)
annotations = raw.annotations
epochs.set_annotations(annotations)

New in v1.0.

set_channel_types(mapping, *, on_unit_change='warn', verbose=None)

Specify the sensor types of channels.

Parameters:

mappingdict

A dictionary mapping channel names to sensor types, e.g., {'EEG061': 'eog'}.

on_unit_change'raise' | 'warn' | 'ignore'

What to do if the measurement unit of a channel is changed automatically to match the new sensor type.

New in v1.4.

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 | Evoked

The instance (modified in place).

Changed in version 0.20: Return the instance.

Notes

The following sensor types are accepted:

bio, chpi, csd, dbs, dipole, ecg, ecog, eeg, emg, eog, exci, eyegaze, fnirs_cw_amplitude, fnirs_fd_ac_amplitude, fnirs_fd_phase, fnirs_od, gof, gsr, hbo, hbr, ias, misc, pupil, ref_meg, resp, seeg, stim, syst, temperature.

When working with eye-tracking data, see mne.preprocessing.eyetracking.set_channel_types_eyetrack().

New in v0.9.0.

set_eeg_reference(ref_channels='average', projection=False, ch_type='auto', forward=None, *, joint=False, verbose=None)

Specify which reference to use for EEG data.

Use this function to explicitly specify the desired reference for EEG. This can be either an existing electrode or a new virtual channel. This function will re-reference the data according to the desired reference.

Parameters:

ref_channelslist of str | str | dict

Can be:

• The name(s) of the channel(s) used to construct the reference for every channel of ch_type.

• 'average' to apply an average reference (default)

• 'REST' to use the Reference Electrode Standardization Technique infinity reference [10].

• A dictionary mapping names of data channels to (lists of) names of reference channels. For example, {‘A1’: ‘A3’} would replace the data in channel ‘A1’ with the difference between ‘A1’ and ‘A3’. To take the average of multiple channels as reference, supply a list of channel names as the dictionary value, e.g. {‘A1’: [‘A2’, ‘A3’]} would replace channel A1 with A1 - mean(A2, A3).

• An empty list, in which case MNE will not attempt any re-referencing of the data

projectionbool

If ref_channels='average' this argument specifies if the average reference should be computed as a projection (True) or not (False; default). If projection=True, the average reference is added as a projection and is not applied to the data (it can be applied afterwards with the apply_proj method). If projection=False, the average reference is directly applied to the data. If ref_channels is not 'average', projection must be set to False (the default in this case).

ch_typelist of str | str

The name of the channel type to apply the reference to. Valid channel types are 'auto', 'eeg', 'ecog', 'seeg', 'dbs'. If 'auto', the first channel type of eeg, ecog, seeg or dbs that is found (in that order) will be selected.

New in v0.19.

Changed in version 1.2: list-of-str is now supported with projection=True.

forwardinstance of Forward | None

Forward solution to use. Only used with ref_channels='REST'.

New in v0.21.

jointbool

How to handle list-of-str ch_type. If False (default), one projector is created per channel type. If True, one projector is created across all channel types. This is only used when projection=True.

New in v1.2.

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 | Evoked

Data with EEG channels re-referenced. If ref_channels='average' and projection=True a projection will be added instead of directly re-referencing the data.

See also

mne.set_bipolar_reference

Convenience function for creating bipolar references.

Notes

Some common referencing schemes and the corresponding value for the ref_channels parameter:

• Average reference:

  A new virtual reference electrode is created by averaging the current EEG signal by setting ref_channels='average'. Bad EEG channels are automatically excluded if they are properly set in info['bads'].

• A single electrode:

  Set ref_channels to a list containing the name of the channel that will act as the new reference, for example ref_channels=['Cz'].

• The mean of multiple electrodes:

  A new virtual reference electrode is created by computing the average of the current EEG signal recorded from two or more selected channels. Set ref_channels to a list of channel names, indicating which channels to use. For example, to apply an average mastoid reference, when using the 10-20 naming scheme, set ref_channels=['M1', 'M2'].

• REST

  The given EEG electrodes are referenced to a point at infinity using the lead fields in forward, which helps standardize the signals.

• Different references for different channels

  Set ref_channels to a dictionary mapping source channel names (str) to the reference channel names (str or list of str). Unlike the other approaches where the same reference is applied globally, you can set different references for different channels with this method. For example, to re-reference channel ‘A1’ to ‘A2’ and ‘B1’ to the average of ‘B2’ and ‘B3’, set ref_channels={'A1': 'A2', 'B1': ['B2', 'B3']}. Warnings are issued when a mapping involves bad channels or channels of different types.

1. If a reference is requested that is not the average reference, this function removes any pre-existing average reference projections.

2. During source localization, the EEG signal should have an average reference.

3. In order to apply a reference, the data must be preloaded. This is not necessary if ref_channels='average' and projection=True.

4. For an average or REST reference, bad EEG channels are automatically excluded if they are properly set in info['bads'].

New in v0.9.0.

References

[10]

D. Yao. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiological Measurement, 22(4):693–711, 2001. doi:10.1088/0967-3334/22/4/305.

set_meas_date(meas_date)

Set the measurement start date.

Parameters:

meas_datedatetime | float | tuple | None

The new measurement date. If datetime object, it must be timezone-aware and in UTC. A tuple of (seconds, microseconds) or float (alias for (meas_date, 0)) can also be passed and a datetime object will be automatically created. If None, will remove the time reference.

Returns:

instinstance of Raw | Epochs | Evoked

The modified raw instance. Operates in place.

See also

mne.io.Raw.anonymize

Notes

If you want to remove all time references in the file, call mne.io.anonymize_info(inst.info) after calling inst.set_meas_date(None).

New in v0.20.

set_montage(montage, match_case=True, match_alias=False, on_missing='raise', verbose=None)

Set EEG/sEEG/ECoG/DBS/fNIRS channel positions and digitization points.

Parameters:

montageNone | str | DigMontage

A montage containing channel positions. If a string or DigMontage is specified, the existing channel information will be updated with the channel positions from the montage. Valid strings are the names of the built-in montages that ship with MNE-Python; you can list those via mne.channels.get_builtin_montages(). If None (default), the channel positions will be removed from the Info.

match_casebool

If True (default), channel name matching will be case sensitive.

New in v0.20.

match_aliasbool | dict

Whether to use a lookup table to match unrecognized channel location names to their known aliases. If True, uses the mapping in mne.io.constants.CHANNEL_LOC_ALIASES. If a dict is passed, it will be used instead, and should map from non-standard channel names to names in the specified montage. Default is False.

New in v0.23.

on_missing‘raise’ | ‘warn’ | ‘ignore’

Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when channels have missing coordinates.

New in v0.20.1.

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 | Evoked

The instance, modified in-place.

See also

mne.channels.make_standard_montage

mne.channels.make_dig_montage

mne.channels.read_custom_montage

Notes

Warning

Only EEG/sEEG/ECoG/DBS/fNIRS channels can have their positions set using a montage. Other channel types (e.g., MEG channels) should have their positions defined properly using their data reading functions.

Warning

Applying a montage will only set locations of channels that exist at the time it is applied. This means when re-referencing make sure to apply the montage only after calling mne.add_reference_channels()

shift_time(tshift, relative=True)

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.

standard_error(picks=None, by_event_type=False)

Compute standard error over epochs.

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 data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

by_event_typebool

When False (the default) all epochs are processed together and a single Evoked object is returned. When True, epochs are first grouped by event type (as specified using the event_id parameter) and a list is returned containing a separate Evoked object for each event type. The .comment attribute is set to the label of the event type.

New in v0.24.0.

Returns:

std_errinstance of Evoked | list of Evoked

The standard error over epochs. When by_event_type=True was specified, a list is returned containing a separate Evoked object for each event type. The list has the same order as the event types as specified in the event_id dictionary.

subtract_evoked(evoked=None)

Subtract an evoked response from each epoch.

Can be used to exclude the evoked response when analyzing induced activity, see e.g. [1].

Parameters:

evokedinstance of Evoked | None

The evoked response to subtract. If None, the evoked response is computed from Epochs itself.

Returns:

selfinstance of Epochs

The modified instance (instance is also modified inplace).

References

[1]

David et al. “Mechanisms of evoked and induced responses in MEG/EEG”, NeuroImage, vol. 31, no. 4, pp. 1580-1591, July 2006.

time_as_index(times, use_rounding=False)

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, scalings=None, copy=True, long_format=False, time_format=None, *, verbose=None)

Export data in tabular structure as a pandas DataFrame.

Channels are converted to columns in the DataFrame. By default, additional columns “time”, “epoch” (epoch number), 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).

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.Index 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’, ‘epoch’, and ‘condition’. Defaults to None.

scalingsdict | None

Scaling factor applied to the channels picked. If None, defaults to dict(eeg=1e6, mag=1e15, grad=1e13) — i.e., converts EEG to µV, magnetometers to fT, and gradiometers to fT/cm.

copybool

If True, data will be copied. Otherwise data may be modified in place. Defaults to True.

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 v0.20.

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 mne.EpochsArray

Creating MNE-Python data structures from scratch

Creating MNE-Python data structures from scratch

Single trial linear regression analysis with the LIMO dataset

Single trial linear regression analysis with the LIMO dataset