mne.Epochs

class mne.Epochs(raw, events, event_id=None, tmin=-0.2, tmax=0.5, baseline=(None, 0), picks=None, preload=False, reject=None, flat=None, proj=True, decim=1, reject_tmin=None, reject_tmax=None, detrend=None, on_missing='error', reject_by_annotation=True, verbose=None)[source]

Epochs extracted from a Raw instance.

Parameters:

raw : Raw object

An instance of Raw.

events : array of int, shape (n_events, 3)

The events typically returned by the read_events function. If some events don’t match the events of interest as specified by event_id, they will be marked as ‘IGNORED’ in the drop log.

event_id : int | list of int | dict | None

The id of the event 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, all events with the IDs specified in the list are used. If None, all events will be used with and a dict is created with string integer names corresponding to the event id integers.

tmin : float

Start time before event. If nothing is provided, defaults to -0.2

tmax : float

End time after event. If nothing is provided, defaults to 0.5

baseline : None or tuple of length 2 (default (None, 0))

The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. Correction is applied by computing mean of the baseline period and subtracting it from the data. The baseline (a, b) includes both endpoints, i.e. all timepoints t such that a <= t <= b.

picks : array-like of int | None (default)

Indices of channels to include (if None, all channels are used).

preload : boolean

Load all epochs from disk when creating the object or wait before accessing each epoch (more memory efficient but can be slower).

reject : dict | None

Rejection parameters based on peak-to-peak amplitude. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’. If reject is None then no rejection is done. Example:

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

flat : dict | None

Rejection parameters based on flatness of signal. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’, and values are floats that set the minimum acceptable peak-to-peak amplitude. If flat is None then no rejection is done.

proj : bool | ‘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.

decim : int

Factor by which to downsample the data from the raw file upon import. Warning: This simply selects every nth sample, data is not filtered here. If data is not properly filtered, aliasing artifacts may occur.

reject_tmin : scalar | None

Start of the time window used to reject epochs (with the default None, the window will start with tmin).

reject_tmax : scalar | None

End of the time window used to reject epochs (with the default None, the window will end with tmax).

detrend : int | None

If 0 or 1, the data channels (MEG and EEG) will be detrended when loaded. 0 is a constant (DC) detrend, 1 is a linear detrend. None is no detrending. Note that detrending is performed before baseline correction. If no DC offset is preferred (zeroth order detrending), either turn off baseline correction, as this may introduce a DC shift, or set baseline correction to use the entire time interval (will yield equivalent results but be slower).

on_missing : str

What to do if one or several event ids are not found in the recording. Valid keys are ‘error’ | ‘warning’ | ‘ignore’ Default is ‘error’. If on_missing is ‘warning’ 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.

reject_by_annotation : bool

Whether to reject based on annotations. If True (default), epochs overlapping with segments whose description begins with 'bad' are rejected. If False, no rejection based on annotations is performed.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to raw.verbose.

Notes

When accessing data, Epochs are detrended, baseline-corrected, and decimated, then projectors are (optionally) applied.

For indexing and slicing using epochs[...], see mne.Epochs.__getitem__().

Attributes

ch_names Channel names.
filename The filename.
info (instance of Info) Measurement info.
event_id (dict) Names of conditions corresponding to event_ids.
selection (array) List of indices of selected events (not dropped or ignored etc.). For example, if the original event array had 4 events and the second event has been dropped, this attribute would be np.array([0, 2, 3]).
preload (bool) Indicates whether epochs are in memory.
drop_log (list of lists) A list of the same length as the event array used to initialize the Epochs object. If the i-th original event is still part of the selection, drop_log[i] will be an empty list; otherwise it will be a list of the reasons the event is not longer in the selection, e.g.: ‘IGNORED’ if it isn’t part of the current subset defined by the user; ‘NO_DATA’ or ‘TOO_SHORT’ if epoch didn’t contain enough data; names of channels that exceeded the amplitude threshold; ‘EQUALIZED_COUNTS’ (see equalize_event_counts); or ‘USER’ for user-defined reasons (see drop method).
verbose (bool, str, int, or None) See above.

Methods

__contains__(ch_type) Check channel type membership.
__getitem__(item) Return an Epochs object with a copied subset of epochs.
__hash__() Hash the object.
__iter__() Facilitate iteration over epochs.
__len__() Return the number of epochs.
add_channels(add_list[, force_update_info]) Append new channels to the instance.
add_proj(projs[, remove_existing, verbose]) Add SSP projection vectors.
anonymize() Anonymize measurement information in place.
apply_baseline([baseline, verbose]) Baseline correct epochs.
apply_proj() Apply the signal space projection (SSP) operators to the data.
average([picks]) Compute average of epochs.
copy() Return copy of Epochs instance.
crop([tmin, tmax]) Crop a time interval from epochs object.
decimate(decim[, offset, verbose]) Decimate the epochs.
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) Drop some channels.
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.
filter(l_freq, h_freq[, picks, …]) Filter a subset of channels.
get_data() Get all epochs as a 3D array.
interpolate_bads([reset_bads, mode, verbose]) Interpolate bad MEG and EEG channels.
iter_evoked() 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_channels(ch_names) Pick some channels.
pick_types([meg, eeg, stim, eog, ecg, emg, …]) Pick some channels by type and names.
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, layout, axes]) Plot SSP vector.
plot_psd([fmin, fmax, tmin, tmax, proj, …]) Plot the power spectral density across epochs.
plot_psd_topomap([bands, vmin, vmax, tmin, …]) Plot the topomap of the power spectral density across epochs.
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) Rename channels.
resample(sfreq[, npad, window, n_jobs, pad, …]) Resample data.
save(fname[, split_size]) Save epochs in a fif file.
savgol_filter(h_freq[, copy, verbose]) Filter the data using Savitzky-Golay polynomial method.
set_channel_types(mapping) Define the sensor type of channels.
set_eeg_reference([ref_channels, …]) Specify which reference to use for EEG data.
set_montage(montage[, set_dig, verbose]) Set EEG sensor configuration and head digitization.
standard_error([picks]) 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, scaling_time, …]) Export data in tabular structure as a pandas DataFrame.
__contains__(ch_type)[source]

Check channel type membership.

Parameters:

ch_type : str

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

Returns:

in : bool

Whether or not the instance contains the given channel type.

Examples

Channel type membership can be tested as:

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

Return an Epochs object with a copied subset of epochs.

Parameters:

item : slice, array-like, str, or list

See below for use cases.

Returns:

epochs : instance of Epochs

See below for use cases.

Notes

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

  1. epochs[idx]: Return Epochs object with a subset of epochs (supports single index and python-style slicing).

  2. epochs['name']: Return Epochs object with a copy of the subset of epochs corresponding to an experimental condition as specified by ‘name’.

    If conditions are tagged by names separated by ‘/’ (e.g. ‘audio/left’, ‘audio/right’), and ‘name’ is not in itself an event key, this selects every event whose condition contains the ‘name’ tag (e.g., ‘left’ matches ‘audio/left’ and ‘visual/left’; but not ‘audio_left’). Note that tags like ‘auditory/left’ and ‘left/auditory’ will be treated the same way when accessed using tags.

  3. epochs[['name_1', 'name_2', ... ]]: Return Epochs object with a copy of the subset of epochs corresponding to multiple experimental conditions as specified by 'name_1', 'name_2', ... .

    If conditions are separated by ‘/’, selects every item containing every list tag (e.g. [‘audio’, ‘left’] selects ‘audio/left’ and ‘audio/center/left’, but not ‘audio/right’).

__hash__()[source]

Hash the object.

Returns:

hash : int

The hash

__iter__()[source]

Facilitate iteration over epochs.

Notes

This enables the use of this Python pattern:

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

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

__len__()[source]

Return the number of epochs.

Returns:

n_epochs : int

The number of remaining epochs.

Notes

This function only works if bad epochs have been dropped.

Examples

This can be used as:

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

Append new channels to the instance.

Parameters:

add_list : list

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

force_update_info : bool

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

New in version 0.12.

Returns:

inst : instance of Raw, Epochs, or Evoked

The modified instance.

add_proj(projs, remove_existing=False, verbose=None)[source]

Add SSP projection vectors.

Parameters:

projs : list

List with projection vectors.

remove_existing : bool

Remove the projection vectors currently in the file.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

self : instance of Raw | Epochs | Evoked

The data container.

anonymize()[source]

Anonymize measurement information in place.

Reset ‘subject_info’, ‘meas_date’, ‘file_id’, and ‘meas_id’ keys if they exist in info.

Returns:

info : instance of Info

Measurement information for the dataset.

Notes

Operates in place.

New in version 0.13.0.

apply_baseline(baseline=(None, 0), verbose=None)[source]

Baseline correct epochs.

Parameters:

baseline : tuple of length 2

The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. Correction is applied by computing mean of the baseline period and subtracting it from the data. The baseline (a, b) includes both endpoints, i.e. all timepoints t such that a <= t <= b.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

epochs : instance of Epochs

The baseline-corrected Epochs object.

Notes

Baseline correction can be done multiple times.

New in version 0.10.0.

apply_proj()[source]

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

Returns:

self : instance 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
average(picks=None)[source]

Compute average of epochs.

Parameters:

picks : array-like of int | None

If None only MEG, EEG, SEEG, ECoG, and fNIRS channels are kept otherwise the channels indices in picks are kept.

Returns:

evoked : instance of Evoked

The averaged epochs.

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.

ch_names

Channel names.

compensation_grade

The current gradient compensation grade.

copy()[source]

Return copy of Epochs instance.

crop(tmin=None, tmax=None)[source]

Crop a time interval from epochs object.

Parameters:

tmin : float | None

Start time of selection in seconds.

tmax : float | None

End time of selection in seconds.

Returns:

epochs : instance of Epochs

The cropped epochs.

Notes

Unlike Python slices, MNE time intervals include both their end points; crop(tmin, tmax) returns the interval tmin <= t <= tmax.

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

Decimate the epochs.

Note

No filtering is performed. To avoid aliasing, ensure your data are properly lowpassed.

Parameters:

decim : int

The amount to decimate data.

offset : int

Apply an offset to where the decimation starts relative to the sample corresponding to t=0. The offset is in samples at the current sampling rate.

New in version 0.12.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

epochs : instance of Epochs

The decimated Epochs object.

Notes

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

New in version 0.10.0.

del_proj(idx='all')[source]

Remove SSP projection vector.

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

idx : int | list of int | str

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

Returns:

self : instance of Raw | Epochs | Evoked

drop(indices, reason='USER', verbose=None)[source]

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:

indices : array of ints or bools

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

reason : str

Reason for dropping the epochs (‘ECG’, ‘timeout’, ‘blink’ etc). Default: ‘USER’.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.

Returns:

epochs : instance of Epochs

The epochs with indices dropped. Operates in-place.

drop_bad(reject='existing', flat='existing', verbose=None)[source]

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().

Parameters:

reject : dict | str | None

Rejection parameters based on peak-to-peak amplitude. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’. If reject is None then no rejection is done. If ‘existing’, then the rejection parameters set at instantiation are used.

flat : dict | str | None

Rejection parameters based on flatness of signal. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’, and values are floats that set the minimum acceptable peak-to-peak amplitude. If flat is None then no rejection is done. If ‘existing’, then the flat parameters set at instantiation are used.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.

Returns:

epochs : instance 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)[source]

Drop some channels.

Parameters:

ch_names : list

List of the names of the channels to remove.

Returns:

inst : instance of Raw, Epochs, or Evoked

The modified instance.

See also

pick_channels

Notes

New in version 0.9.0.

drop_log_stats(ignore=('IGNORED', ))[source]

Compute the channel stats based on a drop_log from Epochs.

Parameters:

ignore : list

The drop reasons to ignore.

Returns:

perc : float

Total percentage of epochs dropped.

See also

plot_drop_log

equalize_event_counts(event_ids, method='mintime')[source]

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 in the two sets of epochs. For example, if one had event times [1, 2, 3, 4, 120, 121] and the other one had [3.5, 4.5, 120.5, 121.5], it would remove events at times [1, 2] in the first epochs and not [20, 21].

Parameters:

event_ids : list

The event types to equalize. Each entry in the list can either be a str (single event) or a list of str. In the case where one of the entries is a list of str, event_ids in that list will be grouped together before equalizing trial counts across conditions. In the case where partial matching is used (using ‘/’ in event_ids), event_ids 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 nonoverlapping subsets of the epochs.

method : str

If ‘truncate’, events will be truncated from the end of each event list. If ‘mintime’, timing differences between each event list will be minimized.

Returns:

epochs : instance of Epochs

The modified Epochs instance.

indices : array 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.

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 evenly. E.g., it is possible to end up with 70 ‘Nonspatial’ trials, 69 ‘Left’ and 1 ‘Right’.

filename

The filename.

filter(l_freq, h_freq, picks=None, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design=None, pad='edge', verbose=None)[source]

Filter a subset of channels.

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 temporaily stored in memory.

Parameters:

l_freq : float | None

Low cut-off frequency in Hz. If None the data are only low-passed.

h_freq : float | None

High cut-off frequency in Hz. If None the data are only high-passed.

picks : array-like of int | None

Indices of channels to filter. If None only the data (MEG/EEG) channels will be filtered.

filter_length : str | 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_bandwidth : float | 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_bandwidth : float | 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) 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_jobs : int | str

Number of jobs to run in parallel. Can be ‘cuda’ if scikits.cuda is installed properly, CUDA is initialized, and method=’fir’.

method : str

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

iir_params : dict | None

Dictionary of parameters to use for IIR filtering. See mne.filter.construct_iir_filter for details. If iir_params is None and method=”iir”, 4th order Butterworth will be used.

phase : str

Phase of the filter, only used if method='fir'. By default, a symmetric linear-phase FIR filter is constructed. If phase='zero' (default), the delay of this filter is compensated for. If phase=='zero-double', then this filter is applied twice, once forward, and once backward. If ‘minimum’, then a minimum-phase, causal filter will be used.

fir_window : str

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

fir_design : str

Can be “firwin” (default in 0.16) to use scipy.signal.firwin(), or “firwin2” (default in 0.15 and before) to use scipy.signal.firwin2(). “firwin” uses a time-domain design technique that generally gives improved attenuation using fewer samples than “firwin2”.

pad : str

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. The default is “edge”, which pads with the edge values of each vector. Only used for method='fir'.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.

Returns:

inst : instance of Epochs or Evoked

The filtered data.

Notes

New in version 0.15.

get_data()[source]

Get all epochs as a 3D array.

Returns:

data : array of shape (n_epochs, n_channels, n_times)

A view on epochs data.

interpolate_bads(reset_bads=True, mode='accurate', verbose=None)[source]

Interpolate bad MEG and EEG channels.

Operates in place.

Parameters:

reset_bads : bool

If True, remove the bads from info.

mode : str

Either 'accurate' or 'fast', determines the quality of the Legendre polynomial expansion used for interpolation of MEG channels.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

inst : instance of Raw, Epochs, or Evoked

The modified instance.

Notes

New in version 0.9.0.

iter_evoked()[source]

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).

load_data()[source]

Load the data if not already preloaded.

Returns:

epochs : instance of Epochs

The epochs object.

Notes

This function operates in-place.

New in version 0.10.0.

next(return_event_id=False)[source]

Iterate over epoch data.

Parameters:

return_event_id : bool

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

Returns:

epoch : array of shape (n_channels, n_times)

The epoch data.

event_id : int

The event id. Only returned if return_event_id is True.

pick_channels(ch_names)[source]

Pick some channels.

Parameters:

ch_names : list

The list of channels to select.

Returns:

inst : instance of Raw, Epochs, or Evoked

The modified instance.

See also

drop_channels

Notes

New in version 0.9.0.

pick_types(meg=True, 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, include=(), exclude='bads', selection=None)[source]

Pick some channels by type and names.

Parameters:

meg : bool | str

If True include all MEG channels. If False include None If string it can be ‘mag’, ‘grad’, ‘planar1’ or ‘planar2’ to select only magnetometers, all gradiometers, or a specific type of gradiometer.

eeg : bool

If True include EEG channels.

stim : bool

If True include stimulus channels.

eog : bool

If True include EOG channels.

ecg : bool

If True include ECG channels.

emg : bool

If True include EMG channels.

ref_meg: bool | str

If True include CTF / 4D reference channels. If ‘auto’, the reference channels are only included if compensations are present.

misc : bool

If True include miscellaneous analog channels.

resp : bool

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

chpi : bool

If True include continuous HPI coil channels.

exci : bool

Flux excitation channel used to be a stimulus channel.

ias : bool

Internal Active Shielding data (maybe on Triux only).

syst : bool

System status channel information (on Triux systems only).

seeg : bool

Stereotactic EEG channels.

dipole : bool

Dipole time course channels.

gof : bool

Dipole goodness of fit channels.

bio : bool

Bio channels.

ecog : bool

Electrocorticography channels.

fnirs : bool | str

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

include : list of string

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

exclude : list of string | str

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

selection : list of string

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

Returns:

inst : instance of Raw, Epochs, or Evoked

The modified instance.

Notes

New in version 0.9.0.

plot(picks=None, scalings=None, n_epochs=20, n_channels=20, title=None, events=None, event_colors=None, show=True, block=False, decim='auto')[source]

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:

picks : array-like of int | None

Channels to be included. If None only good data channels are used. Defaults to None

scalings : dict | ‘auto’ | None

Scaling factors for the traces. If any fields in scalings are ‘auto’, the scaling factor is set to match the 99.5th percentile of a subset of the corresponding data. If scalings == ‘auto’, all scalings fields are set to ‘auto’. If any fields are ‘auto’ and data is not preloaded, a subset of epochs up to 100mb 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)

n_epochs : int

The number of epochs per view. Defaults to 20.

n_channels : int

The number of channels per view. Defaults to 20.

title : str | None

The title of the window. If None, epochs name will be displayed. Defaults to None.

events : None, array, shape (n_events, 3)

Events to show with vertical bars. If events are provided, the epoch numbers are not shown to prevent overlap. You can toggle epoch numbering through options (press ‘o’ key). You can use mne.viz.plot_events() as a legend for the colors. By default, the coloring scheme is the same.

Warning

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

New in version 0.14.0.

event_colors : None, dict

Dictionary of event_id value and its associated color. If None, colors are automatically drawn from a default list (cycled through if number of events longer than list of default colors). Uses the same coloring scheme as mne.viz.plot_events().

New in version 0.14.0.

show : bool

Show figure if True. Defaults to True

block : bool

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.

decim : int | ‘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 version 0.15.

Returns:

fig : Instance of matplotlib.figure.Figure

The figure.

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. These can also be set through options dialog by pressing o key. h key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled with b key. Right mouse click adds a vertical line to the plot. Click ‘help’ button at bottom left corner of the plotter to view all the options.

New in version 0.10.0.

plot_drop_log(threshold=0, n_max_plot=20, subject='Unknown', color=(0.9, 0.9, 0.9), width=0.8, ignore=('IGNORED', ), show=True)[source]

Show the channel stats based on a drop_log from Epochs.

Parameters:

threshold : float

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

n_max_plot : int

Maximum number of channels to show stats for.

subject : str

The subject name to use in the title of the plot.

color : tuple | str

Color to use for the bars.

width : float

Width of the bars.

ignore : list

The drop reasons to ignore.

show : bool

Show figure if True.

Returns:

fig : Instance 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={}, title=None)[source]

Plot Event Related Potential / Fields image.

Parameters:

picks : int | array-like of int | None

The indices of the channels to consider. If None and combine is also None, the first five good channels are plotted.

sigma : float

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

vmin : None | float | callable

The min value in the image (and the ER[P/F]). The unit is uV 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().

vmax : None | float | callable

The max value in the image (and the ER[P/F]). The unit is uV 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.

colorbar : bool

Display or not a colorbar.

order : None | 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).

show : bool

Show figure if True.

units : dict | None

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

scalings : dict | 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).

cmap : None | matplotlib 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.

fig : matplotlib figure | None

Figure instance to draw the image to. Figure must contain two axes for drawing the single trials and evoked responses. If None a new figure is created. Defaults to None.

axes : list of matplotlib axes | dict of lists of matplotlib Axes | None

List of axes instances to draw the image, erp and colorbar to. Must be of length three if colorbar is True (with the last list element being the colorbar axes) or two if colorbar is False. If both fig and axes are passed, an error is raised. If group_by is a dict, this cannot be a list, but it can be a dict of lists of axes, with the keys matching those of group_by. In that case, the provided axes will be used for the corresponding groups. Defaults to None.

overlay_times : array-like, shape (n_epochs,) | None

If not None the parameter is interpreted as time instants in seconds and is added to the image. It is typically useful to display reaction times. Note that it is defined with respect to the order of epochs such that overlay_times[0] corresponds to epochs[0].

combine : None | str | callable

If None, return one figure per pick. If not None, aggregate over channels via the indicated method. If str, must be one of “mean”, “median”, “std” or “gfp”, in which case the mean, the median, the standard deviation or the GFP over channels are plotted. array (n_epochs, n_times). If callable, it must accept one positional input, the data in the format (n_epochs, n_channels, n_times). It must return an array (n_epochs, n_times). For example:

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

Defaults to None if picks are provided, otherwise ‘gfp’.

group_by : None | str | dict

If not None, combining happens over channel groups defined by this parameter. If str, must be “type”, in which case one figure per channel type is returned (combining within channel types). If a dict, the values must be picks and one figure will be returned for each entry, aggregating over the corresponding pick groups; keys will become plot titles. This is useful for e.g. ROIs. Each entry must contain only one channel type. For example:

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

If not None, combine must not be None. Defaults to None if picks are provided, otherwise ‘type’.

evoked : Bool

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

ts_args : dict

Arguments passed to a call to mne.viz.plot_compare_evoked to style the evoked plot below the image. Defaults to an empty dictionary, meaning plot_compare_evokeds will be called with default parameters (yaxis truncation will be turned off).

title : None | str

If str, will be plotted as figure title. Else, the channels will be indicated.

Returns:

figs : lists of matplotlib figures

One figure per channel displayed.

plot_projs_topomap(ch_type=None, layout=None, axes=None)[source]

Plot SSP vector.

Parameters:

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

The channel type to plot. For ‘grad’, the gradiometers are collec- ted in pairs and the RMS for each pair is plotted. If None (default), it will return all channel types present. If a list of ch_types is provided, it will return multiple figures.

layout : None | Layout | List of Layouts

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout file is inferred from the data; if no appropriate layout file was found, the layout is automatically generated from the sensor locations. Or a list of Layout if projections are from different sensor types.

axes : instance of Axes | list | None

The axes to plot to. If list, the list must be a list of Axes of the same length as the number of projectors. If instance of Axes, there must be only one projector. Defaults to None.

Returns:

fig : instance of matplotlib figure

Figure distributing one image per channel across sensor topography.

plot_psd(fmin=0, fmax=inf, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', picks=None, ax=None, color='black', area_mode='std', area_alpha=0.33, dB=True, n_jobs=1, show=True, verbose=None)[source]

Plot the power spectral density across epochs.

Parameters:

fmin : float

Start frequency to consider.

fmax : float

End frequency to consider.

tmin : float | None

Start time to consider.

tmax : float | None

End time to consider.

proj : bool

Apply projection.

bandwidth : float

The bandwidth of the multi taper windowing function in Hz. The default value is a window half-bandwidth of 4.

adaptive : bool

Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).

low_bias : bool

Only use tapers with more than 90% spectral concentration within bandwidth.

normalization : str

Either “full” or “length” (default). If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in nitime).

picks : array-like of int | None

List of channels to use.

ax : instance of matplotlib Axes | None

Axes to plot into. If None, axes will be created.

color : str | tuple

A matplotlib-compatible color to use.

area_mode : str | 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.

area_alpha : float

Alpha for the area.

dB : bool

If True, transform data to decibels.

n_jobs : int

Number of jobs to run in parallel.

show : bool

Show figure if True.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

fig : instance of matplotlib figure

Figure distributing one image per channel across sensor topography.

plot_psd_topomap(bands=None, vmin=None, vmax=None, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', ch_type=None, layout=None, cmap='RdBu_r', agg_fun=None, dB=True, n_jobs=1, normalize=False, cbar_fmt='%0.3f', outlines='head', axes=None, show=True, verbose=None)[source]

Plot the topomap of the power spectral density across epochs.

Parameters:

bands : list of tuple | None

The lower and upper frequency and the name for that band. If None, (default) expands to:

bands = [(0, 4, ‘Delta’), (4, 8, ‘Theta’), (8, 12, ‘Alpha’),

(12, 30, ‘Beta’), (30, 45, ‘Gamma’)]

vmin : float | callable | None

The value specifying the lower bound of the color range. If None np.min(data) is used. If callable, the output equals vmin(data).

vmax : float | callable | None

The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If callable, the output equals vmax(data). Defaults to None.

tmin : float | None

Start time to consider.

tmax : float | None

End time to consider.

proj : bool

Apply projection.

bandwidth : float

The bandwidth of the multi taper windowing function in Hz. The default value is a window half-bandwidth of 4 Hz.

adaptive : bool

Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).

low_bias : bool

Only use tapers with more than 90% spectral concentration within bandwidth.

normalization : str

Either “full” or “length” (default). If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in nitime).

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

The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None.

layout : None | Layout

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout file is inferred from the data; if no appropriate layout file was found, the layout is automatically generated from the sensor locations.

cmap : matplotlib 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 (default), ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to (None, True).

agg_fun : callable

The function used to aggregate over frequencies. Defaults to np.sum. if normalize is True, else np.mean.

dB : bool

If True, transform data to decibels (with 10 * np.log10(data)) following the application of agg_fun. Only valid if normalize is False.

n_jobs : int

Number of jobs to run in parallel.

normalize : bool

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

cbar_fmt : str

The colorbar format. Defaults to ‘%0.3f’.

outlines : ‘head’ | ‘skirt’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask, and the ‘autoshrink’ (bool) field will trigger automated shrinking of the positions due to points outside the outline. 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’.

axes : list of axes | None

List of axes to plot consecutive topographies to. If None the axes will be created automatically. Defaults to None.

show : bool

Show figure if True.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

fig : instance of matplotlib figure

Figure distributing one image per channel across sensor topography.

plot_sensors(kind='topomap', ch_type=None, title=None, show_names=False, ch_groups=None, to_sphere=True, axes=None, block=False, show=True)[source]

Plot sensor positions.

Parameters:

kind : str

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_type : None | str

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

title : str | None

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

show_names : bool | 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 (ch_groups, 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 version 0.13.0.

to_sphere : bool

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 version 0.14.0.

axes : instance 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 version 0.13.0.

block : bool

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

New in version 0.13.0.

show : bool

Show figure if True. Defaults to True.

Returns:

fig : instance of matplotlib figure

Figure containing the sensor topography.

selection : list

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

Notes

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

New in version 0.12.0.

plot_topo_image(layout=None, sigma=0.0, vmin=None, vmax=None, colorbar=True, 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)[source]

Plot Event Related Potential / Fields image on topographies.

Parameters:

layout: instance of Layout

System specific sensor positions.

sigma : float

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

vmin : float

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

vmax : float

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

colorbar : bool

Display or not a colorbar.

order : None | 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)).

cmap : instance of matplotlib.pyplot.colormap

Colors to be mapped to the values.

layout_scale: float

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

title : str

Title of the figure.

scalings : dict | None

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

border : str

matplotlib borders style to be used for each sensor plot.

fig_facecolor : str | obj

The figure face color. Defaults to black.

fig_background : None | array

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

font_color : str | obj

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

show : bool

Show figure if True.

Returns:

fig : instance of matplotlib figure

Figure distributing one image per channel across sensor topography.

proj

Whether or not projections are active.

rename_channels(mapping)[source]

Rename channels.

Parameters:

mapping : dict | 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 (new in version 0.10.0).

Notes

New in version 0.9.0.

resample(sfreq, npad='auto', window='boxcar', n_jobs=1, pad='edge', verbose=None)[source]

Resample data.

Note

Data must be loaded.

Parameters:

sfreq : float

New sample rate to use

npad : int | 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).

window : string or tuple

Window to use in resampling. See scipy.signal.resample().

n_jobs : int

Number of jobs to run in parallel.

pad : str

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. The default is “edge”, which pads with the edge values of each vector.

New in version 0.15.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() Logging documentation for more). Defaults to self.verbose.

Returns:

inst : instance of Epochs | instance of Evoked

The resampled epochs object.

Notes

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

save(fname, split_size='2GB')[source]

Save epochs in a fif file.

Parameters:

fname : str

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

split_size : string | 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 version 0.10.0.

Notes

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

savgol_filter(h_freq, copy=False, verbose=None)[source]

Filter the data using Savitzky-Golay polynomial method.

Parameters:

h_freq : float

Approximate high cut-off frequency in Hz. Note that this is not an exact cutoff, since Savitzky-Golay filtering [R669] 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.

copy : bool

Deprecated. Use inst.copy() instead.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.

Returns:

inst : instance of Epochs or Evoked

The object with the filtering applied.

Notes

For Savitzky-Golay low-pass approximation, see:

New in version 0.9.0.

References

[R669](1, 2) Savitzky, A., Golay, M.J.E. (1964). “Smoothing and Differentiation of Data by Simplified Least Squares Procedures”. Analytical Chemistry 36 (8): 1627-39.

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_channel_types(mapping)[source]

Define the sensor type of channels.

Note: The following sensor types are accepted:
ecg, eeg, emg, eog, exci, ias, misc, resp, seeg, stim, syst, ecog, hbo, hbr
Parameters:

mapping : dict

a dictionary mapping a channel to a sensor type (str) {‘EEG061’: ‘eog’}.

Notes

New in version 0.9.0.

set_eeg_reference(ref_channels='average', projection=None, verbose=None)[source]

Specify which reference to use for EEG data.

By default, MNE-Python will automatically re-reference the EEG signal to use an average reference (see below). 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 and prevent MNE-Python from automatically adding an average reference projection.

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

No re-referencing:
If the EEG data is already using the proper reference, set ref_channels=[]. This will prevent MNE-Python from automatically adding an average reference projection.
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'].

Note

In case of ref_channels='average' in combination with projection=True, the reference is added as a projection and it is not applied automatically. For it to take effect, apply with method apply_proj. Other references are directly applied (this behavior will change in MNE 0.16).

Parameters:

ref_channels : list of str | str

The name(s) of the channel(s) used to construct the reference. To apply an average reference, specify 'average' here (default). If an empty list is specified, the data is assumed to already have a proper reference and MNE will not attempt any re-referencing of the data. Defaults to an average reference.

projection : bool | None

If ref_channels='average' this argument specifies if the average reference should be computed as a projection (True) or not (False). 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. Defaults to None, which means projection=True, but will change to projection=False in the next release. If ref_channels is not 'average', projection must be set to False (the default in this case).

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

inst : instance 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

  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 reference, bad EEG channels are automatically excluded if they are properly set in info['bads'].

New in version 0.9.0.

set_montage(montage, set_dig=True, verbose=None)[source]

Set EEG sensor configuration and head digitization.

Parameters:

montage : instance of Montage or DigMontage

The montage to use.

set_dig : bool

If True, update the digitization information (info['dig']) in addition to the channel positions (info['chs'][idx]['loc']).

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Notes

Operates in place.

New in version 0.9.0.

standard_error(picks=None)[source]

Compute standard error over epochs.

Parameters:

picks : array-like of int | None

If None only MEG, EEG, SEEG, ECoG, and fNIRS channels are kept otherwise the channels indices in picks are kept.

Returns:

evoked : instance of Evoked

The standard error over epochs.

subtract_evoked(evoked=None)[source]

Subtract an evoked response from each epoch.

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

Parameters:

evoked : instance of Evoked | None

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

Returns:

self : instance 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)[source]

Convert time to indices.

Parameters:

times : list-like | float | int

List of numbers or a number representing points in time.

use_rounding : boolean

If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices.

Returns:

index : ndarray

Indices corresponding to the times supplied.

tmax

Last time point.

tmin

First time point.

to_data_frame(picks=None, index=None, scaling_time=1000.0, scalings=None, copy=True, start=None, stop=None, scale_time=None)[source]

Export data in tabular structure as a pandas DataFrame.

Columns and indices will depend on the object being converted. Generally this will include as much relevant information as possible for the data type being converted. This makes it easy to convert data for use in packages that utilize dataframes, such as statsmodels or seaborn.

Parameters:

picks : array-like of int | None

If None only MEG and EEG channels are kept otherwise the channels indices in picks are kept.

index : tuple of str | None

Column to be used as index for the data. Valid string options are ‘epoch’, ‘time’ and ‘condition’. If None, all three info columns will be included in the table as categorial data.

scaling_time : float

Scaling to be applied to time units.

scalings : dict | None

Scaling to be applied to the channels picked. If None, defaults to scalings=dict(eeg=1e6, grad=1e13, mag=1e15, misc=1.0).

copy : bool

If true, data will be copied. Else data may be modified in place.

start : int | None

If it is a Raw object, this defines a starting index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal.

stop : int | None

If it is a Raw object, this defines a stop index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal.

Returns:

df : instance of pandas.core.DataFrame

A dataframe suitable for usage with other statistical/plotting/analysis packages. Column/Index values will depend on the object type being converted, but should be human-readable.