mne.EvokedArray¶

class mne.EvokedArray(data, info, tmin=0.0, comment='', nave=1, kind='average', verbose=None)[source]

Evoked object from numpy array.

Parameters
dataarray of shape (n_channels, n_times)

The channels’ evoked response. See notes for proper units of measure.

infoinstance of Info

Info dictionary. Consider using create_info to populate this structure.

tminfloat

Start time before event. Defaults to 0.

commentstr

Comment on dataset. Can be the condition. Defaults to ‘’.

naveint

Number of averaged epochs. Defaults to 1.

kindstr

Type of data, either average or standard_error. Defaults to ‘average’.

verbose

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

Notes

Proper units of measure: * V: eeg, eog, seeg, emg, ecg, bio, ecog * T: mag * T/m: grad * M: hbo, hbr * Am: dipole * AU: misc

Attributes

 ch_names Channel names. compensation_grade The current gradient compensation grade. data The data matrix. proj Whether or not projections are active.

Methods

 __contains__(self, ch_type) Check channel type membership. __hash__(self) Hash the object. __neg__(self) Negate channel responses. add_channels(self, add_list[, force_update_info]) Append new channels to the instance. add_proj(self, projs[, remove_existing, verbose]) Add SSP projection vectors. animate_topomap(self[, ch_type, times, …]) Make animation of evoked data as topomap timeseries. anonymize(self) Anonymize measurement information in place. apply_baseline(self[, baseline, verbose]) Baseline correct evoked data. apply_hilbert(self[, picks, envelope, …]) Compute analytic signal or envelope for a subset of channels. apply_proj(self) Apply the signal space projection (SSP) operators to the data. as_type(self[, ch_type, mode]) Compute virtual evoked using interpolated fields. copy(self) Copy the instance of evoked. crop(self[, tmin, tmax]) Crop data to a given time interval. decimate(self, decim[, offset]) Decimate the evoked data. del_proj(self[, idx]) Remove SSP projection vector. detrend(self[, order, picks]) Detrend data. drop_channels(self, ch_names) Drop channel(s). filter(self, l_freq, h_freq[, picks, …]) Filter a subset of channels. get_peak(self[, ch_type, tmin, tmax, mode, …]) Get location and latency of peak amplitude. interpolate_bads(self[, reset_bads, mode, …]) Interpolate bad MEG and EEG channels. pick(self, picks[, exclude]) Pick a subset of channels. pick_channels(self, ch_names) Pick some channels. pick_types(self[, meg, eeg, stim, eog, ecg, …]) Pick some channels by type and names. plot(self[, picks, exclude, unit, show, …]) Plot evoked data using butterfly plots. plot_field(self, surf_maps[, time, …]) Plot MEG/EEG fields on head surface and helmet in 3D. plot_image(self[, picks, exclude, unit, …]) Plot evoked data as images. plot_joint(self[, times, title, picks, …]) Plot evoked data as butterfly plot and add topomaps for time points. plot_projs_topomap(self[, ch_type, layout, axes]) Plot SSP vector. plot_sensors(self[, kind, ch_type, title, …]) Plot sensor positions. plot_topo(self[, layout, layout_scale, …]) Plot 2D topography of evoked responses. plot_topomap(self[, times, ch_type, layout, …]) Plot topographic maps of specific time points of evoked data. plot_white(self, noise_cov[, show, rank, …]) Plot whitened evoked response. rename_channels(self, mapping) Rename channels. reorder_channels(self, ch_names) Reorder channels. resample(self, sfreq[, npad, window, …]) Resample data. save(self, fname) Save dataset to file. savgol_filter(self, h_freq[, verbose]) Filter the data using Savitzky-Golay polynomial method. set_channel_types(self, mapping) Define the sensor type of channels. set_eeg_reference(self[, ref_channels, …]) Specify which reference to use for EEG data. set_montage(self, montage[, set_dig, verbose]) Set EEG sensor configuration and head digitization. shift_time(self, tshift[, relative]) Shift time scale in evoked data. time_as_index(self, times[, use_rounding]) Convert time to indices. to_data_frame(self[, picks, index, …]) Export data in tabular structure as a pandas DataFrame.
__contains__(self, ch_type)[source]

Check channel type membership.

Parameters
ch_typestr

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

Returns
inbool

Whether or not the instance contains the given channel type.

Examples

Channel type membership can be tested as:

>>> 'meg' in inst
True
>>> 'seeg' in inst
False

__hash__(self)[source]

Hash the object.

Returns
hashint

The hash

__neg__(self)[source]

Negate channel responses.

Returns
evoked_neginstance of Evoked

The Evoked instance with channel data negated and ‘-‘ prepended to the comment.

add_channels(self, add_list, force_update_info=False)[source]

Append new channels to the instance.

Parameters
add_listlist

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

force_update_infobool

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

New in version 0.12.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

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

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

Parameters
projslist

List with projection vectors.

remove_existingbool

Remove the projection vectors currently in the file.

verbose

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

Returns
selfinstance of Raw | Epochs | Evoked

The data container.

animate_topomap(self, ch_type=None, times=None, frame_rate=None, butterfly=False, blit=True, show=True, time_unit='s')[source]

Make animation of evoked data as topomap timeseries.

The animation can be paused/resumed with left mouse button. Left and right arrow keys can be used to move backward or forward in time.

Parameters
ch_type

Channel type to plot. Accepted data types: ‘mag’, ‘grad’, ‘eeg’. If None, first available channel type from (‘mag’, ‘grad’, ‘eeg’) is used. Defaults to None.

times

The time points to plot. If None, 10 evenly spaced samples are calculated over the evoked time series. Defaults to None.

frame_rate

Frame rate for the animation in Hz. If None, frame rate = sfreq / 10. Defaults to None.

butterflybool

Whether to plot the data as butterfly plot under the topomap. Defaults to False.

blitbool

Whether to use blit to optimize drawing. In general, it is recommended to use blit in combination with show=True. If you intend to save the animation it is better to disable blit. Defaults to True.

showbool

Whether to show the animation. Defaults to True.

time_unitstr

The units for the time axis, can be “ms” (default in 0.16) or “s” (will become the default in 0.17).

New in version 0.16.

Returns
figinstance of matplotlib.figure.Figure

The figure.

animinstance of matplotlib.animation.FuncAnimation

Animation of the topomap.

Notes

New in version 0.12.0.

anonymize(self)[source]

Anonymize measurement information in place.

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

Returns
infoinstance of Info

Measurement information for the dataset.

Notes

Operates in place.

New in version 0.13.0.

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

Baseline correct evoked data.

Parameters
baselinetuple 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

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

Returns
evokedinstance of Evoked

The baseline-corrected Evoked object.

Notes

Baseline correction can be done multiple times.

New in version 0.13.0.

apply_hilbert(self, picks=None, envelope=False, n_jobs=1, n_fft='auto', verbose=None)[source]

Compute analytic signal or envelope for a subset of channels.

Parameters
picks

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).

envelopebool (default: False)

Compute the envelope signal of each channel. See Notes.

n_jobs: int

Number of jobs to run in parallel.

n_fft

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.

verbose

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

Returns
selfinstance of Raw, Epochs, or Evoked

The raw object with transformed data.

Notes

Parameters

If envelope=False, the analytic signal for the channels 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 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 temporaily 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(self)[source]

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

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')
# add the first, copy, apply and see ...
# add the second, copy, apply and see ...
# drop the first and see again
evoked.copy().del_proj(0).apply_proj().plot()
evoked.apply_proj()  # finally keep both

as_type(self, ch_type='grad', mode='fast')[source]

Compute virtual evoked using interpolated fields.

Warning

Using virtual evoked 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
evokedinstance of mne.Evoked

The transformed evoked object containing only virtual channels.

Notes

New in version 0.9.0.

ch_names

Channel names.

compensation_grade

copy(self)[source]

Copy the instance of evoked.

Returns
evokedinstance of Evoked
crop(self, tmin=None, tmax=None)[source]

Crop data to a given time interval.

Parameters
tmin

Start time of selection in seconds.

tmax

End time of selection in seconds.

Returns
evokedinstance of Evoked

The cropped Evoked object.

Notes

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

data

The data matrix.

decimate(self, decim, offset=0)[source]

Decimate the evoked data.

Note

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

Parameters
decimint

The amount to decimate data.

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.

Returns
evokedinstance of Evoked

The decimated Evoked object.

Notes

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

New in version 0.13.0.

del_proj(self, 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

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

Returns
selfinstance of Raw | Epochs | Evoked
detrend(self, order=1, picks=None)[source]

Detrend data.

This function operates in-place.

Parameters
orderint

Either 0 or 1, the order of the detrending. 0 is a constant (DC) detrend, 1 is a linear detrend.

picks

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.

Returns
evokedinstance of Evoked

The detrended evoked object.

drop_channels(self, ch_names)[source]

Drop channel(s).

Parameters
ch_names

List of channel name(s) or channel name to remove.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

New in version 0.9.0.

filter(self, 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='firwin', pad='edge', verbose=None)[source]

Filter a subset of channels.

Parameters
l_freq

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

h_freq

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

picks

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.

filter_length

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

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

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_jobs

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_params

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

phasestr

Phase of the filter, only used if method='fir'. Symmetric linear-phase FIR filters are constructed, and if phase='zero' (default), the delay of this filter is compensated for, making it non-causal. If phase=='zero-double', then this filter is applied twice, once forward, and once backward (also making it non-causal). If ‘minimum’, then a minimum-phase filter will be constricted and applied, which is causal but has weaker stop-band suppression.

New in version 0.13.

fir_windowstr

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

New in version 0.15.

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 version 0.15.

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

verbose

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

Returns
instinstance of Epochs or Evoked

The filtered data.

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 temporaily stored in memory.

New in version 0.15.

get_peak(self, ch_type=None, tmin=None, tmax=None, mode='abs', time_as_index=False, merge_grads=False, return_amplitude=False)[source]

Get location and latency of peak amplitude.

Parameters
ch_type‘mag’, ‘grad’, ‘eeg’, ‘seeg’, ‘ecog’, ‘hbo’, hbr’, ‘misc’, None

The channel type to use. Defaults to None. If more than one sensor Type is present in the data the channel type has to be explicitly set.

tmin

The minimum point in time to be considered for peak getting. If None (default), the beginning of the data is used.

tmax

The maximum point in time to be considered for peak getting. If None (default), the end of the data is used.

mode{‘pos’, ‘neg’, ‘abs’}

How to deal with the sign of the data. If ‘pos’ only positive values will be considered. If ‘neg’ only negative values will be considered. If ‘abs’ absolute values will be considered. Defaults to ‘abs’.

time_as_indexbool

Whether to return the time index instead of the latency in seconds.

If True, compute peak from merged gradiometer data.

return_amplitudebool

If True, return also the amplitude at the maximum response.

New in version 0.16.

Returns
ch_namestr

The channel exhibiting the maximum response.

latency

The time point of the maximum response, either latency in seconds or index.

amplitudefloat

The amplitude of the maximum response. Only returned if return_amplitude is True.

New in version 0.16.

interpolate_bads(self, reset_bads=True, mode='accurate', origin=(0.0, 0.0, 0.04), verbose=None)[source]

Interpolate bad MEG and EEG channels.

Operates in place.

Parameters

If True, remove the bads from info.

modestr

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

originarray_like, shape (3,) | str

Origin of the sphere in the head coordinate frame and in meters. Can be 'auto', which means a head-digitization-based origin fit. Default is (0., 0., 0.04).

New in version 0.17.

verbose

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

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

New in version 0.9.0.

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

Pick a subset of channels.

Parameters
picks

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.

exclude

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

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

pick_channels(self, ch_names)[source]

Pick some channels.

Parameters
ch_nameslist

The list of channels to select.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

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

New in version 0.9.0.

pick_types(self, 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, verbose=None)[source]

Pick some channels by type and names.

Parameters
meg

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.

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_meg: bool | str

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

miscbool

If True include miscellaneous analog channels.

respbool

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

chpibool

If True include continuous HPI coil channels.

excibool

Flux excitation channel used to be a stimulus channel.

iasbool

Internal Active Shielding data (maybe on Triux only).

systbool

System status channel information (on Triux systems only).

seegbool

Stereotactic EEG channels.

dipolebool

Dipole time course channels.

gofbool

Dipole goodness of fit channels.

biobool

Bio channels.

ecogbool

Electrocorticography channels.

fnirs

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

exclude

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

selection

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

verbose

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

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

New in version 0.9.0.

plot(self, picks=None, exclude='bads', unit=True, show=True, ylim=None, xlim='tight', proj=False, hline=None, units=None, scalings=None, titles=None, axes=None, gfp=False, window_title=None, spatial_colors=False, zorder='unsorted', selectable=True, noise_cov=None, time_unit='s', verbose=None)[source]

Plot evoked data using butterfly plots.

Left click to a line shows the channel name. Selecting an area by clicking and holding left mouse button plots a topographic map of the painted area.

Note

If bad channels are not excluded they are shown in red.

Parameters
picks

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.

excludelist of str | ‘bads’

Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded.

unitbool

Scale plot with channel (SI) unit.

showbool

Show figure if True.

ylim

ylim for plots (after scaling has been applied). e.g. ylim = dict(eeg=[-20, 20]) Valid keys are eeg, mag, grad, misc. If None, the ylim parameter for each channel equals the pyplot default.

xlim‘tight’ | tuple | None

xlim for plots.

projbool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown.

hline

The values at which to show an horizontal line.

units

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

scalings

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

titles

The titles associated with the channels. If None, defaults to dict(eeg=’EEG’, grad=’Gradiometers’, mag=’Magnetometers’).

axesinstance 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 channel types. If instance of Axes, there must be only one channel type plotted.

gfpbool | ‘only’

Plot GFP in green if True or “only”. If “only”, then the individual channel traces will not be shown.

window_title

The title to put at the top of the figure.

spatial_colorsbool

If True, the lines are color coded by mapping physical sensor coordinates into color values. Spatially similar channels will have similar colors. Bad channels will be dotted. If False, the good channels are plotted black and bad channels red. Defaults to False.

zorder

Which channels to put in the front or back. Only matters if spatial_colors is used. If str, must be std or unsorted (defaults to unsorted). If std, data with the lowest standard deviation (weakest effects) will be put in front so that they are not obscured by those with stronger effects. If unsorted, channels are z-sorted as in the evoked instance. If callable, must take one argument: a numpy array of the same dimensionality as the evoked raw data; and return a list of unique integers corresponding to the number of channels.

New in version 0.13.0.

selectablebool

Whether to use interactive features. If True (default), it is possible to paint an area to draw topomaps. When False, the interactive features are disabled. Disabling interactive features reduces memory consumption and is useful when using axes parameter to draw multiaxes figures.

New in version 0.13.0.

noise_covinstance of Covariance | str | None

Noise covariance used to whiten the data while plotting. Whitened data 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 version 0.16.0.

time_unitstr

The units for the time axis, can be “ms” or “s” (default).

New in version 0.16.

verbose

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

Returns
figinstance of matplotlib.figure.Figure

Figure containing the butterfly plots.

plot_field(self, surf_maps, time=None, time_label='t = %0.0f ms', n_jobs=1)[source]

Plot MEG/EEG fields on head surface and helmet in 3D.

Parameters
surf_mapslist

The surface mapping information obtained with make_field_map.

time

The time point at which the field map shall be displayed. If None, the average peak latency (across sensor types) is used.

time_labelstr

How to print info about the time instant visualized.

n_jobsint

Number of jobs to run in parallel.

Returns
figinstance of mayavi.mlab.Figure

The mayavi figure.

plot_image(self, picks=None, exclude='bads', unit=True, show=True, clim=None, xlim='tight', proj=False, units=None, scalings=None, titles=None, axes=None, cmap='RdBu_r', colorbar=True, mask=None, mask_style=None, mask_cmap='Greys', mask_alpha=0.25, time_unit='s', show_names=None, group_by=None)[source]

Plot evoked data as images.

Parameters
picks

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. This parameter can also be used to set the order the channels are shown in, as the channel image is sorted by the order of picks.

excludelist of str | ‘bads’

Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded.

unitbool

Scale plot with channel (SI) unit.

showbool

Show figure if True.

clim

clim for plots (after scaling has been applied). e.g. clim = dict(eeg=[-20, 20]) Valid keys are eeg, mag, grad, misc. If None, the clim parameter for each channel equals the pyplot default.

xlim‘tight’ | tuple | None

xlim for plots.

projbool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown.

units

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

scalings

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

titles

The titles associated with the channels. If None, defaults to dict(eeg='EEG', grad='Gradiometers', mag='Magnetometers').

axesinstance of Axes | list | dict | None

The axes to plot to. If list, the list must be a list of Axes of the same length as the number of channel types. If instance of Axes, there must be only one channel type plotted. 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.

cmapmatplotlib 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). Defaults to 'RdBu_r'.

colorbarbool

If True, plot a colorbar. Defaults to True.

New in version 0.16.

An array of booleans of the same shape as the data. Entries of the data that correspond to False in the mask are masked (see do_mask below). Useful for, e.g., masking for statistical significance.

New in version 0.16.

If mask is not None: if ‘contour’, a contour line is drawn around the masked areas (True in mask). If ‘mask’, entries not True in mask are shown transparently. If ‘both’, both a contour and transparency are used. If None, defaults to ‘both’ if mask is not None, and is ignored otherwise.

New in version 0.16.

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

The colormap chosen for masked parts of the image (see below), if mask is not None. If None, cmap is reused. Defaults to Greys. Not interactive. Otherwise, as cmap.

mask_alphafloat

A float between 0 and 1. If mask is not None, this sets the alpha level (degree of transparency) for the masked-out segments. I.e., if 0, masked-out segments are not visible at all. Defaults to .25.

New in version 0.16.

time_unitstr

The units for the time axis, can be “ms” or “s” (default).

New in version 0.16.

show_names

Determines if channel names should be plotted on the y axis. If False, no names are shown. If True, ticks are set automatically and the corresponding channel names are shown. If str, must be “auto” or “all”. If “all”, all channel names are shown. If “auto”, is set to False if picks is None; to True if picks is not None and fewer than 25 picks are shown; to “all” if picks is not None and contains fewer than 25 entries.

group_by

If a dict, the values must be picks, and axes must also be a dict with matching keys, or None. If axes is None, one figure and one axis will be created for each entry in group_by. Then, for each entry, the picked channels will be plotted to the corresponding axis. If titles are None, 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 None, all picked channels are plotted to the same axis.

Returns
figinstance of matplotlib.figure.Figure

Figure containing the images.

plot_joint(self, times='peaks', title='', picks=None, exclude='bads', show=True, ts_args=None, topomap_args=None)[source]

Plot evoked data as butterfly plot and add topomaps for time points.

Note

Axes to plot in can be passed by the user through ts_args or topomap_args. In that case both ts_args and topomap_args axes have to be used. Be aware that when the axes are provided, their position may be slightly modified.

Parameters
timesfloat | array of float | “auto” | “peaks”

The time point(s) to plot. If "auto", 5 evenly spaced topographies between the first and last time instant will be shown. If "peaks", finds time points automatically by checking for 3 local maxima in Global Field Power. Defaults to "peaks".

title

The title. If None, suppress printing channel type title. If an empty string, a default title is created. Defaults to ‘’. If custom axes are passed make sure to set title=None, otherwise some of your axes may be removed during placement of the title axis.

picks

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.

excludeNone | list of str | ‘bads’

Channels names to exclude from being shown. If 'bads', the bad channels are excluded. Defaults to None.

showbool

Show figure if True. Defaults to True.

ts_args

A dict of kwargs that are forwarded to mne.Evoked.plot() to style the butterfly plot. If they are not in this dict, the following defaults are passed: spatial_colors=True, zorder='std'. show and exclude are illegal. If None, no customizable arguments will be passed. Defaults to None.

topomap_args

A dict of kwargs that are forwarded to mne.Evoked.plot_topomap() to style the topomaps. If it is not in this dict, outlines='skirt' will be passed. show, times, colorbar are illegal. If None, no customizable arguments will be passed. Defaults to None.

Returns
figinstance of matplotlib.figure.Figure | list

The figure object containing the plot. If evoked has multiple channel types, a list of figures, one for each channel type, is returned.

Notes

New in version 0.12.0.

plot_projs_topomap(self, 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

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.

axesinstance 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
figinstance of Figure

Figure distributing one image per channel across sensor topography.

plot_sensors(self, 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
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_type

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

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

show_names

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 version 0.13.0.

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 version 0.14.0.

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 version 0.13.0.

blockbool

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

New in version 0.13.0.

showbool

Show figure if True. Defaults to True.

Returns
figinstance of Figure

Figure containing the sensor topography.

selectionlist

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(self, layout=None, layout_scale=0.945, color=None, border='none', ylim=None, scalings=None, title=None, proj=False, vline=[0.0], fig_background=None, merge_grads=False, legend=True, axes=None, background_color='w', noise_cov=None, show=True)[source]

Plot 2D topography of evoked responses.

Clicking on the plot of an individual sensor opens a new figure showing the evoked response for the selected sensor.

Parameters
layoutinstance of Layout | None

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout is inferred from the data.

layout_scale: float

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

color

Everything matplotlib accepts to specify colors. If not list-like, the color specified will be repeated. If None, colors are automatically drawn.

borderstr

matplotlib borders style to be used for each sensor plot.

ylim

ylim for plots (after scaling has been applied). The value determines the upper and lower subplot limits. e.g. ylim = dict(eeg=[-20, 20]). Valid keys are eeg, mag, grad, misc. If None, the ylim parameter for each channel is determined by the maximum absolute peak.

scalings

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

titlestr

Title of the figure.

projbool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown.

vline

The values at which to show a vertical line.

fig_background

A background image for the figure. This must work with a call to plt.imshow. Defaults to None.

Whether to use RMS value of gradiometer pairs. Only works for Neuromag data. Defaults to False.

legend

If True, create a legend based on evoked.comment. If False, disable the legend. Otherwise, the legend is created and the parameter value is passed as the location parameter to the matplotlib legend call. It can be an integer (e.g. 0 corresponds to upper right corner of the plot), a string (e.g. ‘upper right’), or a tuple (x, y coordinates of the lower left corner of the legend in the axes coordinate system). See matplotlib documentation for more details.

axesinstance of matplotlib Axes | None

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

background_colorcolor

Background color. Typically ‘k’ (black) or ‘w’ (white; default).

New in version 0.15.0.

noise_covinstance of Covariance | str | None

Noise covariance used to whiten the data while plotting. Whitened data channel names are shown in italic. Can be a string to load a covariance from disk.

New in version 0.16.0.

showbool

Show figure if True.

Returns
figinstance of matplotlib.figure.Figure

Images of evoked responses at sensor locations

Notes

New in version 0.10.0.

plot_topomap(self, times='auto', ch_type=None, layout=None, vmin=None, vmax=None, cmap=None, sensors=True, colorbar=True, scalings=None, units=None, res=64, size=1, cbar_fmt='%3.1f', time_unit='s', time_format=None, proj=False, show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', average=None, head_pos=None, axes=None, extrapolate='box')[source]

Plot topographic maps of specific time points of evoked data.

Parameters
timesfloat | array of float | “auto” | “peaks” | “interactive”

The time point(s) to plot. If “auto”, the number of axes determines the amount of time point(s). If axes is also None, at most 10 topographies will be shown with a regular time spacing between the first and last time instant. If “peaks”, finds time points automatically by checking for local maxima in global field power. If “interactive”, the time can be set interactively at run-time by using a slider.

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 channels are chosen in the order given above.

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.

vmin

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

vmax

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.

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 (zoom). The mouse scroll can also be used to adjust 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).

Warning

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

sensors

Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True (default), circles will be used.

colorbar

Plot a colorbar in the rightmost column of the figure. None (default) is the same as True, but emits a warning if custom axes are provided to remind the user that the colorbar will occupy the last matplotlib.axes.Axes instance.

scalings

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

units

The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.

resint

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

sizefloat

Side length per topomap in inches.

cbar_fmtstr

String format for colorbar values.

time_unitstr

The units for the time axis, can be “ms” or “s” (default).

New in version 0.16.

time_format

String format for topomap values. Defaults (None) to “%01d ms” if time_unit='ms', “%0.3f s” if time_unit='s', and “%g” otherwise.

projbool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be show.

showbool

Show figure if True.

show_names

If True, show channel names on top of the map. If a callable is passed, 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 significant sensors will be shown.

title

Title. If None (default), no title is displayed.

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

The channels to be marked as significant at a given time point. Indices set to True will be considered. Defaults to None.

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

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

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’.

contours

The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. The values are in uV for EEG, fT for magnetometers and fT/m for gradiometers. If colorbar=True, the ticks in colorbar correspond to the contour levels. Defaults to 6.

image_interpstr

The image interpolation to be used. All matplotlib options are accepted.

average

The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging.

If None (default), the sensors are positioned such that they span the head circle. If dict, can have entries ‘center’ (tuple) and ‘scale’ (tuple) for what the center and scale of the head should be relative to the electrode locations.

axesinstance of Axes | list | None

The axes to plot to. If list, the list must be a list of Axes of the same length as times (unless times is None). If instance of Axes, times must be a float or a list of one float. Defaults to None.

extrapolatestr

If ‘box’ (default) 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. If ‘head’ extrapolate to the edges of the head circle (or to the edges of the skirt if outlines='skirt'). If ‘local’ extrapolate only to nearby points (approximately to points closer than median inter-electrode distance).

New in version 0.18.

Returns
figinstance of matplotlib.figure.Figure

The figure.

plot_white(self, noise_cov, show=True, rank=None, time_unit='s', verbose=None)[source]

Plot whitened evoked response.

Plots the whitened evoked response and the whitened GFP as described in [1]. This function is especially useful for investigating noise covariance properties to determine if data are properly whitened (e.g., achieving expected values in line with model assumptions, see Notes below).

Parameters
noise_covlist | instance of Covariance | str

The noise covariance. Can be a string to load a covariance from disk.

showbool

Show figure if True.

rankNone | dict | ‘info’ | ‘full’

This controls the rank computation that can be read from the measurement info or estimated from the data. See Notes of mne.compute_rank() for details.The default is None.

time_unitstr

The units for the time axis, can be “ms” or “s” (default).

New in version 0.16.

verbose

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

Returns
figinstance of matplotlib.figure.Figure

The figure object containing the plot.

Notes

If baseline signals match the assumption of Gaussian white noise, values should be centered at 0, and be within 2 standard deviations (±1.96) for 95% of the time points. For the global field power (GFP), we expect it to fluctuate around a value of 1.

If one single covariance object is passed, the GFP panel (bottom) will depict different sensor types. If multiple covariance objects are passed as a list, the left column will display the whitened evoked responses for each channel based on the whitener from the noise covariance that has the highest log-likelihood. The left column will depict the whitened GFPs based on each estimator separately for each sensor type. Instead of numbers of channels the GFP display shows the estimated rank. Note. The rank estimation will be printed by the logger (if verbose=True) for each noise covariance estimator that is passed.

References

1

Engemann D. and Gramfort A. (2015) Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals, vol. 108, 328-342, NeuroImage.

proj

Whether or not projections are active.

rename_channels(self, mapping)[source]

Rename channels.

Parameters
mapping

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.

reorder_channels(self, ch_names)[source]

Reorder channels.

Parameters
ch_nameslist

The desired channel order.

Returns
instinstance of Raw, Epochs, or Evoked

The modified instance.

Notes

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

New in version 0.16.0.

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

Resample data.

Note

Parameters
sfreqfloat

New sample rate to use.

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

Frequency-domain window to use in resampling. See scipy.signal.resample().

n_jobs

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

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

New in version 0.15.

verbose

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

Returns
instinstance of Epochs or Evoked

The resampled 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(self, fname)[source]

Save dataset to file.

Parameters
fnamestr

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

Notes

To write multiple conditions into a single file, use mne.write_evokeds().

savgol_filter(self, h_freq, verbose=None)[source]

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 [1] 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.

verbose

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

Returns
instinstance 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

1

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')  # doctest:+SKIP
>>> evoked = mne.read_evokeds(evoked_fname, baseline=(None, 0))[0]  # doctest:+SKIP
>>> evoked.savgol_filter(10.)  # low-pass at around 10 Hz # doctest:+SKIP
>>> evoked.plot()  # doctest:+SKIP

set_channel_types(self, 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
mappingdict

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

Notes

New in version 0.9.0.

set_eeg_reference(self, ref_channels='average', projection=False, 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'].

Parameters
ref_channels

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.

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).

verbose

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

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.

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

Set EEG sensor configuration and head digitization.

Parameters
montageinstance of Montage | instance of DigMontage | str | None

The montage to use (None removes any location information).

set_digbool

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

verbose

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

Notes

Operates in place.

New in version 0.9.0.

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

Shift time scale in evoked data.

Parameters
tshiftfloat

The amount of time shift to be applied if relative is True else the first time point. When relative is True, positive value of tshift moves the data forward while negative tshift moves it backward.

relativebool

If true, move the time backwards or forwards by specified amount. Else, set the starting time point to the value of tshift.

Notes

Maximum accuracy of time shift is 1 / evoked.info[‘sfreq’]

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

Convert time to indices.

Parameters
timeslist-like | float | int

List of numbers or a number representing points in time.

use_roundingbool

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

Returns
indexndarray

Indices corresponding to the times supplied.

to_data_frame(self, picks=None, index=None, scaling_time=1000.0, scalings=None, copy=True, start=None, stop=None, long_format=False)[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

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.

index

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_timefloat

Scaling to be applied to time units.

scalings

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

copybool

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

start

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

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.

long_formatbool

If True, the dataframe is returned in long format where each row is one observation of the signal at a unique coordinate of channels, time points, epochs and conditions. The number of factors depends on the data container. For convenience, a ch_type column is added when using this option that will facilitate subsetting the resulting dataframe. Defaults to False.

Returns
dfinstance of pandas.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.