mne.Evoked #

Plotting topographic maps of evoked data

add_reference_channels(ref_channels)[source]#

Add reference channels to data that consists of all zeros.

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

Parameters:

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

Returns:

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

animate_topomap(ch_type=None, times=None, frame_rate=None, butterfly=False, blit=True, show=True, time_unit='s', sphere=None, *, image_interp='cubic', extrapolate='auto', verbose=None)[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_typestr | None

Channel type to plot. Accepted data types: ‘mag’, ‘grad’, ‘eeg’, ‘hbo’, ‘hbr’, ‘fnirs_cw_amplitude’, ‘fnirs_fd_ac_amplitude’, ‘fnirs_fd_phase’, and ‘fnirs_od’. If None, first available channel type from the above list is used. Defaults to None.

timesarray of float | None

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

frame_rateint | None

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.

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

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.

image_interpstr

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

extrapolatestr

Options:

'box'
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local' (default for MEG sensors)
Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head' (default for non-MEG sensors)
Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.

Changed in version 0.21:

The default was changed to 'local' for MEG sensors.
'local' was changed to use a convex hull mask
'head' was changed to extrapolate out to the clipping circle.

New in version 0.22.

verbosebool | str | int | None

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

Returns:

figinstance of matplotlib.figure.Figure: The figure.
animinstance of matplotlib.animation.FuncAnimation: Animation of the topomap.

Notes

New in version 0.12.0.

Examples using animate_topomap:

anonymize(daysback=None, keep_his=False, verbose=None)[source]#

Anonymize measurement information in place.

Parameters:

daysbackint | None: Number of days to subtract from all dates. If None (default), the acquisition date, info['meas_date'], will be set to January 1ˢᵗ, 2000. This parameter is ignored if info['meas_date'] is None (i.e., no acquisition date has been set).
keep_hisbool: If True, his_id of subject_info will not be overwritten. Defaults to False.

Warning

This could mean that info is not fully anonymized. Use with caution.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

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

Notes

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

meas_date, file_id, meas_id
A default value, or as specified by daysback.
subject_info
Default values, except for ‘birthday’ which is adjusted to maintain the subject age.
experimenter, proj_name, description
Default strings.
utc_offset
None.
proj_id
Zeros.
proc_history
Dates use the meas_date logic, and experimenter a default string.
helium_info, device_info
Dates use the meas_date logic, meta info uses defaults.

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

Operates in place.

New in version 0.13.0.

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

Baseline correct evoked data.

Parameters:

baselineNone | tuple of length 2

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

Note

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

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

Calculate the mean signal of the baseline period.
Subtract this mean from the entire Evoked.

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

verbosebool | str | int | None

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

Returns:

evokedinstance of Evoked: The baseline-corrected Evoked object.

Notes

Baseline correction can be done multiple times.

New in version 0.13.0.

Examples using apply_baseline:

Repairing artifacts with ICA

apply_function(fun, picks=None, dtype=None, n_jobs=None, verbose=None, **kwargs)[source]#

Apply a function to a subset of channels.

The function fun is applied to the channels defined in picks. The evoked object’s data is modified in-place. If the function returns a different data type (e.g. numpy.complex128) it must be specified using the dtype parameter, which causes the data type of all the data to change (even if the function is only applied to channels in picks).

Note

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

Note

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

Parameters:

funcallable(): A function to be applied to the channels. The first argument of fun has to be a timeseries (numpy.ndarray). The function must operate on an array of shape (n_times,) because it will apply channel-wise. The function must return an ndarray shaped like its input.
picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
dtypenumpy.dtype: Data type to use after applying the function. If None (default) the data type is not modified.
n_jobsint | None: The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.
**kwargsdict: Additional keyword arguments to pass to fun.

Returns:

selfinstance of Evoked: The evoked object with transformed data.

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

Compute analytic signal or envelope for a subset of channels.

Parameters:

picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
envelopebool: Compute the envelope signal of each channel. Default False. See Notes.
n_jobsint | None: The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs.
n_fftint | None | str: Points to use in the FFT for Hilbert transformation. The signal will be padded with zeros before computing Hilbert, then cut back to original length. If None, n == self.n_times. If ‘auto’, the next highest fast FFT length will be use.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

selfinstance of Raw, Epochs, 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(verbose=None)[source]#

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

Parameters:

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

Returns:

selfinstance of Raw | Epochs | Evoked: The instance.

Notes

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

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

Examples using apply_proj:

Auto-generating Epochs metadata

as_type(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

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

New in version 0.9.0.

Examples using as_type:

Remap MEG channel types

property ch_names#: Channel names.

property compensation_grade#: The current gradient compensation grade.

copy()[source]#

Copy the instance of evoked.

Returns:

evokedinstance of Evoked: A copy of the object.

Examples using copy:

Computing a covariance matrix

Source localization with equivalent current dipole (ECD) fit

4D Neuroimaging/BTi phantom dataset tutorial

Working with ECoG data

Interpolate bad channels for MEG/EEG channels

Plotting topographic arrowmaps of evoked data

Compute source power spectral density (PSD) of VectorView and OPM data

Computing source timecourses with an XFit-like multi-dipole model

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Computing source space SNR

Optically pumped magnetometer (OPM) data

HF-SEF dataset

crop(tmin=None, tmax=None, include_tmax=True, verbose=None)[source]#

Crop data to a given time interval.

Parameters:

tminfloat | None: Start time of selection in seconds.
tmaxfloat | None: End time of selection in seconds.
include_tmaxbool: If True (default), include tmax. If False, exclude tmax (similar to how Python indexing typically works).

New in version 0.19.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of Raw, Epochs, Evoked, AverageTFR, or SourceEstimate: The cropped time-series object, modified in-place.

Notes

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

Examples using crop:

Source localization with equivalent current dipole (ECD) fit

Computing various MNE solutions

Visualize source time courses (stcs)

Brainstorm CTF phantom dataset tutorial

Non-parametric 1 sample cluster statistic on single trial power

Source localization with a custom inverse solver

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Compute Rap-Music on evoked data

Compute MxNE with time-frequency sparse prior

property data#: The data matrix.

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

Decimate the time-series data.

Parameters:

decimint: Factor by which to subsample the data.

Warning

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

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

Returns:

instMNE-object: The decimated object.

See also

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

Notes

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

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

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

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

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

New in version 0.10.0.

References

Examples using decimate:

Visualize source time courses (stcs)

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:

idxint | list of int | str: Index of the projector to remove. Can also be “all” (default) to remove all projectors.

Returns:

selfinstance of Raw | Epochs | Evoked: The instance.

Examples using del_proj:

detrend(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.
picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

Returns:

evokedinstance of Evoked: The detrended evoked object.

drop_channels(ch_names)[source]#

Drop channel(s).

Parameters:

ch_namesiterable or str: Iterable (e.g. list) of channel name(s) or channel name to remove.

Returns:

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

See also

reorder_channels
pick_channels
pick_types

Notes

New in version 0.9.0.

Examples using drop_channels:

Compute source power estimate by projecting the covariance with MNE

export(fname, fmt='auto', *, overwrite=False, verbose=None)[source]#

Export Evoked to external formats.

Supported formats:

MFF (.mff, uses mne.export.export_evokeds_mff())

Warning

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

Parameters:

fnamestr: Name of the output file.
fmt‘auto’ | ‘mff’: Format of the export. Defaults to 'auto', which will infer the format from the filename extension. See supported formats above for more information.
overwritebool: If True (default False), overwrite the destination file if it exists.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Notes

New in version 1.1.

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

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

Filter a subset of channels.

Parameters:

l_freqfloat | None

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

h_freqfloat | None

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

picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

filter_lengthstr | int

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

‘auto’ (default): The filter length is chosen based on the size of the transition regions (6.6 times the reciprocal of the shortest transition band for fir_window=’hamming’ and fir_design=”firwin2”, and half that for “firwin”).
str: A human-readable time in units of “s” or “ms” (e.g., “10s” or “5500ms”) will be converted to that number of samples if phase="zero", or the shortest power-of-two length at least that duration for phase="zero-double".
int: Specified length in samples. For fir_design=”firwin”, this should not be used.

l_trans_bandwidthfloat | str

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

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

Only used for method='fir'.

h_trans_bandwidthfloat | str

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

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

Only used for method='fir'.

n_jobsint | str

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

methodstr

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

iir_paramsdict | None

Dictionary of parameters to use for IIR filtering. If iir_params 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.

skip_by_annotationstr | list of str

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

New in version 0.16..

padstr

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

Only used for method='fir'.

verbosebool | str | int | None

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

Returns:

instinstance of Epochs, Evoked, or Raw: The filtered data.

See also

mne.filter.create_filter
mne.Evoked.savgol_filter
mne.io.Raw.notch_filter
mne.io.Raw.resample
mne.filter.create_filter
mne.filter.filter_data
mne.filter.construct_iir_filter

Notes

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

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

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

l_freq < h_freq: band-pass filter

l_freq > h_freq: band-stop filter

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

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

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

Note

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

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

New in version 0.15.

Examples using filter:

Working with CTF data: the Brainstorm auditory dataset

Regression-based baseline correction

HF-SEF dataset

get_channel_types(picks=None, unique=False, only_data_chs=False)[source]#

Get a list of channel type for each channel.

Parameters:

picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
uniquebool: Whether to return only unique channel types. Default is False.
only_data_chsbool: Whether to ignore non-data channels. Default is False.

Returns:

channel_typeslist: The channel types.

get_data(picks=None, units=None, tmin=None, tmax=None)[source]#

Get evoked data as 2D array.

Parameters:

picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
unitsstr | dict | None: Specify the unit(s) that the data should be returned in. If None (default), the data is returned in the channel-type-specific default units, which are SI units (see Internal representation (units) and data channels). If a string, must be a sub-multiple of SI units that will be used to scale the data from all channels of the type associated with that unit. This only works if the data contains one channel type that has a unit (unitless channel types are left unchanged). For example if there are only EEG and STIM channels, units='uV' will scale EEG channels to micro-Volts while STIM channels will be unchanged. Finally, if a dictionary is provided, keys must be channel types, and values must be units to scale the data of that channel type to. For example dict(grad='fT/cm', mag='fT') will scale the corresponding types accordingly, but all other channel types will remain in their channel-type-specific default unit.
tminfloat | None: Start time of data to get in seconds.
tmaxfloat | None: End time of data to get in seconds.

Returns:

datandarray, shape (n_channels, n_times): A view on evoked data.

Notes

New in version 0.24.

Examples using get_data:

get_montage()[source]#

Get a DigMontage from instance.

Returns:

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

get_peak(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_typestr | None: The channel type to use. Defaults to None. If more than one channel type is present in the data, this value must be provided.
tminfloat | None: The minimum point in time to be considered for peak getting. If None (default), the beginning of the data is used.
tmaxfloat | None: 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.
merge_gradsbool: 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.
latencyfloat | int: 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.

Examples using get_peak:

Make figures more publication ready

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

Interpolate bad MEG and EEG channels.

Operates in place.

Parameters:

reset_badsbool

If True, remove the bads from info.

modestr

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

originarray-like, shape (3,) | str

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

New in version 0.17.

methoddict

Method to use for each channel type. Currently only the key “eeg” has multiple options:

"spline" (default)
Use spherical spline interpolation.
"MNE"
Use minimum-norm projection to a sphere and back. This is the method used for MEG channels.

The value for “meg” is “MNE”, and the value for “fnirs” is “nearest”. The default (None) is thus an alias for:

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

New in version 0.21.

excludelist | tuple

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

verbosebool | str | int | None

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

Returns:

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

Notes

New in version 0.9.0.

property kind#: The data kind.

pick(picks, exclude=(), *, verbose=None)[source]#

Pick a subset of channels.

Parameters:

picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
excludelist | str: Set of channels to exclude, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”).
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

New in version 0.24.0.

Returns:

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

Examples using pick:

Getting started with mne.Report

pick_channels(ch_names, ordered=False, *, verbose=None)[source]#

Pick some channels.

Parameters:

ch_nameslist: The list of channels to select.
orderedbool: If True (default False), ensure that the order of the channels in the modified instance matches the order of ch_names.

New in version 0.20.0.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

New in version 1.1.

Returns:

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

See also

drop_channels
pick_types
reorder_channels

Notes

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

New in version 0.9.0.

Examples using pick_channels:

Computing source timecourses with an XFit-like multi-dipole model

pick_types(meg=False, eeg=False, stim=False, eog=False, ecg=False, emg=False, ref_meg='auto', misc=False, resp=False, chpi=False, exci=False, ias=False, syst=False, seeg=False, dipole=False, gof=False, bio=False, ecog=False, fnirs=False, csd=False, dbs=False, include=(), exclude='bads', selection=None, verbose=None)[source]#

Pick some channels by type and names.

Parameters:

megbool | str: If True include MEG channels. If string it can be ‘mag’, ‘grad’, ‘planar1’ or ‘planar2’ to select only magnetometers, all gradiometers, or a specific type of gradiometer.
eegbool: If True include EEG channels.
stimbool: If True include stimulus channels.
eogbool: If True include EOG channels.
ecgbool: If True include ECG channels.
emgbool: If True include EMG channels.
ref_megbool | str: If True include CTF / 4D reference channels. If ‘auto’, reference channels are included if compensations are present and meg is not False. Can also be the string options for the meg parameter.
miscbool: If True include miscellaneous analog channels.
respbool: If True include respiratory channels.
chpibool: If True include continuous HPI coil channels.
excibool: Flux excitation channel used to be a stimulus channel.
iasbool: Internal Active Shielding data (maybe on Triux only).
systbool: System status channel information (on Triux systems only).
seegbool: Stereotactic EEG channels.
dipolebool: Dipole time course channels.
gofbool: Dipole goodness of fit channels.
biobool: Bio channels.
ecogbool: Electrocorticography channels.
fnirsbool | str: Functional near-infrared spectroscopy channels. If True include all fNIRS channels. If False (default) include none. If string it can be ‘hbo’ (to include channels measuring oxyhemoglobin) or ‘hbr’ (to include channels measuring deoxyhemoglobin).
csdbool: EEG-CSD channels.
dbsbool: Deep brain stimulation channels.
includelist of str: List of additional channels to include. If empty do not include any.
excludelist of str | str: List of channels to exclude. If ‘bads’ (default), exclude channels in info['bads'].
selectionlist of str: Restrict sensor channels (MEG, EEG) to this list of channel names.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

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

See also

pick_channels

Notes

New in version 0.9.0.

Examples using pick_types:

Overview of MEG/EEG analysis with MNE-Python

Source localization with equivalent current dipole (ECD) fit

Visualize source time courses (stcs)

Make figures more publication ready

Source localization with a custom inverse solver

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute Rap-Music on evoked data

Compute MxNE with time-frequency sparse prior

plot(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', sphere=None, *, highlight=None, 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:

picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

excludelist 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.

ylimdict | None

Y limits 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

X limits for plots.

projbool | ‘interactive’ | ‘reconstruct’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown. If ‘reconstruct’, projection vectors will be applied and then M/EEG data will be reconstructed via field mapping to reduce the signal bias caused by projection.

Changed in version 0.21: Support for ‘reconstruct’ was added.

hlinelist of float | None

The values at which to show an horizontal line.

unitsdict | None

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

scalingsdict | None

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

titlesdict | None

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 the global field power (GFP) or the root mean square (RMS) of the data. For MEG data, this will plot the RMS. For EEG, it plots GFP, i.e. the standard deviation of the signal across channels. The GFP is equivalent to the RMS of an average-referenced signal.

True
Plot GFP or RMS (for EEG and MEG, respectively) and traces for all channels.
'only'
Plot GFP or RMS (for EEG and MEG, respectively), and omit the traces for individual channels.

The color of the GFP/RMS trace will be green if spatial_colors=False, and black otherwise.

Changed in version 0.23: Plot GFP for EEG instead of RMS. Label RMS traces correctly as such.

window_titlestr | None

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.

zorderstr | callable()

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 “s” (default) or “ms”.

New in version 0.16.

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.

highlightarray-like of float, shape(2,) | array-like of float, shape (n, 2) | None

Segments of the data to highlight by means of a light-yellow background color. Can be used to put visual emphasis on certain time periods. The time periods must be specified as array-like objects in the form of (t_start, t_end) in the unit given by the time_unit parameter. Multiple time periods can be specified by passing an array-like object of individual time periods (e.g., for 3 time periods, the shape of the passed object would be (3, 2). If None, no highlighting is applied.

New in version 1.1.

verbosebool | str | int | None

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

Returns:

figinstance of matplotlib.figure.Figure: Figure containing the butterfly plots.

See also

mne.viz.plot_evoked_white

Examples using plot:

Working with CTF data: the Brainstorm auditory dataset

Overview of artifact detection

Regression-based baseline correction

Auto-generating Epochs metadata

Source localization with MNE, dSPM, sLORETA, and eLORETA

Plotting whitened data

Brainstorm Elekta phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Creating MNE-Python data structures from scratch

Generate simulated evoked data

Simulate raw data using subject anatomy

Generate simulated source data

Define target events based on time lag, plot evoked response

Interpolate bad channels for MEG/EEG channels

Shifting time-scale in evoked data

Whitening evoked data with a noise covariance

Make figures more publication ready

Regression on continuous data (rER[P/F])

Analysis of evoked response using ICA and PCA reduction techniques

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute source power estimate by projecting the covariance with MNE

Compute Rap-Music on evoked data

Compute MxNE with time-frequency sparse prior

Optically pumped magnetometer (OPM) data

plot_field(surf_maps, time=None, time_label='t = %0.0f ms', n_jobs=None, fig=None, vmax=None, n_contours=21, *, interaction='terrain', verbose=None)[source]#

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

Parameters:

surf_mapslist: The surface mapping information obtained with make_field_map.
timefloat | None: The time point at which the field map shall be displayed. If None, the average peak latency (across sensor types) is used.
time_labelstr | None: How to print info about the time instant visualized.
n_jobsint | None: The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs.
figinstance of Figure3D | None: If None (default), a new figure will be created, otherwise it will plot into the given figure.

New in version 0.20.
vmaxfloat | None: Maximum intensity. Can be None to use the max(abs(data)).

New in version 0.21.
n_contoursint: The number of contours.

New in version 0.21.
interaction‘trackball’ | ‘terrain’: How interactions with the scene via an input device (e.g., mouse or trackpad) modify the camera position. If 'terrain', one axis is fixed, enabling “turntable-style” rotations. If 'trackball', movement along all axes is possible, which provides more freedom of movement, but you may incidentally perform unintentional rotations along some axes. Defaults to 'terrain'.

New in version 1.1.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figinstance of Figure3D: The figure.

Examples using plot_field:

Plot the MNE brain and helmet

plot_image(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, sphere=None)[source]#

Plot evoked data as images.

Parameters:

picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided. 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.

climdict | None

Color limits 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

X limits 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.

unitsdict | None

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

scalingsdict | None

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

titlesdict | None

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.

maskndarray | None

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.

mask_styleNone | ‘both’ | ‘contour’ | ‘mask’

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_namesbool | ‘auto’ | ‘all’

Determines if channel names should be plotted on the y axis. If False, no names are shown. If True, ticks are set automatically by matplotlib and the corresponding channel names are shown. If “all”, all channel names are shown. If “auto”, is set to False if picks is None, to True if picks contains 25 or more entries, or to “all” if picks contains fewer than 25 entries.

group_byNone | dict

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.

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.

Returns:

figinstance of matplotlib.figure.Figure: Figure containing the images.

Examples using plot_image:

Visualising statistical significance thresholds on EEG data

Analysing continuous features with binning and regression in sensor space

plot_joint(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".
titlestr | None: 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.
picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
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_argsNone | dict: 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_argsNone | dict: 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.

Examples using plot_joint:

Overview of MEG/EEG analysis with MNE-Python

Overview of artifact detection

Repairing artifacts with ICA

Source reconstruction using an LCMV beamformer

EEG source localization given electrode locations on an MRI

Visualising statistical significance thresholds on EEG data

Decoding (MVPA)

Transform EEG data using current source density (CSD)

Single trial linear regression analysis with the LIMO dataset

plot_projs_topomap(ch_type=None, cmap=None, sensors=True, colorbar=False, res=64, size=1, show=True, outlines='head', contours=6, image_interp='cubic', axes=None, vlim=(None, None), sphere=None, extrapolate='auto', border='mean')[source]#

Plot SSP vector.

Parameters:

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.

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 (only works if colorbar=True) 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).

sensorsbool | str

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

colorbarbool

Plot a colorbar.

resint

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

sizescalar

Side length of the topomaps in inches (only applies when plotting multiple topomaps at a time).

showbool

Show figure if True.

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. 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’.

contoursint | array of float

image_interpstr

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.

vlimtuple of length 2 | ‘joint’

Colormap limits to use. If tuple, specifies the lower and upper bounds of the colormap (in that order); providing None for either of these will set the corresponding boundary at the min/max of the data (separately for each projector). The keyword value 'joint' will compute the colormap limits jointly across all provided projectors of the same channel type, using the min/max of the projector data. If vlim is 'joint', info must not be None. Defaults to (None, None).

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.

image_interpstr

extrapolatestr

Options:

'box'
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local' (default for MEG sensors)
Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head' (default for non-MEG sensors)
Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.

Changed in version 0.21:

The default was changed to 'local' for MEG sensors.
'local' was changed to use a convex hull mask
'head' was changed to extrapolate out to the clipping circle.

New in version 0.20.

borderfloat | ‘mean’

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

New in version 0.20.

Returns:

figinstance of Figure: Figure distributing one image per channel across sensor topography.

Examples using plot_projs_topomap:

plot_sensors(kind='topomap', ch_type=None, title=None, show_names=False, ch_groups=None, to_sphere=True, axes=None, block=False, show=True, sphere=None, verbose=None)[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_typeNone | str: The channel type to plot. Available options ‘mag’, ‘grad’, ‘eeg’, ‘seeg’, ‘dbs’, ‘ecog’, ‘all’. If 'all', all the available mag, grad, eeg, seeg, dbs, and ecog channels are plotted. If None (default), then channels are chosen in the order given above.
titlestr | None: Title for the figure. If None (default), equals to 'Sensor positions (%s)' % ch_type.
show_namesbool | array of str: Whether to display all channel names. If an array, only the channel names in the array are shown. Defaults to False.
ch_groups‘position’ | array of shape (n_ch_groups, n_picks) | None: Channel groups for coloring the sensors. If None (default), default coloring scheme is used. If ‘position’, the sensors are divided into 8 regions. See order kwarg of mne.viz.plot_raw(). If array, the channels are divided by picks given in the array.

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

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figinstance of Figure: Figure containing the sensor topography.
selectionlist: A list of selected channels. Only returned if kind=='select'.

See also

mne.viz.plot_layout

Notes

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

New in version 0.12.0.

plot_topo(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, exclude='bads', 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_scalefloat: Scaling factor for adjusting the relative size of the layout on the canvas.
colorlist of color | color | None: 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.
ylimdict | None: Y limits 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 type is determined by the minimum and maximum peak.
scalingsdict | None: 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.
vlinelist of float | None: The values at which to show a vertical line.
fig_backgroundNone | ndarray: A background image for the figure. This must work with a call to plt.imshow. Defaults to None.
merge_gradsbool: Whether to use RMS value of gradiometer pairs. Only works for Neuromag data. Defaults to False.
legendbool | int | str | tuple: 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.
excludelist of str | ‘bads’: Channels names to exclude from the plot. If ‘bads’, the bad channels are excluded. By default, exclude is set to ‘bads’.
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.

Examples using plot_topo:

Overview of MEG/EEG analysis with MNE-Python

Plotting whitened data

plot_topomap(times='auto', ch_type=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='cubic', average=None, axes=None, extrapolate='auto', sphere=None, border='mean', nrows=1, ncols='auto')[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.

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.

vmin, vmaxfloat | callable() | None

Lower and upper bounds of the colormap, in the same units as the data. If vmin and vmax are both None, they are set at ± the maximum absolute value of the data (yielding a colormap with midpoint at 0). If only one of vmin, vmax is None, will use min(data) or max(data), respectively. If callable, should accept a NumPy array of data and return a float.

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.

sensorsbool | str

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.

colorbarbool

Plot a colorbar in the rightmost column of the figure.

scalingsdict | float | None

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

unitsdict | str | None

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_formatstr | None

String format for topomap values. Defaults (None) to “%01d ms” if time_unit='ms', “%0.3f s” if time_unit='s', and “%g” otherwise. Can be an empty string to omit the time label.

projbool | ‘interactive’ | ‘reconstruct’

Changed in version 0.21: Support for ‘reconstruct’ was added.

showbool

Show the figure if True.

show_namesbool | callable()

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.

titlestr | None

The title of the generated figure. If None (default), no title is displayed.

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

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

mask_paramsdict | None

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

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

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

contoursint | array of float

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 µV 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

averagefloat | array-like of float, shape (n_times,) | None

The time window (in seconds) around a given time point to be used for averaging. For example, 0.2 would translate into a time window that starts 0.1 s before and ends 0.1 s after the given time point. If the time window exceeds the duration of the data, it will be clipped. Different time windows (one per time point) can be provided by passing an array-like object (e.g., [0.1, 0.2, 0.3]). If None (default), no averaging will take place.

Changed in version 1.1: Support for array-like input.

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

Options:

'box'
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local' (default for MEG sensors)
Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head' (default for non-MEG sensors)
Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.

Changed in version 0.21:

The default was changed to 'local' for MEG sensors.
'local' was changed to use a convex hull mask
'head' was changed to extrapolate out to the clipping circle.

New in version 0.18.

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.

borderfloat | ‘mean’

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

New in version 0.20.

nrowsint | ‘auto’

The number of rows of topographies to plot. Defaults to 1. If ‘auto’, obtains the number of rows depending on the amount of times to plot and the number of cols. Not valid when times == ‘interactive’.

New in version 0.20.

ncolsint | ‘auto’

The number of columns of topographies to plot. If ‘auto’ (default), obtains the number of columns depending on the amount of times to plot and the number of rows. Not valid when times == ‘interactive’.

New in version 0.20.

Returns:

figinstance of matplotlib.figure.Figure: The figure.

Notes

When existing axes are provided and colorbar=True, note that the colorbar scale will only accurately reflect topomaps that are generated in the same call as the colorbar. Note also that the colorbar will not be resized automatically when axes are provided; use Matplotlib’s axes.set_position() method or gridspec interface to adjust the colorbar size yourself.

Examples using plot_topomap:

Working with CTF data: the Brainstorm auditory dataset

Overview of artifact detection

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Regression-based baseline correction

Auto-generating Epochs metadata

Source localization with equivalent current dipole (ECD) fit

Source localization with MNE, dSPM, sLORETA, and eLORETA

Spatiotemporal permutation F-test on full sensor data

Transform EEG data using current source density (CSD)

Maxwell filter data with movement compensation

Remap MEG channel types

Plotting topographic maps of evoked data

Permutation T-test on sensor data

XDAWN Decoding From EEG data

Compute effect-matched-spatial filtering (EMS)

Linear classifier on sensor data with plot patterns and filters

Compute Spectro-Spatial Decomposition (SSD) spatial filters

Compute source power estimate by projecting the covariance with MNE

Computing a covariance matrix

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

Plot whitened evoked response.

Plots the whitened evoked response and the whitened GFP as described in [3]. 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 | path-like

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

showbool

Show figure if True.

rankNone | ‘info’ | ‘full’ | dict

This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).

None

The rank will be estimated from the data after proper scaling of different channel types.

'info'

The rank is inferred from info. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 66.

'full'

The rank is assumed to be full, i.e. equal to the number of good channels. If a Covariance is passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.

dict

Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.

This parameter must be a dictionary whose keys correspond to channel types in the data (e.g. 'meg', 'mag', 'grad', 'eeg'), and whose values are integers representing the respective ranks. For example, {'mag': 90, 'eeg': 45} will assume a rank of 90 and 45 for magnetometer data and EEG data, respectively.

The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.

The default is None.

time_unitstr

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

New in version 0.16.

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.

axeslist | None

List of axes to plot into.

New in version 0.21.0.

verbosebool | str | int | None

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

Returns:

figinstance of matplotlib.figure.Figure: The figure object containing the plot.

See also

mne.Evoked.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.

Examples using plot_white:

Plotting whitened data

Source localization with MNE, dSPM, sLORETA, and eLORETA

Generate simulated raw data

Whitening evoked data with a noise covariance

property proj#: Whether or not projections are active.

rename_channels(mapping, allow_duplicates=False, verbose=None)[source]#

Rename channels.

Parameters:

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

Changed in version 0.10.0: Support for a callable function.
allow_duplicatesbool: If True (default False), allow duplicates, which will automatically be renamed with -N at the end.

New in version 0.22.0.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of Raw | Epochs | Evoked: The instance (modified in place).

Changed in version 0.20: Return the instance.

Notes

New in version 0.9.0.

reorder_channels(ch_names)[source]#

Reorder channels.

Parameters:

ch_nameslist: The desired channel order.

Returns:

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

See also

drop_channels
pick_types
pick_channels

Notes

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

New in version 0.16.0.

Examples using reorder_channels:

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

Resample data.

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

Note

Data must be loaded.

Parameters:

sfreqfloat: New sample rate to use.
npadint | str: Amount to pad the start and end of the data. Can also be “auto” to use a padding that will result in a power-of-two size (can be much faster).
windowstr | tuple: Frequency-domain window to use in resampling. See scipy.signal.resample().
n_jobsint | str: Number of jobs to run in parallel. Can be ‘cuda’ if cupy is installed properly.
padstr: The type of padding to use. 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.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of Epochs or Evoked: The resampled object.

See also

mne.io.Raw.resample

Notes

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

Examples using resample:

Permutation t-test on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

save(fname, *, overwrite=False, verbose=None)[source]#

Save evoked data to a file.

Parameters:

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

Notes

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

Changed in version 0.23: Information on baseline correction will be stored with the data, and will be restored when reading again via mne.read_evokeds.

Examples using save:

Getting started with mne.Report

savgol_filter(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 [4] is done using polynomial fits instead of FIR/IIR filtering. This parameter is thus used to determine the length of the window over which a 5th-order polynomial smoothing is used.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of Epochs or Evoked: The object with the filtering applied.

See also

mne.io.Raw.filter

Notes

For Savitzky-Golay low-pass approximation, see:

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

New in version 0.9.0.

References

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, verbose=None)[source]#

Define the sensor type of channels.

Parameters:

mappingdict: A dictionary mapping a channel to a sensor type (str), e.g., {'EEG061': 'eog'}.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of Raw | Epochs | Evoked: The instance (modified in place).

Changed in version 0.20: Return the instance.

Notes

The following sensor types are accepted:

ecg, eeg, emg, eog, exci, ias, misc, resp, seeg, dbs, stim, syst, ecog, hbo, hbr, fnirs_cw_amplitude, fnirs_fd_ac_amplitude, fnirs_fd_phase, fnirs_od

New in version 0.9.0.

set_eeg_reference(ref_channels='average', projection=False, ch_type='auto', forward=None, verbose=None)[source]#

Specify which reference to use for EEG data.

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

Parameters:

ref_channelslist of str | str

Can be:

The name(s) of the channel(s) used to construct the reference.
'average' to apply an average reference (default)
'REST' to use the Reference Electrode Standardization Technique infinity reference [5].
An empty list, in which case MNE will not attempt any re-referencing of the data

projectionbool

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

ch_typelist of str | str

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

New in version 0.19.

forwardinstance of Forward | None

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

New in version 0.21.

verbosebool | str | int | None

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

Returns:

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

See also

mne.set_bipolar_reference: Convenience function for creating bipolar references.

Notes

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

Average reference:
A new virtual reference electrode is created by averaging the current EEG signal by setting ref_channels='average'. Bad EEG channels are automatically excluded if they are properly set in info['bads'].
A single electrode:
Set ref_channels to a list containing the name of the channel that will act as the new reference, for example ref_channels=['Cz'].
The mean of multiple electrodes:
A new virtual reference electrode is created by computing the average of the current EEG signal recorded from two or more selected channels. Set ref_channels to a list of channel names, indicating which channels to use. For example, to apply an average mastoid reference, when using the 10-20 naming scheme, set ref_channels=['M1', 'M2'].
REST
The given EEG electrodes are referenced to a point at infinity using the lead fields in forward, which helps standardize the signals.

If a reference is requested that is not the average reference, this function removes any pre-existing average reference projections.
During source localization, the EEG signal should have an average reference.
In order to apply a reference, the data must be preloaded. This is not necessary if ref_channels='average' and projection=True.
For an average or REST reference, bad EEG channels are automatically excluded if they are properly set in info['bads'].

New in version 0.9.0.

References

set_meas_date(meas_date)[source]#

Set the measurement start date.

Parameters:

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

Returns:

instinstance of Raw | Epochs | Evoked: The modified raw instance. Operates in place.

See also

mne.io.Raw.anonymize

Notes

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

New in version 0.20.

set_montage(montage, match_case=True, match_alias=False, on_missing='raise', verbose=None)[source]#

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

Parameters:

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

New in version 0.20.
match_aliasbool | dict: Whether to use a lookup table to match unrecognized channel location names to their known aliases. If True, uses the mapping in mne.io.constants.CHANNEL_LOC_ALIASES. If a dict is passed, it will be used instead, and should map from non-standard channel names to names in the specified montage. Default is False.

New in version 0.23.
on_missing‘raise’ | ‘warn’ | ‘ignore’: Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when channels have missing coordinates.

New in version 0.20.1.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of Raw | Epochs | Evoked: The instance, modified in-place.

See also

mne.channels.make_standard_montage
mne.channels.make_dig_montage
mne.channels.read_custom_montage

Notes

Warning

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

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

Shift time scale in epoched or evoked data.

Parameters:

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

Returns:

epochsMNE-object: The modified instance.

Notes

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

Examples using shift_time:

Shifting time-scale in evoked data

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

Convert time to indices.

Parameters:

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

Returns:

indexndarray: Indices corresponding to the times supplied.

Examples using time_as_index: