mne.time_frequency.BaseTFR

class mne.time_frequency.BaseTFR(inst, method, freqs, tmin, tmax, picks, proj, *, decim, n_jobs, reject_by_annotation=None, verbose=None, **method_kw)

Base class for RawTFR, EpochsTFR, and AverageTFR (for type checking only).

Note

This class should not be instantiated directly; it is provided in the public API only for type-checking purposes (e.g., isinstance(my_obj, BaseTFR)). To create TFR objects, use the .compute_tfr() methods on Raw, Epochs, or Evoked, or use the constructors listed below under “See Also”.

Parameters:

instinstance of Raw, Epochs, or Evoked

The data from which to compute the time-frequency representation.

method'morlet' | 'multitaper' | None

Spectrotemporal power estimation method. 'morlet' uses Morlet wavelets, 'multitaper' uses DPSS tapers [1]. None (the default) only works when using __setstate__ and will raise an error otherwise.

freqsarray_like | None

The frequencies at which to compute the power estimates. Must be an array of shape (n_freqs,). None (the default) only works when using __setstate__ and will raise an error otherwise.

tmin, tmaxfloat | None

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

picksstr | array_like | slice | None

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

projbool

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

decimint | slice

Decimation factor, applied after time-frequency decomposition.

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

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

Note

Decimation is done after convolutions and may create aliasing artifacts.

n_jobsint | None

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

reject_by_annotationbool

Whether to omit bad spans of data before spectrotemporal power estimation. If True, spans with annotations whose description begins with bad will be represented with np.nan in the time-frequency representation.

verbosebool | str | int | None

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

**method_kw

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

Attributes:

baseline

Start and end of the baseline period (in seconds).

ch_names

The channel names.

compensation_grade

The current gradient compensation grade.

data

The time-frequency-resolved power estimates.

freqs

The frequencies at which power estimates were computed.

method

The method used to compute the time-frequency power estimates.

sfreq

Sampling frequency of the data.

shape

Data shape.

times

The time points present in the data (in seconds).

tmax

Last time point.

tmin

First time point.

Methods

__add__(other)	Add two TFR instances.
__contains__(ch_type)	Check channel type membership.
__mul__(num)	Multiply a TFR instance by a scalar.
__sub__(other)	Subtract two TFR instances.
add_channels(add_list[, force_update_info])	Append new channels to the instance.
add_reference_channels(ref_channels)	Add reference channels to data that consists of all zeros.
apply_baseline(baseline[, mode, verbose])	Baseline correct the data.
copy()	Return copy of the TFR instance.
crop([tmin, tmax, fmin, fmax, include_tmax])	Crop data to a given time interval in place.
decimate(decim[, offset, verbose])	Decimate the time-series data.
drop_channels(ch_names[, on_missing])	Drop channel(s).
get_channel_types([picks, unique, only_data_chs])	Get a list of channel type for each channel.
get_data([picks, exclude, fmin, fmax, tmin, ...])	Get time-frequency data in NumPy array format.
pick(picks[, exclude, verbose])	Pick a subset of channels.
pick_channels(ch_names[, ordered, verbose])
pick_types([meg, eeg, stim, eog, ecg, emg, ...])
plot([picks, exclude, tmin, tmax, fmin, ...])	Plot TFRs as two-dimensional time-frequency images.
plot_joint(*[, timefreqs, picks, exclude, ...])	Plot TFRs as a two-dimensional image with topomap highlights.
plot_topo([picks, baseline, mode, tmin, ...])	Plot a TFR image for each channel in a sensor layout arrangement.
plot_topomap([tmin, tmax, fmin, fmax, ...])	Plot topographic maps of specific time-frequency intervals of TFR data.
reorder_channels(ch_names)	Reorder channels.
save(fname, *[, overwrite, verbose])	Save time-frequency data to disk (in HDF5 format).
shift_time(tshift[, relative])	Shift time scale in epoched or evoked data.
time_as_index(times[, use_rounding])	Convert time to indices.
to_data_frame([picks, index, long_format, ...])	Export data in tabular structure as a pandas DataFrame.

See also

mne.time_frequency.RawTFR

mne.time_frequency.RawTFRArray

mne.time_frequency.EpochsTFR

mne.time_frequency.EpochsTFRArray

mne.time_frequency.AverageTFR

mne.time_frequency.AverageTFRArray

__add__(other)

Add two TFR instances.

Parameters:

otherinstance of RawTFR | instance of EpochsTFR | instance of AverageTFR

The TFR instance to add. Must have the same type as self, and matching .times and .freqs attributes.

Returns:

tfrinstance of RawTFR | instance of EpochsTFR | instance of AverageTFR

A new TFR instance, of the same type as self.

__contains__(ch_type)

Check channel type membership.

Parameters:

ch_typestr

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

Returns:

inbool

Whether or not the instance contains the given channel type.

Examples

Channel type membership can be tested as:

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

__mul__(num)

Multiply a TFR instance by a scalar.

Parameters:

numint | float

The number to multiply by.

Returns:

tfrinstance of RawTFR | instance of EpochsTFR | instance of AverageTFR

A new TFR instance, of the same type as self.

__sub__(other)

Subtract two TFR instances.

Parameters:

otherinstance of RawTFR | instance of EpochsTFR | instance of AverageTFR

The TFR instance to subtract. Must have the same type as self, and matching .times and .freqs attributes.

Returns:

tfrinstance of RawTFR | instance of EpochsTFR | instance of AverageTFR

A new TFR instance, of the same type as self.

add_channels(add_list, force_update_info=False)

Append new channels to the instance.

Parameters:

add_listlist

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

force_update_infobool

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

New in v0.12.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

drop_channels

Notes

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

add_reference_channels(ref_channels)

Add reference channels to data that consists of all zeros.

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

Parameters:

ref_channelsstr | list of str

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

Returns:

instinstance of Raw | Epochs | Evoked

The modified instance.

apply_baseline(baseline, mode='mean', verbose=None)

Baseline correct the 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 data. If (None, None), the entire time interval is used.

Note

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

How baseline is computed is determined by the mode parameter.

mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’

Perform baseline correction by

• subtracting the mean of baseline values (‘mean’)

• dividing by the mean of baseline values (‘ratio’)

• dividing by the mean of baseline values and taking the log (‘logratio’)

• subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)

• subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’)

• dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’)

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 RawTFR, EpochsTFR, or AverageTFR

The modified instance.

Examples using apply_baseline:

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power

Spatiotemporal permutation F-test on full sensor data

Spatiotemporal permutation F-test on full sensor data

property baseline

Start and end of the baseline period (in seconds).

property ch_names

The channel names.

property compensation_grade

The current gradient compensation grade.

copy()

Return copy of the TFR instance.

Returns:

%(inst_tfr)s

A copy of the object.

crop(tmin=None, tmax=None, fmin=None, fmax=None, include_tmax=True)

Crop data to a given time interval in place.

Parameters:

tmin, tmaxfloat | None

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

fminfloat | None

Lowest frequency of selection in Hz.

New in v0.18.0.

fmaxfloat | None

Highest frequency of selection in Hz.

New in v0.18.0.

include_tmaxbool

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

New in v0.19.

Returns:

instinstance of RawTFR, EpochsTFR, or AverageTFR

The modified instance.

Examples using crop:

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power

Compute and visualize ERDS maps

Compute and visualize ERDS maps

Compute source level time-frequency timecourses using a DICS beamformer

Compute source level time-frequency timecourses using a DICS beamformer

property data

The time-frequency-resolved power estimates.

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

Decimate the time-series data.

Parameters:

decimint

Factor by which to subsample the data.

Warning

Low-pass filtering is not performed, this simply selects every Nth sample (where N is the value passed to decim), i.e., it compresses the signal (see Notes). If the data are not properly filtered, aliasing artifacts may occur. See Resampling and decimating data for more information.

offsetint

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

New in v0.12.

verbosebool | str | int | None

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

Returns:

instMNE-object

The decimated object.

See also

mne.Epochs.resample

mne.io.Raw.resample

Notes

For historical reasons, decim / “decimation” refers to simply subselecting samples from a given signal. This contrasts with the broader signal processing literature, where decimation is defined as (quoting [2], p. 172; which cites [3]):

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

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

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

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

New in v0.10.0.

References

[1]

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

[2]

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

[3]

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

drop_channels(ch_names, on_missing='raise')

Drop channel(s).

Parameters:

ch_namesiterable or str

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

on_missing‘raise’ | ‘warn’ | ‘ignore’

Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when entries in ch_names are not present in the raw instance.

New in v0.23.0.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

reorder_channels

pick_channels

pick_types

Notes

New in v0.9.0.

property freqs

The frequencies at which power estimates were computed.

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

Get a list of channel type for each channel.

Parameters:

picksstr | array_like | slice | None

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

uniquebool

Whether to return only unique channel types. Default is False.

only_data_chsbool

Whether to ignore non-data channels. Default is False.

Returns:

channel_typeslist

The channel types.

get_data(picks=None, exclude='bads', fmin=None, fmax=None, tmin=None, tmax=None, return_times=False, return_freqs=False)

Get time-frequency data in NumPy array format.

Parameters:

picksstr | array_like | slice | None

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

excludelist of str | ‘bads’

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

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is None which is equivalent to fmin=0, fmax=np.inf (spans all frequencies present in the data).

tmin, tmaxfloat | None

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

return_timesbool

Whether to return the time values for the requested time range. Default is False.

return_freqsbool

Whether to return the frequency bin values for the requested frequency range. Default is False.

Returns:

dataarray

The requested data in a NumPy array.

timesarray

The time values for the requested data range. Only returned if return_times is True.

freqsarray

The frequency values for the requested data range. Only returned if return_freqs is True.

Notes

Returns a copy of the underlying data (not a view).

property method

The method used to compute the time-frequency power estimates.

pick(picks, exclude=(), *, verbose=None)

Pick a subset of channels.

Parameters:

picksstr | array_like | slice | None

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

excludelist | str

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

verbosebool | str | int | None

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

New in v0.24.0.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

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

Warning

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

Pick some channels.

Parameters:

ch_nameslist

The list of channels to select.

orderedbool

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

New in v0.20.0.

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

verbosebool | str | int | None

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

New in v1.1.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

drop_channels

pick_types

reorder_channels

Notes

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

New in v0.9.0.

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

Warning

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

Pick some channels by type and names.

Parameters:

megbool | str

If True include MEG channels. If string it can be ‘mag’, ‘grad’, ‘planar1’ or ‘planar2’ to select only magnetometers, all gradiometers, or a specific type of gradiometer.

eegbool

If True include EEG channels.

stimbool

If True include stimulus channels.

eogbool

If True include EOG channels.

ecgbool

If True include ECG channels.

emgbool

If True include EMG channels.

ref_megbool | str

If True include CTF / 4D reference channels. If ‘auto’, reference channels are included if compensations are present and meg is not False. Can also be the string options for the meg parameter.

miscbool

If True include miscellaneous analog channels.

respbool

If True include respiratory channels.

chpibool

If True include continuous HPI coil channels.

excibool

Flux excitation channel used to be a stimulus channel.

iasbool

Internal Active Shielding data (maybe on Triux only).

systbool

System status channel information (on Triux systems only).

seegbool

Stereotactic EEG channels.

dipolebool

Dipole time course channels.

gofbool

Dipole goodness of fit channels.

biobool

Bio channels.

ecogbool

Electrocorticography channels.

fnirsbool | str

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

csdbool

EEG-CSD channels.

dbsbool

Deep brain stimulation channels.

temperaturebool

Temperature channels.

gsrbool

Galvanic skin response channels.

eyetrackbool | str

Eyetracking channels. If True include all eyetracking channels. If False (default) include none. If string it can be ‘eyegaze’ (to include eye position channels) or ‘pupil’ (to include pupil-size channels).

includelist of str

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

excludelist of str | str

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

selectionlist of str

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

verbosebool | str | int | None

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

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

pick_channels

Notes

New in v0.9.0.

plot(picks=None, *, exclude=(), tmin=None, tmax=None, fmin=0.0, fmax=inf, baseline=None, mode='mean', dB=False, combine=None, layout=None, yscale='auto', vlim=(None, None), cnorm=None, cmap=None, colorbar=True, title=None, mask=None, mask_style=None, mask_cmap='Greys', mask_alpha=0.1, axes=None, show=True, verbose=None)

Plot TFRs as two-dimensional time-frequency images.

Parameters:

picksstr | array_like | slice | None

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

excludelist of str | ‘bads’

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

tmin, tmaxfloat | None

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

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is None which is equivalent to fmin=0, fmax=np.inf (spans all frequencies present in the data).

baselineNone | tuple of length 2

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

Note

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

How baseline is computed is determined by the mode parameter.

mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’

Perform baseline correction by

• subtracting the mean of baseline values (‘mean’) (default)

• dividing by the mean of baseline values (‘ratio’)

• dividing by the mean of baseline values and taking the log (‘logratio’)

• subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)

• subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’)

• dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’)

dBbool

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

combine‘mean’ | ‘rms’ | callable() | None

How to aggregate across channels. If None, plot one figure per selected channel. If a string, "mean" uses numpy.mean(), "rms" computes the root-mean-square. If callable(), it must operate on an array of shape (n_channels, n_freqs, n_times) and return an array of shape (n_freqs, n_times). Defaults to None.

Changed in version 1.3: Added support for callable.

layoutinstance of Layout | None

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

yscale‘auto’ | ‘linear’ | ‘log’

The scale of the y (frequency) axis. ‘linear’ gives linear y axis, ‘log’ gives log-spaced y axis and ‘auto’ detects if frequencies are log-spaced and if so sets the y axis to ‘log’. Default is ‘auto’.

New in v0.14.0.

vlimtuple of length 2

Lower and upper bounds of the colormap, typically a numeric value in the same units as the data. If both entries are None, the bounds are set at ± the maximum absolute value of the data (yielding a colormap with midpoint at 0), or (0, max(abs(data))) if the (possibly baselined) data are all-positive. Providing None for just one entry will set the corresponding boundary at the min/max of the data. Defaults to (None, None).

cnormmatplotlib.colors.Normalize | None

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

New in v0.24.

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

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

Warning

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

colorbarbool

Whether to add a colorbar to the plot. Default is True.

titlestr | ‘auto’ | None

Title for the plot. If "auto", will use the channel name (if combine is None) or state the number and method of combined channels used to generate the plot. If None, no title is shown. Default is None.

maskndarray | None

An array of boolean values, of the same shape as the data. Data that corresponds to False entries in the mask are plotted differently, as determined by mask_style, mask_alpha, and mask_cmap. Useful for, e.g., highlighting areas of statistical significance.

New in v0.16.0.

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

How to distinguish the masked/unmasked regions of the plot. If "contour", a line is drawn around the areas where the mask is True. If "mask", areas where the mask is False will be (partially) transparent, as determined by mask_alpha. If "both", both a contour and transparency are used. Default is None, which is silently ignored if mask is None and is interpreted like "both" otherwise.

New in v0.17.

mask_cmapmatplotlib colormap | str | None

Colormap to use for masked areas of the plot. If a str, must be a valid Matplotlib colormap name. If None, cmap is used for both masked and unmasked areas. Ignored if mask is None. Default is 'Greys'.

New in v0.17.

mask_alphafloat

Relative opacity of the masked region versus the unmasked region, given as a float between 0 and 1 (i.e., 0 means masked areas are not visible at all). Defaults to 0.1.

New in v0.16.0.

axesinstance of Axes | list of Axes | None

The axes to plot into. If None, a new Figure will be created with the correct number of axes. If Axes are provided (either as a single instance or a list of axes), the number of axes provided must match the number of picks. If combine is not None, axes must either be an instance of Axes, or a list of length 1. Default is None.

showbool

Show the figure if True.

verbosebool | str | int | None

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

Returns:

figslist of instances of matplotlib.figure.Figure

A list of figures containing the time-frequency power.

Examples using plot:

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

plot_joint(*, timefreqs=None, picks=None, exclude=(), combine='mean', tmin=None, tmax=None, fmin=None, fmax=None, baseline=None, mode='mean', dB=False, yscale='auto', vlim=(None, None), cnorm=None, cmap=None, colorbar=True, title=None, show=True, topomap_args=None, image_args=None, verbose=None)

Plot TFRs as a two-dimensional image with topomap highlights.

Parameters:

timefreqsNone | list of tuple | dict of tuple

The time-frequency point(s) for which topomaps will be plotted. See Notes.

picksstr | array_like | slice | None

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

excludelist of str | ‘bads’

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

combine‘mean’ | ‘rms’ | callable()

How to aggregate across channels. If a string, "mean" uses numpy.mean(), "rms" computes the root-mean-square. If callable(), it must operate on an array of shape (n_channels, n_freqs, n_times) and return an array of shape (n_freqs, n_times). Defaults to "mean".

Changed in version 1.3: Added support for callable.

tmin, tmaxfloat | None

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

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is None which is equivalent to fmin=0, fmax=np.inf (spans all frequencies present in the data).

baselineNone | tuple of length 2

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

Note

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

How baseline is computed is determined by the mode parameter.

mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’

Perform baseline correction by

• subtracting the mean of baseline values (‘mean’) (default)

• dividing by the mean of baseline values (‘ratio’)

• dividing by the mean of baseline values and taking the log (‘logratio’)

• subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)

• subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’)

• dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’)

dBbool

Whether to plot on a decibel-like scale. If True, plots 10 × log₁₀(data).

yscale‘auto’ | ‘linear’ | ‘log’

The scale of the y (frequency) axis. ‘linear’ gives linear y axis, ‘log’ gives log-spaced y axis and ‘auto’ detects if frequencies are log-spaced and if so sets the y axis to ‘log’. Default is ‘auto’.

vlimtuple of length 2

Lower and upper bounds of the colormap, typically a numeric value in the same units as the data. If both entries are None, the bounds are set at ± the maximum absolute value of the data (yielding a colormap with midpoint at 0), or (0, max(abs(data))) if the (possibly baselined) data are all-positive. Providing None for just one entry will set the corresponding boundary at the min/max of the data. To specify the colormap separately for the topomap annotations, see topomap_args. Defaults to (None, None).

cnormmatplotlib.colors.Normalize | None

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

cmapmatplotlib colormap | str

The Colormap to use. If a str, must be a valid Matplotlib colormap name. Default is "RdBu_r".

colorbarbool

Whether to add a colorbar to the plot (for the topomap annotations). Not compatible with user-defined axes. Default is True.

titlestr | None

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

showbool

Show the figure if True.

topomap_argsdict | None

Keyword arguments to pass to mne.viz.plot_topomap(). axes and show are ignored. If times is not in this dict, automatic peak detection is used. Beyond that, if None, no customizable arguments will be passed. Defaults to None (i.e., an empty dict).

image_argsdict | None

Keyword arguments to pass to mne.time_frequency.AverageTFR.plot(). axes and show are ignored. Defaults to None (i.e., and empty dict).

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:

figmatplotlib.figure.Figure

The figure containing the topography.

Notes

timefreqs has three different modes: tuples, dicts, and auto. For (list of) tuple(s) mode, each tuple defines a pair (time, frequency) in s and Hz on the TFR plot. For example, to look at 10 Hz activity 1 second into the epoch and 3 Hz activity 300 ms into the epoch,

timefreqs=((1, 10), (.3, 3))

If provided as a dictionary, (time, frequency) tuples are keys and (time_window, frequency_window) tuples are the values — indicating the width of the windows (centered on the time and frequency indicated by the key) to be averaged over. For example,

timefreqs={(1, 10): (0.1, 2)}

would translate into a window that spans 0.95 to 1.05 seconds and 9 to 11 Hz. If None, a single topomap will be plotted at the absolute peak across the time-frequency representation.

New in v0.16.0.

Examples using plot_joint:

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

plot_topo(picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, layout=None, cmap='RdBu_r', title=None, dB=False, colorbar=True, layout_scale=0.945, show=True, border='none', fig_facecolor='k', fig_background=None, font_color='w', yscale='auto', verbose=None)

Plot a TFR image for each channel in a sensor layout arrangement.

Parameters:

picksstr | array_like | slice | None

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

baselineNone | tuple of length 2

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

Note

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

How baseline is computed is determined by the mode parameter.

mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’

Perform baseline correction by

• subtracting the mean of baseline values (‘mean’) (default)

• dividing by the mean of baseline values (‘ratio’)

• dividing by the mean of baseline values and taking the log (‘logratio’)

• subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)

• subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’)

• dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’)

tmin, tmaxfloat | None

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

fmin, fmaxfloat

The lower- and upper-bound on frequencies of interest. Default is None which is equivalent to fmin=0, fmax=np.inf (spans all frequencies present in the data).

vmin, vmaxfloat | None

Lower and upper bounds of the colormap, in the same units as the data. If vmin and vmax are both None, the bounds 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.

layoutinstance of Layout | None

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

cmapmatplotlib colormap | str

The Colormap to use. If a str, must be a valid Matplotlib colormap name. Default is "RdBu_r".

titlestr | None

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

dBbool

Whether to plot on a decibel-like scale. If True, plots 10 × log₁₀(data).

colorbarbool

Whether to add a colorbar to the plot. Default is True.

layout_scalefloat

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

showbool

Show the figure if True.

borderstr

Matplotlib border style to be used for each sensor plot.

fig_facecolorstr | tuple

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

fig_backgroundNone | array

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

font_colorcolor

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

yscale‘auto’ | ‘linear’ | ‘log’

The scale of the y (frequency) axis. ‘linear’ gives linear y axis, ‘log’ gives log-spaced y axis and ‘auto’ detects if frequencies are log-spaced and if so sets the y axis to ‘log’. Default is ‘auto’.

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:

figmatplotlib.figure.Figure

The figure containing the topography.

Examples using plot_topo:

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

plot_topomap(tmin=None, tmax=None, fmin=0.0, fmax=inf, *, ch_type=None, baseline=None, mode='mean', sensors=True, show_names=False, mask=None, mask_params=None, contours=6, outlines='head', sphere=None, image_interp='cubic', extrapolate='auto', border='mean', res=64, size=2, cmap=None, vlim=(None, None), cnorm=None, colorbar=True, cbar_fmt='%1.1e', units=None, axes=None, show=True)

Plot topographic maps of specific time-frequency intervals of TFR data.

Parameters:

tmin, tmaxfloat | None

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

fmin, fmaxfloat

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

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

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

baselinetuple or list of length 2

The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) the whole time interval is used.

mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’ | None

Perform baseline correction by

• subtracting the mean baseline power (‘mean’)

• dividing by the mean baseline power (‘ratio’)

• dividing by the mean baseline power and taking the log (‘logratio’)

• subtracting the mean baseline power followed by dividing by the mean baseline power (‘percent’)

• subtracting the mean baseline power and dividing by the standard deviation of the baseline power (‘zscore’)

• dividing by the mean baseline power, taking the log, and dividing by the standard deviation of the baseline power (‘zlogratio’)

If None no baseline correction is applied.

sensorsbool | str

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

show_namesbool | callable()

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

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

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

mask_paramsdict | None

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

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

contoursint | array_like

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

outlines‘head’ | dict | None

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

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

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

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

image_interpstr

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

extrapolatestr

Options:

• 'box'

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

• 'local' (default for MEG sensors)

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

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

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

Changed in version 0.21:

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

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

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

borderfloat | ‘mean’

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

New in v0.20.

resint

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

sizefloat

Side length of each subplot in inches.

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

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

Warning

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

vlimtuple of length 2

Lower and upper bounds of the colormap, typically a numeric value in the same units as the data. If both entries are None, the bounds are set at (min(data), max(data)). Providing None for just one entry will set the corresponding boundary at the min/max of the data. Defaults to (None, None).

New in v1.2.

cnormmatplotlib.colors.Normalize | None

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

New in v1.2.

colorbarbool

Plot a colorbar in the rightmost column of the figure.

cbar_fmtstr

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

unitsstr | None

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

axesinstance of Axes | None

The axes to plot into. If None, a new Figure will be created. Default is None.

showbool

Show the figure if True.

Returns:

figmatplotlib.figure.Figure

The figure containing the topography.

Examples using plot_topomap:

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

reorder_channels(ch_names)

Reorder channels.

Parameters:

ch_nameslist

The desired channel order.

Returns:

instinstance of Raw, Epochs, or Evoked

The modified instance.

See also

drop_channels

pick_types

pick_channels

Notes

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

New in v0.16.0.

save(fname, *, overwrite=False, verbose=None)

Save time-frequency data to disk (in HDF5 format).

Parameters:

fnamepath-like

Path of file to save to, which should end with -tfr.h5 or -tfr.hdf5.

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.

See also

mne.time_frequency.read_tfrs

property sfreq

Sampling frequency of the data.

property shape

Data shape.

shift_time(tshift, relative=True)

Shift time scale in epoched or evoked data.

Parameters:

tshiftfloat

The (absolute or relative) time shift in seconds. If relative is True, positive tshift increases the time value associated with each sample, while negative tshift decreases it.

relativebool

If True, increase or decrease time values by tshift seconds. Otherwise, shift the time values such that the time of the first sample equals tshift.

Returns:

epochsMNE-object

The modified instance.

Notes

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

time_as_index(times, use_rounding=False)

Convert time to indices.

Parameters:

timeslist-like | float | int

List of numbers or a number representing points in time.

use_roundingbool

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

Returns:

indexndarray

Indices corresponding to the times supplied.

property times

The time points present in the data (in seconds).

property tmax

Last time point.

property tmin

First time point.

to_data_frame(picks=None, index=None, long_format=False, time_format=None, *, verbose=None)

Export data in tabular structure as a pandas DataFrame.

Channels are converted to columns in the DataFrame. By default, additional columns 'time', 'freq', 'epoch', and 'condition' (epoch event description) are added, unless index is not None (in which case the columns specified in index will be used to form the DataFrame’s index instead). 'epoch', and 'condition' are not supported for AverageTFR.

Parameters:

picksstr | array_like | slice | None

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

indexstr | list of str | None

Kind of index to use for the DataFrame. If None, a sequential integer index (pandas.RangeIndex) will be used. If 'time', a pandas.Index or pandas.TimedeltaIndex will be used (depending on the value of time_format). If a list of two or more string values, a pandas.MultiIndex will be created. Valid string values are 'time', 'freq', 'epoch', and 'condition' for EpochsTFR and 'time' and 'freq' for AverageTFR. Defaults to None.

long_formatbool

If True, the DataFrame is returned in long format where each row is one observation of the signal at a unique combination of time point, channel, epoch number, and condition. For convenience, a ch_type column is added to facilitate subsetting the resulting DataFrame. Defaults to False.

time_formatstr | None

Desired time format. If None, no conversion is applied, and time values remain as float values in seconds. If 'ms', time values will be rounded to the nearest millisecond and converted to integers. If 'timedelta', time values will be converted to pandas.Timedelta values. Default is None.

New in v0.23.

verbosebool | str | int | None

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

Returns:

dfinstance of pandas.DataFrame

A dataframe suitable for usage with other statistical/plotting/analysis packages.

Examples using to_data_frame:

Compute and visualize ERDS maps

Compute and visualize ERDS maps

Examples using mne.time_frequency.BaseTFR

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)