mne.decoding.SSD#

class mne.decoding.SSD(info, filt_params_signal, filt_params_noise, reg=None, n_components=None, picks=None, sort_by_spectral_ratio=True, return_filtered=False, n_fft=None, cov_method_params=None, rank=None)[source]#

M/EEG signal decomposition using the Spatio-Spectral Decomposition (SSD).

SSD seeks to maximize the power at a frequency band of interest while simultaneously minimizing it at the flanking (surrounding) frequency bins (considered noise). It extremizes the covariance matrices associated with signal and noise [1].

SSD can either be used as a dimensionality reduction method or a ‘denoised’ low rank factorization method [2].

Parameters:

infomne.Info: The mne.Info object with information about the sensors and methods of measurement. Must match the input data.
filt_params_signaldict: Filtering for the frequencies of interest.
filt_params_noisedict: Filtering for the frequencies of non-interest.
regfloat | str | None (default): Which covariance estimator to use. If not None (same as ‘empirical’), allow regularization for covariance estimation. If float, shrinkage is used (0 <= shrinkage <= 1). For str options, reg will be passed to method to mne.compute_covariance().
n_componentsint | None (default None): The number of components to extract from the signal. If n_components is None, no dimensionality reduction is applied.
picksarray of int | None (default None): The indices of good channels.
sort_by_spectral_ratiobool (default False): If set to True, the components are sorted accordingly to the spectral ratio. See Eq. (24) in [1].
return_filteredbool (default True): If return_filtered is True, data is bandpassed and projected onto the SSD components.
n_fftint (default None): If sort_by_spectral_ratio is set to True, then the SSD sources will be sorted accordingly to their spectral ratio which is calculated based on mne.time_frequency.psd_array_welch() function. The n_fft parameter set the length of FFT used. See mne.time_frequency.psd_array_welch() for more information.
cov_method_paramsdict | None (default None): As in mne.decoding.SPoC The default is None.
rankNone | dict | ‘info’ | ‘full’: As in mne.decoding.SPoC This controls the rank computation that can be read from the measurement info or estimated from the data. See Notes of mne.compute_rank() for details. We recommend to use ‘full’ when working with epoched data.

References

Attributes:

filters_array, shape (n_channels, n_components): The spatial filters to be multiplied with the signal.
patterns_array, shape (n_components, n_channels): The patterns for reconstructing the signal from the filtered data.

Methods

`apply`(X)	Remove selected components from the signal.
`fit`(X[, y])	Estimate the SSD decomposition on raw or epoched data.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`get_spectral_ratio`(ssd_sources)	Get the spectal signal-to-noise ratio for each spatial filter.
`inverse_transform`()	Not implemented yet.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Estimate epochs sources given the SSD filters.

apply(X)[source]#

Remove selected components from the signal.

This procedure will reconstruct M/EEG signals from which the dynamics described by the excluded components is subtracted (denoised by low-rank factorization). See [2] for more information.

Note

Unlike in other classes with an apply method, only NumPy arrays are supported (not instances of MNE objects).

Parameters:

Xarray, shape ([n_epochs, ]n_channels, n_times): The input data from which to estimate the SSD. Either 2D array obtained from continuous data or 3D array obtained from epoched data.

Returns:

Xarray, shape ([n_epochs, ]n_channels, n_times): The processed data.

fit(X, y=None)[source]#

Estimate the SSD decomposition on raw or epoched data.

Parameters:

Xarray, shape ([n_epochs, ]n_channels, n_times): The input data from which to estimate the SSD. Either 2D array obtained from continuous data or 3D array obtained from epoched data.
yNone | array, shape (n_samples,): Used for scikit-learn compatibility.

Returns:

selfinstance of SSD: Returns the modified instance.

Examples using fit:

Compute Spectro-Spatial Decomposition (SSD) spatial filters

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params, and returns a transformed version of X.

Parameters:

Xarray, shape (n_samples, n_features): Training set.
yarray, shape (n_samples,): Target values or class labels.
**fit_paramsdict: Additional fitting parameters passed to the fit method..

Returns:

X_newarray, shape (n_samples, n_features_new): Transformed array.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, optional: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

get_spectral_ratio(ssd_sources)[source]#

Get the spectal signal-to-noise ratio for each spatial filter.

Spectral ratio measure for best n_components selection See [1], Eq. (24).

Parameters:

ssd_sourcesarray: Data projected to SSD space.

Returns:

spec_ratioarray, shape (n_channels): Array with the sprectal ratio value for each component.
sorter_specarray, shape (n_channels): Array of indices for sorting spec_ratio.

References

Examples using get_spectral_ratio:

Compute Spectro-Spatial Decomposition (SSD) spatial filters

inverse_transform()[source]#: Not implemented yet.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Parameters.

Returns:

instinstance: The object.

transform(X)[source]#

Estimate epochs sources given the SSD filters.

Parameters:

Xarray, shape ([n_epochs, ]n_channels, n_times): The input data from which to estimate the SSD. Either 2D array obtained from continuous data or 3D array obtained from epoched data.

Returns:

X_ssdarray, shape ([n_epochs, ]n_components, n_times): The processed data.

Examples using transform:

Compute Spectro-Spatial Decomposition (SSD) spatial filters

Examples using `mne.decoding.SSD`#

Compute Spectro-Spatial Decomposition (SSD) spatial filters

mne.decoding.SSD#

Examples using mne.decoding.SSD#

Examples using `mne.decoding.SSD`#