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)

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

[1] (1,2,3)

Vadim V Nikulin, Guido Nolte, and Gabriel Curio. A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition. NeuroImage, 55(4):1528–1535, 2011. doi:10.1016/j.neuroimage.2011.01.057.

[2] (1,2)

Stefan Haufe, Sven Dähne, and Vadim V Nikulin. Dimensionality reduction for the analysis of brain oscillations. NeuroImage, 101:583–597, 2014. doi:https://doi.org/10.1016/j.neuroimage.2014.06.073.

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)

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)

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

Compute Spectro-Spatial Decomposition (SSD) spatial filters

fit_transform(X, y=None, **fit_params)

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)

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)

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

Compute Spectro-Spatial Decomposition (SSD) spatial filters

inverse_transform()

Not implemented yet.

set_params(**params)

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)

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

Compute Spectro-Spatial Decomposition (SSD) spatial filters

Examples using mne.decoding.SSD

Compute Spectro-Spatial Decomposition (SSD) spatial filters

Compute Spectro-Spatial Decomposition (SSD) spatial filters