braindecode.preprocessing.ComputeCurrentSourceDensity

class braindecode.preprocessing.ComputeCurrentSourceDensity(sphere='auto', lambda2=1e-05, stiffness=4, n_legendre_terms=50, copy=True, *, verbose=None)

Braindecode preprocessor wrapper for compute_current_source_density().

Get the current source density (CSD) transformation.

Transformation based on spherical spline surface Laplacian [1][2][3][4].

This function can be used to re-reference the signal using a Laplacian (LAP) “reference-free” transformation.

Parameters:

instinstance of Raw, Epochs or Evoked

The data to be transformed.

spherearray-like, shape (4,) | str

The sphere, head-model of the form (x, y, z, r) where x, y, z is the center of the sphere and r is the radius in meters. Can also be “auto” to use a digitization-based fit.

lambda2float

Regularization parameter, produces smoothness. Defaults to 1e-5.

stiffnessfloat

Stiffness of the spline.

n_legendre_termsint

Number of Legendre terms to evaluate.

copybool

Whether to overwrite instance data or create a copy.

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:

inst_csdinstance of Raw, Epochs or Evoked

The transformed data. Output type will match input type.

Notes

Added in version 0.20.

Methods

fn(sphere='auto', lambda2=1e-05, stiffness=4, n_legendre_terms=50, copy=True, *, verbose=None)

Get the current source density (CSD) transformation.

Transformation based on spherical spline surface Laplacian [1][2][3][4].

This function can be used to re-reference the signal using a Laplacian (LAP) “reference-free” transformation.

Parameters:

• inst (instance of Raw, Epochs or Evoked) – The data to be transformed.

• sphere (array-like, shape (4,) | str) – The sphere, head-model of the form (x, y, z, r) where x, y, z is the center of the sphere and r is the radius in meters. Can also be “auto” to use a digitization-based fit.

• lambda2 (float) – Regularization parameter, produces smoothness. Defaults to 1e-5.

• stiffness (float) – Stiffness of the spline.

• n_legendre_terms (int) – Number of Legendre terms to evaluate.

• copy (bool) – Whether to overwrite instance data or create a copy.

• verbose (bool | 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:

inst_csd – The transformed data. Output type will match input type.

Return type:

instance of Raw, Epochs or Evoked

Notes

Added in version 0.20.

References

[1] (1,2)

F. Perrin, O. Bertrand, and J. Pernier. Scalp Current Density Mapping: Value and Estimation from Potential Data. IEEE Transactions on Biomedical Engineering, BME-34(4):283–288, 1987. doi:10.1109/TBME.1987.326089.

[2] (1,2)

François M. Perrin, Jacques Pernier, Olivier M. Bertrand, and Jean Franćois Echallier. Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2):184–187, 1989. doi:10.1016/0013-4694(89)90180-6.

[3] (1,2)

Mike X. Cohen. Analyzing Neural Time Series Data: Theory and Practice. MIT Press, 2014.

[4] (1,2)

Jürgen Kayser and Craig E. Tenke. On the benefits of using surface Laplacian (Current Source Density) methodology in electrophysiology. International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 97(3):171–173, 2015. doi:10.1016/j.ijpsycho.2015.06.001.