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EogRegression

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

Python equivalent

mne.preprocessing.regress_artifact in MNE-Python.

#include <dsp/eog_regression.h>

class UTILSLIB::EogRegression

Remove EOG artifacts via linear regression.

Fits a least-squares model: data_ch = beta * EOG + residual, then subtracts beta * EOG from each non-EOG channel.

Usage:

EogRegression eogReg;
eogReg.fit(data, info);           // fit regression coefficients
eogReg.apply(data, info);         // subtract EOG contribution in-place
// or one-step:
EogRegression::fitApply(data, info);

Public Methods​

EogRegression()​

Constructs a default EogRegression object.

fit(data, info, eogChannels)​

Fit regression coefficients from EOG to all other channels.

Uses ordinary least squares: beta = T * E^T * (E * E^T)^{-1}

Parameters:

  • data : const Eigen::MatrixXd & Data matrix (n_channels x n_times).

  • info : const FiffInfo & Measurement info (identifies EOG channels by kind == FIFFV_EOG_CH).

  • eogChannels : const QStringList & Optional explicit EOG channel names. If empty, auto-detect from info.

apply(data, info)​

Apply the fitted regression to remove EOG artifacts in-place.

Subtracts beta * EOG from all non-EOG channels. fit() must be called first.

Parameters:

  • data : Eigen::MatrixXd & Data matrix (n_channels x n_times). Modified in-place.

  • info : const FiffInfo & Measurement info.

coefficients()​

Returns the fitted regression coefficients.

Returns:

  • const Eigen::MatrixXd & — Matrix (n_non_eog_channels x n_eog_channels).

isFitted()​

Returns true if fit() has been called successfully.

Static Methods​

fitApply(data, info, eogChannels)​

Convenience: fit and apply in one step.

Parameters:

  • data : Eigen::MatrixXd & Data matrix (n_channels x n_times). Modified in-place.

  • info : const FiffInfo & Measurement info.

  • eogChannels : const QStringList & Optional explicit EOG channel names.

Authors of this file​