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ConnectivityAec

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

Python equivalent

mne_connectivity.envelope_correlation in MNE-Python.

#include <dsp/connectivity_aec.h>

class UTILSLIB::ConnectivityAec

Amplitude Envelope Correlation connectivity.

// Standard AEC
MatrixXd aec = ConnectivityAec::compute(bandpassedData);

// Orthogonalized AEC (corrected for source leakage)
MatrixXd aecCorr = ConnectivityAec::computeOrthogonalized(bandpassedData);

Static Methods​

compute(matData)​

Compute AEC between all pairs of signals.

Steps: Hilbert envelope → Pearson correlation of envelopes.

Parameters:

  • matData : const Eigen::MatrixXd & Bandpassed data (n_signals x n_samples).

Returns:

  • Eigen::MatrixXd — Symmetric connectivity matrix (n_signals x n_signals) with values in [-1, 1].

computeOrthogonalized(matData)​

Compute orthogonalized AEC (corrected for volume conduction / source leakage).

For each pair (i, j): orthogonalize j w.r.t. i, compute envelope correlation, then symmetrise by averaging AEC(i→j) and AEC(j→i).

Parameters:

  • matData : const Eigen::MatrixXd & Bandpassed data (n_signals x n_samples).

Returns:

  • Eigen::MatrixXd — Symmetric connectivity matrix (n_signals x n_signals) with values in [0, 1].

hilbertEnvelope(signal)​

Compute the analytic signal envelope via Hilbert transform (using FFT).

Parameters:

  • signal : const Eigen::VectorXd & Real-valued signal (n_samples).

Returns:

  • Eigen::VectorXd — Envelope (n_samples) — absolute value of analytic signal.

pearsonCorrelation(a, b)​

Pearson correlation between two vectors.

Parameters:

  • a : const Eigen::VectorXd & First vector.

  • b : const Eigen::VectorXd & Second vector (same length).

Returns:

  • double — Correlation coefficient in [-1, 1].

Authors of this file​