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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.

src/examples/ex_dsp_analysis/main.cpp
const MatrixXd aec = ConnectivityAec::compute(envelopeSignals);                   // Pearson r of Hilbert envelopes
const MatrixXd aecOrth = ConnectivityAec::computeOrthogonalized(envelopeSignals); // leakage-corrected

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, nFft)​

Compute the analytic signal envelope via Hilbert transform (using FFT), like numpy.abs(scipy.signal.hilbert(signal, N=nFft)[:n_samples]).

Parameters:

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

  • nFft : int FFT length (zero padding), at least n_samples; 0 for 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].

Example​

Used in examples​

ex_dsp_analysis​

DSP library: Welch PSD, FIR filtering, peak finding, envelope correlation, SPHARA and simulation.

src/examples/ex_dsp_analysis/main.cpp, line 160
const MatrixXd aec = ConnectivityAec::compute(envelopeSignals);                   // Pearson r of Hilbert envelopes
const MatrixXd aecOrth = ConnectivityAec::computeOrthogonalized(envelopeSignals); // leakage-corrected
// numpy corrcoef(|scipy.signal.hilbert(x)|): r(a,b) = 0.98006658, r(a,c) = -0.00701172; orthogonalized r(a,c) = 0.09799620
ok &= expect(std::fabs(aec(0, 1) - 0.9800665778412415) < 1e-9 && std::fabs(aec(0, 2) + 0.007011719607347298) < 1e-9 && std::fabs(aecOrth(0, 2) - 0.09799619541278072) < 1e-9 && aecOrth(0, 1) > 0.98,
             QString("ConnectivityAec: r(a,b) = %1, r(a,c) = %2 match scipy").arg(aec(0, 1), 0, 'f', 6).arg(aec(0, 2), 0, 'f', 6));

Authors of this file​