RtCov

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

#include <dsp/rt_cov.h>

class RTPROCESSINGLIB::RtCov

Real-time covariance worker.

Controller that manages background covariance matrix estimation from streaming data.

src/examples/ex_dsp_rt/main.cpp

RtCov rtCov(info);
FiffCov cov;
for (int start = 0; start < covData.cols(); start += 1000) {
    cov = rtCov.estimateCovariance(covData.middleCols(start, 1000), 4000); // empty until 4000 samples arrived
}

Inheritance

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Public Methods

RtCov(pFiffInfo)

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estimateCovariance(matData, iNewMaxSamples)

Perform actual covariance estimation.

Parameters:

• matData : const Eigen::MatrixXd & Data to estimate the covariance from.

• iNewMaxSamples : int Number of samples to accumulate before a covariance is estimated.

Returns:

• FiffCov — Noise covariance of the MEG/EEG channels, or an empty FiffCov while fewer than iNewMaxSamples samples are accumulated or if no FiffInfo was set.

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Example

• Full source: src/examples/ex_dsp_rt/main.cpp
• Build target: ex_dsp_rt (runs under CTest)
• Data: mne-cpp-test-data

Used in examples

ex_dsp_rt

DSP real-time workers: averaging, covariance, noise spectrum, connectivity, HPI fitting and inverse operators.

src/examples/ex_dsp_rt/main.cpp, line 196

RtCov rtCov(info);
FiffCov cov;
for (int start = 0; start < covData.cols(); start += 1000) {
    cov = rtCov.estimateCovariance(covData.middleCols(start, 1000), 4000); // empty until 4000 samples arrived
}
// regularize() adds 0.1 x mean MEG variance to the diagonal; the off-diagonal term is untouched
ok &= expect(cov.dim == 2 && cov.names == QStringList({"MEG0111", "MEG0121"}) && cov.nfree == 4000 && std::fabs(cov.data(0, 1) - numpyCov(0, 1)) < 1e-6 * std::fabs(numpyCov(0, 1)),
             QString("RtCov: covariance of 4000 samples matches numpy.cov off the diagonal (%1)").arg(cov.data(0, 1), 0, 'g', 6));

Authors of this file

• Christoph Dinh <christoph.dinh@mne-cpp.org>