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MorletTfr

Namespace: RTPROCESSINGLIB  ·  Library: DSP Library

Python equivalent

mne.time_frequency.tfr_morlet in MNE-Python.

#include <dsp/morlet_tfr.h>

class UTILSLIB::MorletTfr

Complex Morlet wavelet time-frequency representation.

For each requested centre frequency f the signal is convolved (via FFT) with a complex Morlet wavelet whose time-domain standard deviation is σ_t = nCycles / (2π·f) The instantaneous power at every time sample is |convolution|².

// 30 log-spaced frequencies from 4 to 80 Hz, 7 cycles per wavelet
RowVectorXd freqs = RowVectorXd::LinSpaced(30, 4.0, 80.0);
MorletTfrResult r = MorletTfr::compute(vecSignal, 600.0, freqs);
// r.matPower → 30 × n_samples instantaneous-power map

Static Methods​

compute(vecData, dSFreq, vecFreqs, dNCycles)​

Compute Morlet TFR for a single channel time series.

Parameters:

  • vecData : const Eigen::RowVectorXd & Single-channel data row vector (1 × n_samples).

  • dSFreq : double Sampling frequency in Hz.

  • vecFreqs : const Eigen::RowVectorXd & Centre frequencies in Hz (row vector).

  • dNCycles : double Number of wavelet cycles (controls time–frequency trade-off; default 7).

Returns:

  • MorletTfrResult — MorletTfrResult with matPower (n_freqs × n_samples).

computeMultiChannel(matData, dSFreq, vecFreqs, dNCycles, vecPicks)​

Compute Morlet TFR for every (selected) channel of a data matrix.

Parameters:

  • matData : const Eigen::MatrixXd & Data matrix (n_channels × n_samples).

  • dSFreq : double Sampling frequency in Hz.

  • vecFreqs : const Eigen::RowVectorXd & Centre frequencies in Hz.

  • dNCycles : double Number of wavelet cycles (default 7).

  • vecPicks : const Eigen::RowVectorXi & Channel row indices; empty = all channels.

Returns:

  • QVector< MorletTfrResult > — One MorletTfrResult per selected channel.

Authors of this file​