mne.time_frequency.tfr_stockwell

mne.time_frequency.tfr_stockwell(inst, fmin=None, fmax=None, n_fft=None, width=1.0, decim=1, return_itc=False, n_jobs=None, verbose=None)

Warning

LEGACY: New code should use .compute_tfr(method=”stockwell”, freqs=”auto”).

Compute Time-Frequency Representation (TFR) using Stockwell Transform.

Same computation as tfr_array_stockwell, but operates on Epochs objects instead of NumPy arrays.

See [1][2][3][4] for more information.

Parameters:

instEpochs | Evoked

The epochs or evoked object.

fminNone, float

The minimum frequency to include. If None defaults to the minimum fft frequency greater than zero.

fmaxNone, float

The maximum frequency to include. If None defaults to the maximum fft.

n_fftint | None

The length of the windows used for FFT. If None, it defaults to the next power of 2 larger than the signal length.

widthfloat

The width of the Gaussian window. If < 1, increased temporal resolution, if > 1, increased frequency resolution. Defaults to 1. (classical S-Transform).

decimint

The decimation factor on the time axis. To reduce memory usage.

return_itcbool

Return intertrial coherence (ITC) as well as averaged power.

n_jobsint

The number of jobs to run in parallel (over channels).

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

powerAverageTFR

The averaged power.

itcAverageTFR

The intertrial coherence. Only returned if return_itc is True.

See also

mne.time_frequency.tfr_array_stockwell

mne.time_frequency.tfr_multitaper

mne.time_frequency.tfr_array_multitaper

mne.time_frequency.tfr_morlet

mne.time_frequency.tfr_array_morlet

Notes

New in v0.9.0.

References

[1]

R. G. Stockwell. Why use the S-transform? In Luigi Rodino, Bert-Wolfgang Schulze, and M. W. Wong, editors, Pseudo-Differential Operators: Partial Differential Equations and Time-Frequency Analysis, number 52 in Fields Institute Communications, pages 279–309. American Mathematical Society, Providence, RI, 2007. doi:10.1090/fic/052.

[2]

Ali Moukadem, Zied Bouguila, Djaffar Ould Abdeslam, and Alain Dieterlen. Stockwell transform optimization applied on the detection of split in heart sounds. In Proceedings of EUSIPCO-2014, 2015–2019. Lisbon, 2014. IEEE. URL: https://ieeexplore.ieee.org/document/6952743.

[3]

Katherine L. Wheat, Piers L. Cornelissen, Stephen J. Frost, and Peter C. Hansen. During visual word recognition, phonology is accessed within 100 ms and may be mediated by a speech production code: evidence from magnetoencephalography. Journal of Neuroscience, 30(15):5229–5233, 2010. doi:10.1523/JNEUROSCI.4448-09.2010.

[4]

Kevin A. Jones, Bernice Porjesz, David Chorlian, Madhavi Rangaswamy, Chella Kamarajan, Ajayan Padmanabhapillai, Arthur Stimus, and Henri Begleiter. S-transform time-frequency analysis of P300 reveals deficits in individuals diagnosed with alcoholism. Clinical Neurophysiology, 117(10):2128–2143, 2006. doi:10.1016/j.clinph.2006.02.028.

Examples using mne.time_frequency.tfr_stockwell

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)