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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=1, verbose=None)[source]#

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 version 0.9.0.

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`#

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

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

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

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

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