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 1234 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, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

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.

mne.time_frequency.tfr_multitaper mne.time_frequency.read_tfrs