mne.time_frequency.tfr_array_stockwell

mne.time_frequency.tfr_array_stockwell(data, sfreq, fmin=None, fmax=None, n_fft=None, width=1.0, decim=1, return_itc=False, n_jobs=None, *, verbose=None)

Compute power and intertrial coherence using Stockwell (S) transform.

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

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

Parameters:

datandarray, shape (n_epochs, n_channels, n_times)

The signal to transform.

sfreqfloat

The sampling frequency.

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 | slice

Decimation factor, applied after time-frequency decomposition.

• if int, returns tfr[..., ::decim] (keep only every Nth sample along the time axis).

• if slice, returns tfr[..., decim] (keep only the specified slice along the time axis).

Note

Decimation is done after convolutions and may create aliasing artifacts.

return_itcbool

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

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

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:

st_powerndarray

The multitaper power of the Stockwell transformed data. The last two dimensions are frequency and time.

itcndarray

The intertrial coherence. Only returned if return_itc is True.

freqsndarray

The frequencies.

See also

mne.time_frequency.tfr_stockwell

mne.time_frequency.tfr_multitaper

mne.time_frequency.tfr_array_multitaper

mne.time_frequency.tfr_morlet

mne.time_frequency.tfr_array_morlet

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.