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)[source]#

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: The decimation factor on the time axis. To reduce memory usage.
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_backend() context manager that sets another value for n_jobs.

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.