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.

References