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]¶

Time-Frequency Representation (TFR) using Stockwell Transform.

Parameters:

inst : Epochs | Evoked The epochs or evoked object. fmin : None, float The minimum frequency to include. If None defaults to the minimum fft frequency greater than zero. fmax : None, float The maximum frequency to include. If None defaults to the maximum fft. n_fft : int | None The length of the windows used for FFT. If None, it defaults to the next power of 2 larger than the signal length. width : float The width of the Gaussian window. If < 1, increased temporal resolution, if > 1, increased frequency resolution. Defaults to 1. (classical S-Transform). decim : int The decimation factor on the time axis. To reduce memory usage. return_itc : bool Return intertrial coherence (ITC) as well as averaged power. n_jobs : int The number of jobs to run in parallel (over channels). verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

power : AverageTFR The averaged power. itc : AverageTFR 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.

Examples using mne.time_frequency.tfr_stockwell¶

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