mne_denoise.dss.variants.ssvep_dss#

mne_denoise.dss.variants.ssvep_dss(sfreq: float, stim_freq: float, *, n_harmonics: int = 3, n_components: int | None = None, **dss_kws) → DSS[source]#

Create a DSS configured for SSVEP extraction.

Returns a pre-configured DSS object that can be fit on data to extract components locked to a stimulus frequency and its harmonics.

Parameters:

  • sfreq (float) – Sampling frequency in Hz.

  • stim_freq (float) – SSVEP stimulus frequency in Hz.

  • n_harmonics (int) – Number of harmonics to include in the comb filter. Default 3.

  • n_components (int, optional) – Number of DSS components to keep. If None, keep all.

  • **dss_kws – Additional keyword arguments passed to DSS (e.g. reg, normalize_input).

Returns:

dss – A DSS object configured with a CombFilterBias. Call .fit(data) to compute spatial filters, then .transform(data) to extract sources.

Return type:

DSS

Examples

>>> # Create SSVEP DSS for 12 Hz stimulation
>>> dss = ssvep_dss(sfreq=250, stim_freq=12)
>>> dss.fit(epochs)
>>> ssvep_sources = dss.transform(epochs)

>>> # Get denoised data back in sensor space
>>> dss = ssvep_dss(sfreq=250, stim_freq=12, return_type="epochs")
>>> dss.fit(epochs)
>>> denoised_epochs = dss.transform(epochs)