mne.time_frequency.fit_iir_model_raw(raw, order=2, picks=None, tmin=None, tmax=None, verbose=None)[source]#

Fit an AR model to raw data and creates the corresponding IIR filter.

The computed filter is fitted to data from all of the picked channels, with frequency response given by the standard IIR formula:

\[H(e^{jw}) = \frac{1}{a[0] + a[1]e^{-jw} + ... + a[n]e^{-jnw}}\]
rawRaw object

An instance of Raw.


Order of the FIR filter.

picksstr | array_like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.


The beginning of time interval in seconds.


The end of time interval in seconds.

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.


Numerator filter coefficients.


Denominator filter coefficients.

Examples using mne.time_frequency.fit_iir_model_raw#

Generate simulated evoked data

Generate simulated evoked data

Temporal whitening with AR model

Temporal whitening with AR model