mne.compute_raw_covariance(raw, tmin=0, tmax=None, tstep=0.2, reject=None, flat=None, picks=None, method='empirical', method_params=None, cv=3, scalings=None, n_jobs=1, return_estimators=False, reject_by_annotation=True, rank=None, verbose=None)[source]

Estimate noise covariance matrix from a continuous segment of raw data.

It is typically useful to estimate a noise covariance from empty room data or time intervals before starting the stimulation.


To estimate the noise covariance from epoched data, use mne.compute_covariance() instead.

rawinstance of Raw

Raw data.


Beginning of time interval in seconds. Defaults to 0.

tmaxfloat | None (default None)

End of time interval in seconds. If None (default), use the end of the recording.

tstepfloat (default 0.2)

Length of data chunks for artifact rejection in seconds. Can also be None to use a single epoch of (tmax - tmin) duration. This can use a lot of memory for large Raw instances.

rejectdict | None (default None)

Rejection parameters based on peak-to-peak amplitude. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’. If reject is None then no rejection is done. Example:

reject = dict(grad=4000e-13, # T / m (gradiometers)
              mag=4e-12, # T (magnetometers)
              eeg=40e-6, # V (EEG channels)
              eog=250e-6 # V (EOG channels)
flatdict | None (default None)

Rejection parameters based on flatness of signal. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’, and values are floats that set the minimum acceptable peak-to-peak amplitude. If flat is None then no rejection is done.

picksstr | list | 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(excluding reference MEG channels).

methodstr | list | None (default ‘empirical’)

The method used for covariance estimation. See mne.compute_covariance().

New in version 0.12.

method_paramsdict | None (default None)

Additional parameters to the estimation procedure. See mne.compute_covariance().

New in version 0.12.

cvint | sklearn.model_selection object (default 3)

The cross validation method. Defaults to 3, which will internally trigger by default sklearn.model_selection.KFold with 3 splits.

New in version 0.12.

scalingsdict | None (default None)

Defaults to dict(mag=1e15, grad=1e13, eeg=1e6). These defaults will scale magnetometers and gradiometers at the same unit.

New in version 0.12.


The number of jobs to run in parallel (default 1). Requires the joblib package.

New in version 0.12.

return_estimatorsbool (default False)

Whether to return all estimators or the best. Only considered if method equals ‘auto’ or is a list of str. Defaults to False.

New in version 0.12.


Whether to reject based on annotations. If True (default), epochs overlapping with segments whose description begins with 'bad' are rejected. If False, no rejection based on annotations is performed.

New in version 0.14.

rankNone | dict | ‘info’ | ‘full’

This controls the rank computation that can be read from the measurement info or estimated from the data. See Notes of mne.compute_rank() for details.The default is None.

New in version 0.17.

New in version 0.18: Support for ‘info’ mode.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

covinstance of Covariance | list

The computed covariance. If method equals ‘auto’ or is a list of str and return_estimators equals True, a list of covariance estimators is returned (sorted by log-likelihood, from high to low, i.e. from best to worst).

See also


Estimate noise covariance matrix from epoched data.


This function will:

  1. Partition the data into evenly spaced, equal-length epochs.

  2. Load them into memory.

  3. Subtract the mean across all time points and epochs for each channel.

  4. Process the Epochs by compute_covariance().

This will produce a slightly different result compared to using make_fixed_length_events(), Epochs, and compute_covariance() directly, since that would (with the recommended baseline correction) subtract the mean across time for each epoch (instead of across epochs) for each channel.