mne.compute_raw_covariance¶

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.
Note
To estimate the noise covariance from epoched data, use
mne.compute_covariance()
instead. Parameters
 rawinstance of
Raw
Raw data.
 tmin
float
Beginning of time interval in seconds. Defaults to 0.
 tmax
float
None
(defaultNone
) End of time interval in seconds. If None (default), use the end of the recording.
 tstep
float
(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. reject
dict
None
(defaultNone
) Rejection parameters based on peaktopeak amplitude. Valid keys are ‘grad’  ‘mag’  ‘eeg’  ‘eog’  ‘ecg’. If reject is None then no rejection is done. Example:
reject = dict(grad=4000e13, # T / m (gradiometers) mag=4e12, # T (magnetometers) eeg=40e6, # V (EEG channels) eog=250e6 # V (EOG channels) )
 flat
dict
None
(defaultNone
) Rejection parameters based on flatness of signal. Valid keys are ‘grad’  ‘mag’  ‘eeg’  ‘eog’  ‘ecg’, and values are floats that set the minimum acceptable peaktopeak amplitude. If flat is None then no rejection is done.
 picks
str
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). Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided. method
str
list
None
(default ‘empirical’) The method used for covariance estimation. See
mne.compute_covariance()
.New in version 0.12.
 method_params
dict
None
(defaultNone
) Additional parameters to the estimation procedure. See
mne.compute_covariance()
.New in version 0.12.
 cv
int
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.
 scalings
dict
None
(defaultNone
) 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.
 n_jobs
int
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.
 reject_by_annotationbool
Whether to reject based on annotations. If
True
(default), epochs overlapping with segments whose description begins with'bad'
are rejected. IfFalse
, no rejection based on annotations is performed.New in version 0.14.
 rank
None
 ‘info’  ‘full’ dict
This controls the rank computation that can be read from the measurement info or estimated from the data.
None
The rank will be estimated from the data after proper scaling of different channel types.
'info'
The rank is inferred from
info
. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 66.'full'
The rank is assumed to be full, i.e. equal to the number of good channels. If a
Covariance
is passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.dict
Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.
This parameter must be a dictionary whose keys correspond to channel types in the data (e.g.
'meg'
,'mag'
,'grad'
,'eeg'
), and whose values are integers representing the respective ranks. For example,{'mag': 90, 'eeg': 45}
will assume a rank of90
and45
for magnetometer data and EEG data, respectively.The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.
The default is
None
.New in version 0.17.
New in version 0.18: Support for ‘info’ mode.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only.
 rawinstance of
 Returns
 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 loglikelihood, from high to low, i.e. from best to worst).
 covinstance of
See also
compute_covariance
Estimate noise covariance matrix from epoched data.
Notes
This function will:
Partition the data into evenly spaced, equallength epochs.
Load them into memory.
Subtract the mean across all time points and epochs for each channel.
Process the
Epochs
bycompute_covariance()
.
This will produce a slightly different result compared to using
make_fixed_length_events()
,Epochs
, andcompute_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.