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.
 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). 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
(default 1) Number of jobs to run in parallel.
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. If False, no rejection based on annotations is performed.New in version 0.14.
 rank
None
dict
 ‘info’  ‘full’ This controls the rank computation that can be read from the measurement info or estimated from the data. See
Notes
ofmne.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
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 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 epochs
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.