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.
Raw
Raw data.
float
Beginning of time interval in seconds. Defaults to 0.
float
| None
(default None
)End of time interval in seconds. If None (default), use the end of the recording.
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.
dict
| 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)
)
dict
| 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.
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 in info['bads']
will be included if
their names or indices are explicitly provided.
str
| list
| None
(default ‘empirical’)The method used for covariance estimation.
See mne.compute_covariance()
.
New in version 0.12.
dict
| None
(default None
)Additional parameters to the estimation procedure.
See mne.compute_covariance()
.
New in version 0.12.
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.
dict
| 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.
int
| None
The number of jobs to run in parallel. If -1
, it is set
to the number of CPU cores. Requires the joblib
package.
None
(default) is a marker for ‘unset’ that will be interpreted
as n_jobs=1
(sequential execution) unless the call is performed under
a joblib.parallel_backend()
context manager that sets another
value for n_jobs
.
New in version 0.12.
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.
None
| ‘info’ | ‘full’ | dict
This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).
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 of 90
and 45
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.
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.
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
compute_covariance
Estimate noise covariance matrix from epoched data.
Notes
This function will:
Partition the data into evenly spaced, equal-length epochs.
Load them into memory.
Subtract the mean across all time points and epochs for each channel.
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.
mne.compute_raw_covariance
#Working with CTF data: the Brainstorm auditory dataset
Rejecting bad data spans and breaks
Brainstorm CTF phantom dataset tutorial
Compute source power spectral density (PSD) of VectorView and OPM data
Compute source power estimate by projecting the covariance with MNE