mne.read_evokeds#
- mne.read_evokeds(fname, condition=None, baseline=None, kind='average', proj=True, allow_maxshield=False, verbose=None) list[Evoked] | Evoked [source]#
Read evoked dataset(s).
- Parameters:
- fnamepath-like
The filename, which should end with
-ave.fif
or-ave.fif.gz
.- condition
int
orstr
|list
ofint
orstr
|None
The index or list of indices of the evoked dataset to read. FIF files can contain multiple datasets. If None, all datasets are returned as a list.
- baseline
None
|tuple
of length 2 The time interval to consider as “baseline” when applying baseline correction. If
None
, do not apply baseline correction. If a tuple(a, b)
, the interval is betweena
andb
(in seconds), including the endpoints. Ifa
isNone
, the beginning of the data is used; and ifb
isNone
, it is set to the end of the data. If(None, None)
, the entire time interval is used.Note
The baseline
(a, b)
includes both endpoints, i.e. all timepointst
such thata <= t <= b
.Correction is applied to each channel individually in the following way:
Calculate the mean signal of the baseline period.
Subtract this mean from the entire
Evoked
.
If
None
(default), do not apply baseline correction.Note
Note that if the read
Evoked
objects have already been baseline-corrected, the data retrieved from disk will always be baseline-corrected (in fact, only the baseline-corrected version of the data will be saved, so there is no way to undo this procedure). Only after the data has been loaded, a custom (additional) baseline correction may be optionally applied by passing a tuple here. PassingNone
will not remove an existing baseline correction, but merely omit the optional, additional baseline correction.- kind
str
Either
'average'
or'standard_error'
, the type of data to read.- projbool
If False, available projectors won’t be applied to the data.
- allow_maxshieldbool |
str
(defaultFalse
) If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be
"yes"
to load without eliciting a warning.- verbosebool |
str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- Returns:
See also
Notes
Changed in version 0.23: If the read
Evoked
objects had been baseline-corrected before saving, this will be reflected in theirbaseline
attribute after reading.
Examples using mne.read_evokeds
#
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Compute MNE-dSPM inverse solution on evoked data in volume source space
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Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
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Extracting the time series of activations in a label
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Compute sparse inverse solution with mixed norm: MxNE and irMxNE
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Compute MNE inverse solution on evoked data with a mixed source space
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Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
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Compute spatial resolution metrics in source space
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Compute spatial resolution metrics to compare MEG with EEG+MEG
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Source localization with equivalent current dipole (ECD) fit
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The role of dipole orientations in distributed source localization