Read evoked dataset(s).
The filename, which should end with -ave.fif
or -ave.fif.gz
.
int
or str
| list
of int
or str
| 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.
None
| tuple
of length 2The 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 between a
and b
(in seconds), including the endpoints.
If a
is None
, the beginning of the data is used; and if b
is None
, it is set to the end of the interval.
If (None, None)
, the entire time interval is used.
Note
The baseline (a, b)
includes both endpoints, i.e. all
timepoints t
such that a <= 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. Passing None
will not remove an existing
baseline correction, but merely omit the optional, additional
baseline correction.
str
Either ‘average’ or ‘standard_error’, the type of data to read.
If False, available projectors won’t be applied to the data.
str
(default False
)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.
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.
See also
Notes
Changed in version 0.23: If the read Evoked
objects had been baseline-corrected before
saving, this will be reflected in their baseline
attribute after
reading.
mne.read_evokeds
#Getting started with mne.Report
The Evoked data structure: evoked/averaged data
Source localization with equivalent current dipole (ECD) fit
The role of dipole orientations in distributed source localization
Computing various MNE solutions
Visualize source time courses (stcs)
Interpolate bad channels for MEG/EEG channels
Shifting time-scale in evoked data
Plotting topographic arrowmaps of evoked data
Plotting topographic maps of evoked data
Make figures more publication ready
Compute MNE-dSPM inverse solution on evoked data in volume source space
Source localization with a custom inverse solver
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Generate a functional label from source estimates
Extracting the time series of activations in a label
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space
Morph volumetric source estimate
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute Rap-Music on evoked data
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Estimate data SNR using an inverse
Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution