mne.read_evokeds#
- mne.read_evokeds(fname, condition=None, baseline=None, kind='average', proj=True, allow_maxshield=False, verbose=None)[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 interval. 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.
- proj
bool
If False, available projectors won’t be applied to the data.
- allow_maxshield
bool
|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.
- verbose
bool
|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
#
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)
Plot point-spread functions (PSFs) for a volume
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
Compute Trap-Music on evoked data
Plotting the full vector-valued MNE solution