- mne.read_evokeds(fname, condition=None, baseline=None, kind='average', proj=True, allow_maxshield=False, verbose=None)[source]#
Read evoked dataset(s).
The filename, which should end with
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.
tupleof 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 between
b(in seconds), including the endpoints. If
None, the beginning of the data is used; and if
None, it is set to the end of the interval. If
(None, None), the entire time interval is used.
(a, b)includes both endpoints, i.e. all timepoints
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
None(default), do not apply baseline correction.
Note that if the read
Evokedobjects 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
Nonewill not remove an existing baseline correction, but merely omit the optional, additional baseline correction.
Either ‘average’ or ‘standard_error’, the type of data to read.
If False, available projectors won’t be applied to the data.
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.
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.
Changed in version 0.23: If the read
Evokedobjects had been baseline-corrected before saving, this will be reflected in their
baselineattribute after reading.
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
Plotting the full vector-valued MNE solution