mne.read_evokeds(fname, condition=None, baseline=None, kind='average', proj=True, allow_maxshield=False, verbose=None)[source]

Read evoked dataset(s).


The file name, which should end with -ave.fif or -ave.fif.gz.

conditionint 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.

baselineNone | 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 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.


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:

  1. Calculate the mean signal of the baseline period.

  2. Subtract this mean from the entire Evoked.

Defaults to None, i.e. no baseline correction.


Either ‘average’ or ‘standard_error’, the type of data to read.


If False, available projectors won’t be applied to the data.

allow_maxshieldbool | 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.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

evokedEvoked or list of Evoked

The evoked dataset(s); one Evoked if condition is int or str, or list of Evoked if condition is None or list.

See also


Examples using mne.read_evokeds