mne.read_epochs

mne.read_epochs(fname, proj=True, preload=True, verbose=None)[source]

Read epochs from a fif file.

Parameters
fnamepath-like | file-like

The epochs to load. If a filename, should end with -epo.fif or -epo.fif.gz. If a file-like object, preloading must be used.

projbool | ‘delayed’

Apply SSP projection vectors. If proj is ‘delayed’ and reject is not None the single epochs will be projected before the rejection decision, but used in unprojected state if they are kept. This way deciding which projection vectors are good can be postponed to the evoked stage without resulting in lower epoch counts and without producing results different from early SSP application given comparable parameters. Note that in this case baselining, detrending and temporal decimation will be postponed. If proj is False no projections will be applied which is the recommended value if SSPs are not used for cleaning the data.

preloadbool

If True, read all epochs from disk immediately. If False, epochs will be read on demand.

verbosebool | 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.

Returns
epochsinstance of Epochs

The epochs.

Examples using mne.read_epochs

The Epochs data structure: discontinuous data

The Epochs data structure: discontinuous data

The Epochs data structure: discontinuous data
Working with Epoch metadata

Working with Epoch metadata

Working with Epoch metadata
Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data
Analysing continuous features with binning and regression in sensor space

Analysing continuous features with binning and regression in sensor space

Analysing continuous features with binning and regression in sensor space