mne.read_events

mne.read_events(filename, include=None, exclude=None, mask=None, mask_type='and', return_event_id=False, verbose=None)

Read events from fif or text file.

See Parsing events from raw data and Working with events for more information about events.

Parameters

filenamestr

Name of the input file. If the extension is .fif, events are read assuming the file is in FIF format, otherwise (e.g., .eve, .lst, .txt) events are read as coming from text. Note that new format event files do not contain the “time” column (used to be the second column).

includeint | list | None

A event id to include or a list of them. If None all events are included.

excludeint | list | None

A event id to exclude or a list of them. If None no event is excluded. If include is not None the exclude parameter is ignored.

maskint | None

The value of the digital mask to apply to the stim channel values. If None (default), no masking is performed.

mask_type‘and’ | ‘not_and’

The type of operation between the mask and the trigger. Choose ‘and’ (default) for MNE-C masking behavior.

New in version 0.13.

return_event_idbool

If True, event_id will be returned. This is only possible for -annot.fif files produced with MNE-C mne_browse_raw.

New in version 0.20.

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.

Returns

events: array, shape (n_events, 3)

The list of events.

event_iddict

Dictionary of {str: int} mappings of event IDs.

See also

find_events, write_events

Notes

This function will discard the offset line (i.e., first line with zero event number) if it is present in a text file.

For more information on mask and mask_type, see mne.find_events().

Examples using mne.read_events

Working with events

Rejecting bad data spans

The Epochs data structure: discontinuous data

Exporting Epochs to Pandas DataFrames

EEG processing and Event Related Potentials (ERPs)

Non-parametric between conditions cluster statistic on single trial power

Spatiotemporal permutation F-test on full sensor data

Permutation t-test on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Mass-univariate twoway repeated measures ANOVA on single trial power

Simulate raw data using subject anatomy

Define target events based on time lag, plot evoked response

XDAWN Denoising

Visualize channel over epochs as an image

Whitening evoked data with a noise covariance

Compare evoked responses for different conditions

Compute a cross-spectral density (CSD) matrix

Compute Power Spectral Density of inverse solution from single epochs

Permutation F-test on sensor data with 1D cluster level

FDR correction on T-test on sensor data

Permutation T-test on sensor data

Decoding source space data

Decoding sensor space data with generalization across time and conditions

Analysis of evoked response using ICA and PCA reduction techniques

XDAWN Decoding From EEG data

Compute effect-matched-spatial filtering (EMS)

Linear classifier on sensor data with plot patterns and filters

Compute seed-based time-frequency connectivity in sensor space

Compute coherence in source space using a MNE inverse solution

Compute full spectrum source space connectivity between labels

Compute source space connectivity and visualize it using a circular graph

Compute Phase Slope Index (PSI) in source space for a visual stimulus

Compute all-to-all connectivity in sensor space

Compute MNE-dSPM inverse solution on single epochs

mne.read_epochs_fieldtrip mne.read_evokeds