mne.read_events

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

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 | 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
eventsarray of int, shape (n_events, 3)

The array of events. The first column contains the event time in samples, with first_samp included. The third column contains the event id.

event_iddict

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

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

Working with events

Working with events
Rejecting bad data spans and breaks

Rejecting bad data spans and breaks

Rejecting bad data spans and breaks
The Epochs data structure: discontinuous data

The Epochs data structure: discontinuous data

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Exporting Epochs to Pandas DataFrames

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EEG processing and Event Related Potentials (ERPs)

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Non-parametric between conditions cluster statistic on single trial power

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Spatiotemporal permutation F-test on full sensor data

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

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

Permutation t-test on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

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Mass-univariate twoway repeated measures ANOVA on single trial power

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Mass-univariate twoway repeated measures ANOVA on single trial power
Simulate raw data using subject anatomy

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Simulate raw data using subject anatomy
Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response
XDAWN Denoising

XDAWN Denoising

XDAWN Denoising
Visualize channel over epochs as an image

Visualize channel over epochs as an image

Visualize channel over epochs as an image
Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance
Compare evoked responses for different conditions

Compare evoked responses for different conditions

Compare evoked responses for different conditions
Compute a cross-spectral density (CSD) matrix

Compute a cross-spectral density (CSD) matrix

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Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

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

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

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

FDR correction on T-test on sensor data

FDR correction on T-test on sensor data
Permutation T-test on sensor data

Permutation T-test on sensor data

Permutation T-test on sensor data
Decoding source space data

Decoding source space data

Decoding source space data
Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions
Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques
XDAWN Decoding From EEG data

XDAWN Decoding From EEG data

XDAWN Decoding From EEG data
Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)
Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters
Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs