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, 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 — Working with events¶

Rejecting bad data spans — Rejecting bad data spans¶

The Epochs data structure: discontinuous data — The Epochs data structure: discontinuous data¶

Exporting Epochs to Pandas DataFrames — Exporting Epochs to Pandas DataFrames¶

EEG processing and Event Related Potentials (ERPs) — EEG processing and Event Related Potentials (ERPs)¶

Non-parametric between conditions cluster statistic on single trial power — Non-parametric between conditions cluster statistic on single trial power¶

Spatiotemporal permutation F-test on full sensor data — Spatiotemporal permutation F-test on full sensor data¶

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¶

Mass-univariate twoway repeated measures ANOVA on single trial power — Mass-univariate twoway repeated measures ANOVA on single trial power¶

Simulate raw data using subject anatomy — 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¶

XDAWN Denoising — XDAWN Denoising¶

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¶

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¶

Compute Power Spectral Density of inverse solution from single epochs — Compute Power Spectral Density of inverse solution from single epochs¶

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¶

Permutation T-test on sensor data — Permutation T-test on sensor 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¶

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¶

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¶

Compute seed-based time-frequency connectivity in sensor space — Compute seed-based time-frequency connectivity in sensor space¶

Compute coherence in source space using a MNE inverse solution — Compute coherence in source space using a MNE inverse solution¶

Compute full spectrum source space connectivity between labels — Compute full spectrum source space connectivity between labels¶

Compute source space connectivity and visualize it using a circular graph — Compute source space connectivity and visualize it using a circular graph¶

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

Compute all-to-all connectivity in sensor space — Compute all-to-all connectivity in sensor space¶

Compute MNE-dSPM inverse solution on single epochs — Compute MNE-dSPM inverse solution on single epochs¶

Time-frequency beamforming using DICS — Time-frequency beamforming using DICS¶