mne.read_events¶

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

Read events from fif or text file.

See Epoching and averaging (ERP/ERF) as well as Reading an event file 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.

Returns

events: array, shape (n_events, 3): The list of events

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`¶

../_images/sphx_glr_plot_read_epochs_thumb.png

Reading epochs from a raw FIF file¶

../_images/sphx_glr_plot_read_events_thumb.png

Reading an event file¶

../_images/sphx_glr_plot_simulated_raw_data_using_subject_anatomy_thumb.png

Simulate raw data using subject anatomy¶

../_images/sphx_glr_plot_define_target_events_thumb.png

Define target events based on time lag, plot evoked response¶

../_images/sphx_glr_plot_metadata_query_thumb.png

Querying epochs with rich metadata¶

../_images/sphx_glr_plot_rereference_eeg_thumb.png

Re-referencing the EEG signal¶

../_images/sphx_glr_plot_xdawn_denoising_thumb.png

XDAWN Denoising¶

../_images/sphx_glr_plot_channel_epochs_image_thumb.png

Visualize channel over epochs as an image¶

../_images/sphx_glr_plot_evoked_whitening_thumb.png

Whitening evoked data with a noise covariance¶

../_images/sphx_glr_plot_topo_compare_conditions_thumb.png

Compare evoked responses for different conditions¶

../_images/sphx_glr_plot_compute_csd_thumb.png

Compute a cross-spectral density (CSD) matrix¶

../_images/sphx_glr_plot_compute_source_psd_epochs_thumb.png

Compute Power Spectral Density of inverse solution from single epochs¶

../_images/sphx_glr_plot_cluster_stats_evoked_thumb.png

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

../_images/sphx_glr_plot_fdr_stats_evoked_thumb.png

FDR correction on T-test on sensor data¶

../_images/sphx_glr_plot_sensor_permutation_test_thumb.png

Permutation T-test on sensor data¶

../_images/sphx_glr_plot_decoding_spatio_temporal_source_thumb.png

Decoding source space data¶

../_images/sphx_glr_plot_decoding_time_generalization_conditions_thumb.png

Decoding sensor space data with generalization across time and conditions¶

../_images/sphx_glr_plot_decoding_unsupervised_spatial_filter_thumb.png

Analysis of evoked response using ICA and PCA reduction techniques¶

../_images/sphx_glr_plot_decoding_xdawn_eeg_thumb.png

XDAWN Decoding From EEG data¶

../_images/sphx_glr_plot_ems_filtering_thumb.png

Compute effect-matched-spatial filtering (EMS)¶

../_images/sphx_glr_plot_linear_model_patterns_thumb.png

Linear classifier on sensor data with plot patterns and filters¶

../_images/sphx_glr_plot_cwt_sensor_connectivity_thumb.png

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

../_images/sphx_glr_plot_mixed_source_space_connectivity_thumb.png

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

../_images/sphx_glr_plot_mne_inverse_coherence_epochs_thumb.png

Compute coherence in source space using a MNE inverse solution¶

../_images/sphx_glr_plot_mne_inverse_connectivity_spectrum_thumb.png

Compute full spectrum source space connectivity between labels¶

../_images/sphx_glr_plot_mne_inverse_label_connectivity_thumb.png

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

../_images/sphx_glr_plot_mne_inverse_psi_visual_thumb.png

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

../_images/sphx_glr_plot_sensor_connectivity_thumb.png

Compute all-to-all connectivity in sensor space¶

../_images/sphx_glr_plot_compute_mne_inverse_epochs_in_label_thumb.png

Compute MNE-dSPM inverse solution on single epochs¶

../_images/sphx_glr_plot_lcmv_beamformer_thumb.png

Compute LCMV beamformer on evoked data¶

../_images/sphx_glr_plot_lcmv_beamformer_volume_thumb.png

Compute LCMV inverse solution in volume source space¶

../_images/sphx_glr_plot_tf_dics_thumb.png

Time-frequency beamforming using DICS¶

../_images/sphx_glr_plot_tf_lcmv_thumb.png

Time-frequency beamforming using LCMV¶

../_images/sphx_glr_plot_visualize_raw_thumb.png

Visualize Raw data¶

../_images/sphx_glr_plot_epochs_to_data_frame_thumb.png

Export epochs to Pandas DataFrame¶

../_images/sphx_glr_plot_visualize_epochs_thumb.png

Visualize Epochs data¶

../_images/sphx_glr_plot_eeg_erp_thumb.png

EEG processing and Event Related Potentials (ERPs)¶

../_images/sphx_glr_plot_stats_cluster_time_frequency_thumb.png

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

../_images/sphx_glr_plot_stats_spatio_temporal_cluster_sensors_thumb.png

Spatiotemporal permutation F-test on full sensor data¶

../_images/sphx_glr_plot_stats_cluster_spatio_temporal_thumb.png

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

../_images/sphx_glr_plot_stats_cluster_spatio_temporal_repeated_measures_anova_thumb.png

Repeated measures ANOVA on source data with spatio-temporal clustering¶

../_images/sphx_glr_plot_stats_cluster_time_frequency_repeated_measures_anova_thumb.png

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