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:
- filenamepath-like
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).- include
int
|list
|None
A event id to include or a list of them. If None all events are included.
- exclude
int
|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.
- mask
int
|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 v0.13.
- return_event_idbool
If True,
event_id
will be returned. This is only possible for-annot.fif
files produced with MNE-Cmne_browse_raw
.New in v0.20.
- verbosebool |
str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- Returns:
See also
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
andmask_type
, seemne.find_events()
.
Examples using mne.read_events
#
Getting started with mne.Report
Rejecting bad data spans and breaks
Exporting Epochs to Pandas DataFrames
EEG analysis - Event-Related Potentials (ERPs)
Non-parametric between conditions cluster statistic on single trial power
Mass-univariate twoway repeated measures ANOVA 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
Simulate raw data using subject anatomy
Define target events based on time lag, plot evoked response
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 sensor space data with generalization across time and conditions
Analysis of evoked response using ICA and PCA reduction techniques
Compute effect-matched-spatial filtering (EMS)
Linear classifier on sensor data with plot patterns and filters
Compute MNE-dSPM inverse solution on single epochs