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:
- filename
str
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 version 0.13.
- return_event_id
bool
If True,
event_id
will be returned. This is only possible for-annot.fif
files produced with MNE-Cmne_browse_raw
.New in version 0.20.
- verbose
bool
|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.
- filename
- Returns:
- events
array
ofint
, 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_id
dict
Dictionary of
{str: int}
mappings of event IDs.
- events
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
#
Rejecting bad data spans and breaks
The Epochs data structure: discontinuous data
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