- mne.find_events(raw, stim_channel=None, output='onset', consecutive='increasing', min_duration=0, shortest_event=2, mask=None, uint_cast=False, mask_type='and', initial_event=False, verbose=None)[source]#
Find events from raw file.
See Parsing events from raw data and Working with events for more information about events.
The raw data.
Name of the stim channel or all the stim channels affected by triggers. If None, the config variables ‘MNE_STIM_CHANNEL’, ‘MNE_STIM_CHANNEL_1’, ‘MNE_STIM_CHANNEL_2’, etc. are read. If these are not found, it will fall back to ‘STI 014’ if present, then fall back to the first channel of type ‘stim’, if present. If multiple channels are provided then the returned events are the union of all the events extracted from individual stim channels.
- output‘onset’ | ‘offset’ | ‘step’
Whether to report when events start, when events end, or both.
If True, consider instances where the value of the events channel changes without first returning to zero as multiple events. If False, report only instances where the value of the events channel changes from/to zero. If ‘increasing’, report adjacent events only when the second event code is greater than the first.
The minimum duration of a change in the events channel required to consider it as an event (in seconds).
Minimum number of samples an event must last (default is 2). If the duration is less than this an exception will be raised.
The value of the digital mask to apply to the stim channel values. If None (default), no masking is performed.
If True (default False), do a cast to
uint16on the channel data. This can be used to fix a bug with STI101 and STI014 in Neuromag acquisition setups that use channel STI016 (channel 16 turns data into e.g. -32768), similar to
mne_fix_stim14 --32in MNE-C.
New in version 0.12.
- 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.
If True (default False), an event is created if the stim channel has a value different from 0 as its first sample. This is useful if an event at t=0s is present.
New in version 0.16.
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.
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.
Find all the steps in the stim channel.
Read events from disk.
Write events to disk.
If you are working with downsampled data, events computed before decimation are no longer valid. Please recompute your events after decimation, but note this reduces the precision of event timing.
Consider data with a stim channel that looks like:
[0, 32, 32, 33, 32, 0]
By default, find_events returns all samples at which the value of the stim channel increases:
>>> print(find_events(raw)) [[ 1 0 32] [ 3 32 33]]
If consecutive is False, find_events only returns the samples at which the stim channel changes from zero to a non-zero value:
>>> print(find_events(raw, consecutive=False)) [[ 1 0 32]]
If consecutive is True, find_events returns samples at which the event changes, regardless of whether it first returns to zero:
>>> print(find_events(raw, consecutive=True)) [[ 1 0 32] [ 3 32 33] [ 4 33 32]]
If output is ‘offset’, find_events returns the last sample of each event instead of the first one:
>>> print(find_events(raw, consecutive=True, ... output='offset')) [[ 2 33 32] [ 3 32 33] [ 4 0 32]]
If output is ‘step’, find_events returns the samples at which an event starts or ends:
>>> print(find_events(raw, consecutive=True, ... output='step')) [[ 1 0 32] [ 3 32 33] [ 4 33 32] [ 5 32 0]]
To ignore spurious events, it is also possible to specify a minimum event duration. Assuming our events channel has a sample rate of 1000 Hz:
>>> print(find_events(raw, consecutive=True, ... min_duration=0.002)) [[ 1 0 32]]
For the digital mask, if mask_type is set to ‘and’ it will take the binary representation of the digital mask, e.g. 5 -> ‘00000101’, and will allow the values to pass where mask is one, e.g.:
7 '0000111' <- trigger value 37 '0100101' <- mask ---------------- 5 '0000101'
For the digital mask, if mask_type is set to ‘not_and’ it will take the binary representation of the digital mask, e.g. 5 -> ‘00000101’, and will block the values where mask is one, e.g.:
7 '0000111' <- trigger value 37 '0100101' <- mask ---------------- 2 '0000010'
Overview of MEG/EEG analysis with MNE-Python
Getting started with mne.Report
Working with CTF data: the Brainstorm auditory dataset
Repairing artifacts with regression
Preprocessing optically pumped magnetometer (OPM) MEG data
Working with eye tracker data in MNE-Python
The Epochs data structure: discontinuous data
Regression-based baseline correction
The Evoked data structure: evoked/averaged data
The Spectrum and EpochsSpectrum classes: frequency-domain data
Frequency and time-frequency sensor analysis
Source localization with MNE, dSPM, sLORETA, and eLORETA
Source reconstruction using an LCMV beamformer
EEG source localization given electrode locations on an MRI
Brainstorm Elekta phantom dataset tutorial
4D Neuroimaging/BTi phantom dataset tutorial
Non-parametric 1 sample cluster statistic on single trial power
Corrupt known signal with point spread
Compare simulated and estimated source activity
Generate simulated source data
Transform EEG data using current source density (CSD)
Reduce EOG artifacts through regression
Maxwell filter data with movement compensation
Plot sensor denoising using oversampled temporal projection
Compute power and phase lock in label of the source space
Compute source power spectral density (PSD) in a label
Compute induced power in the source space with dSPM
Explore event-related dynamics for specific frequency bands
Regression on continuous data (rER[P/F])
Representational Similarity Analysis
Compute source level time-frequency timecourses using a DICS beamformer
Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute source power estimate by projecting the covariance with MNE
Computing source timecourses with an XFit-like multi-dipole model
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Compute cross-talk functions for LCMV beamformers
Brainstorm raw (median nerve) dataset
Optically pumped magnetometer (OPM) data
From raw data to dSPM on SPM Faces dataset