mne.find_events#

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.

Parameters:

rawRaw object: The raw data.
stim_channelNone | str | list of str: 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.
consecutivebool | ‘increasing’: 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.
min_durationfloat: The minimum duration of a change in the events channel required to consider it as an event (in seconds).
shortest_eventint: Minimum number of samples an event must last (default is 2). If the duration is less than this an exception will be raised.
maskint | None: The value of the digital mask to apply to the stim channel values. If None (default), no masking is performed.
uint_castbool: If True (default False), do a cast to uint16 on 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 --32 in MNE-C.

New in v0.12.
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 v0.13.
initial_eventbool: 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 v0.16.
verbosebool | str | int | None: 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.

Returns:

eventsndarray of int, shape (n_events, 3): The identity and timing of experimental events, around which the epochs were created. See events for more information.

See also

find_stim_steps: Find all the steps in the stim channel.
read_events: Read events from disk.
write_events: Write events to disk.

Notes

Warning

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.

Examples

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.:

'0000111' <- trigger value
'0100101' <- mask
----------------
'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.:

'0000111' <- trigger value
'0100101' <- mask
----------------
'0000010'

Examples using `mne.find_events`#

Overview of MEG/EEG analysis with MNE-Python

Parsing events from raw data

Getting started with mne.Report

Working with CTF data: the Brainstorm auditory dataset

Working with events

Handling bad channels

Filtering and resampling data

Repairing artifacts with regression

Repairing artifacts with ICA

Repairing artifacts with SSP

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

Visualizing epoched data

The Evoked data structure: evoked/averaged data

Plotting whitened data

The Spectrum and EpochsSpectrum classes: frequency-domain data

Frequency and time-frequency sensor analysis

Computing a covariance matrix

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

Decoding (MVPA)

Corrupt known signal with point spread

DICS for power mapping

Compare simulated and estimated source activity

Generate simulated raw data

Generate simulated source data

Transform EEG data using current source density (CSD)

Show EOG artifact timing

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

Kernel OPM phantom data

Optically pumped magnetometer (OPM) data

From raw data to dSPM on SPM Faces dataset

mne.find_events#

Examples using mne.find_events#

Examples using `mne.find_events`#