mne.
find_events
(raw, stim_channel=None, output=’onset’, consecutive=’increasing’, min_duration=0, shortest_event=2, mask=None, uint_cast=False, mask_type=’not_and’, verbose=None)[source]¶Find events from raw file.
Parameters: | raw : Raw object
stim_channel : None | string | list of string
output : ‘onset’ | ‘offset’ | ‘step’
consecutive : bool | ‘increasing’
min_duration : float
shortest_event : int
mask : int | None
uint_cast : bool
mask_type: ‘and’ | ‘not_and’
verbose : bool, str, int, or None
|
---|---|
Returns: | events : array, shape = (n_events, 3)
|
See also
find_stim_steps
read_events
write_events
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If you are working with downsampled data, events computed before decimation are no longer valid.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.:
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'
mne.find_events
¶