Get events and event_id
from an Annotations object.
Raw
The raw data for which Annotations are defined.
dict
| callable()
| None
| ‘auto’Can be:
dict: map descriptions (keys) to integer event codes (values). Only the descriptions present will be mapped, others will be ignored.
callable: must take a string input and return an integer event
code, or return None
to ignore the event.
None: Map descriptions to unique integer values based on their
sorted
order.
‘auto’ (default): prefer a raw-format-specific parser:
Brainvision: map stimulus events to their integer part; response
events to integer part + 1000; optic events to integer part + 2000;
‘SyncStatus/Sync On’ to 99998; ‘New Segment/’ to 99999;
all others like None
with an offset of 10000.
Other raw formats: Behaves like None.
New in version 0.18.
str
| None
Regular expression used to filter the annotations whose
descriptions is a match. The default ignores descriptions beginning
'bad'
or 'edge'
(case-insensitive).
Changed in version 0.18: Default ignores bad and edge descriptions.
If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices.
float
| None
Chunk duration in seconds. If chunk_duration
is set to None
(default), generated events correspond to the annotation onsets.
If not, mne.events_from_annotations()
returns as many events as
they fit within the annotation duration spaced according to
chunk_duration
. As a consequence annotations with duration shorter
than chunk_duration
will not contribute events.
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.
See also
Notes
For data formats that store integer events as strings (e.g., NeuroScan
.cnt
files), passing the Python built-in function int
as the
event_id
parameter will do what most users probably want in those
circumstances: return an event_id
dictionary that maps event '1'
to
integer event code 1
, '2'
to 2
, etc.
mne.events_from_annotations
#Preprocessing functional near-infrared spectroscopy (fNIRS) data
Auto-generating Epochs metadata
Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
Sleep stage classification from polysomnography (PSG) data
Plot single trial activity, grouped by ROI and sorted by RT
Compute and visualize ERDS maps
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Decoding in time-frequency space using Common Spatial Patterns (CSP)