Get events and
event_id from an Annotations object.
The raw data for which Annotations are defined.
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
‘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.
Regular expression used to filter the annotations whose
descriptions is a match. The default ignores descriptions beginning
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.
Chunk duration in seconds. If
chunk_duration is set to None
(default), generated events correspond to the annotation onsets.
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
chunk_duration will not contribute events.
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
integer event code