- mne.events_from_annotations(raw, event_id='auto', regexp='^(?![Bb][Aa][Dd]|[Ee][Dd][Gg][Ee]).*$', use_rounding=True, chunk_duration=None, verbose=None)[source]#
Get events and
event_idfrom an Annotations object.
- rawinstance of
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
Noneto 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
Nonewith 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_durationis 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_durationwill not contribute events.
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.
- rawinstance of
For data formats that store integer events as strings (e.g., NeuroScan
.cntfiles), passing the Python built-in function
event_idparameter will do what most users probably want in those circumstances: return an
event_iddictionary that maps event
'1'to integer event code
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)