support user-defined event fields in mne.read_epochs_eeglab()

Hi all,

EEGLAB supports adding custom field names in the event structure, on top of the default ones (type, latency, duration, urevent, epoch) : https://sccn.ucsd.edu/wiki/Chapter_03:_Event_Processing#Event_fields

For instance I use a behaviour field to store the subjects responses on each epoch.

It would be very useful if one could somehow access those custom fields in MNE (maybe the option already exists but I haven’t found it).

Best wishes, Nicolas

[larsoner]

We could add links to gists in the FAQ

[mmagnuski]

I have used something like this in the past to get info from the set file to a pandas dataframe:

import pandas as pd
from scipy.io import loadmat

def read_set_events(filename, ignore_fields=None):
	'''Open set file, read events and turn them into a dataframe
	Parameters
	----------
	filename: str
		Name of the set file to read (absolute or relative path)
	ignore_fields: list of str | None
		Event fields to ignore
	Returns
	-------
	df: pandas.DatFrame
		Events read into a dataframe
	'''
	EEG = loadmat(filename, uint16_codec='latin1',
				  struct_as_record=False, squeeze_me=True)['EEG']
	flds = [f for f in dir(EEG.event[0]) if not f.startswith('_')]
	events = EEG.event
	df_dict = dict()
	for f in flds:
		df_dict[f] = [ev.__getattribute__(f) for ev in events]
	df = pd.DataFrame(df_dict)

	# reorder columns:
	take_fields = ['epoch', 'type']
	ignore_fields = list() if ignore_fields is None else ignore_fields
	take_fields.extend([col for col in df.columns if not
					 (col in take_fields or col in ignore_fields)])
	return df.loc[:, take_fields]

it worked in my usecases - it goes through all EEG.event elements and extracts relevant fields so that every row in returned dataframe correspons to one EEG.event. You might not need the uint16_codec argument when reading your set files though. (and may have been more elegant with struct_as_record=True… 😃 )