Epoching with stimulus in the first sample of raw data

Dear MNErs,

This is my first project trying to use MNE in "real" data, and I'm trying
to move datasets from eeglab / fieldtrip to MNE

I run into some trouble trying to epoch the data.

The data is a continuous dataset with different sessions.
I have a STIM channel with numbers 1 and 2 for each type of session.
*One pulse every 2 min starting at the first sample*.
I then exported the data using

fieldtrip2fiff

making sure the channel's unit and kind were correctly set.

However, after importing the raw, when I tried to find the events to do the
epoching using this function

events = mne.find_events(raw, stim_channel='STIM')

MNE droped the first event saying

Removing orphaned offset at the beginning of the file.

I looked at the _find_events code and realized that the event pulse should
have at least one empty sample before it.
I tried to change the consecutive parameter to no avail.

What would be the best way to keep the first event / session?

I imagine it is rare to have a stimulus already in the first sample of a
continuous data, but I would like to avoid losing the first session of each
subject or recoding the stimulus at the EEGLAB/fieldtrip if possible.

Thank you,
Leonardo
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Hi Leonardo,

Since find_events returns to you a plain numpy array, a simple fix could be
to just add add your event manually to the array. Something like:

pick_stim = mne.pick_channels(raw.info['ch_names'], ['STIM'])
stim_data = raw[pick_stim][0][0]
session_id = stim_data[0]
events = np.vstack(([raw.first_samp, 0, session_id], events))

It's a bit hackish but should do your job.

Mainak

And if you have the original raw data, you could just read that
instead of performing all those conversions.

hth
d

Hello Mainak,

Thank you for the idea! I set a filter for MNE e-mails and did no see the
reply.

I end up moving the event one sample into the future and decreasing the
epochs by one sample. Since they are long sessions, it should be fine.

Best Regards,
Leonardo

PS: dgw, since the data was already visually inspected elsewhere, I was
hopping to avoid re-doing the work. So the raw data is not really an
option...

2016-08-24 14:58 GMT-05:00 Mainak Jas <mainakjas at gmail.com>:

Hi Leonardo,

Since find_events returns to you a plain numpy array, a simple fix could
be to just add add your event manually to the array. Something like:

pick_stim = mne.pick_channels(raw.info['ch_names'], ['STIM'])
stim_data = raw[pick_stim][0][0]
session_id = stim_data[0]
events = np.vstack(([raw.first_samp, 0, session_id], events))

It's a bit hackish but should do your job.

Mainak

Dear MNErs,

This is my first project trying to use MNE in "real" data, and I'm trying
to move datasets from eeglab / fieldtrip to MNE

I run into some trouble trying to epoch the data.

The data is a continuous dataset with different sessions.
I have a STIM channel with numbers 1 and 2 for each type of session.
*One pulse every 2 min starting at the first sample*.
I then exported the data using

fieldtrip2fiff

making sure the channel's unit and kind were correctly set.

However, after importing the raw, when I tried to find the events to do
the epoching using this function

events = mne.find_events(raw, stim_channel='STIM')

MNE droped the first event saying

Removing orphaned offset at the beginning of the file.

I looked at the _find_events code and realized that the event pulse
should have at least one empty sample before it.
I tried to change the consecutive parameter to no avail.

What would be the best way to keep the first event / session?

I imagine it is rare to have a stimulus already in the first sample of a
continuous data, but I would like to avoid losing the first session of each
subject or recoding the stimulus at the EEGLAB/fieldtrip if possible.

Thank you,
Leonardo

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