Epoching with stimulus in the first sample of raw data

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1

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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2

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

3

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

hth
d

4

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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contains patient information, please contact the Partners Compliance
HelpLine at
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but does not contain patient information, please contact the sender and
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