[Mne_analysis] Epoching with stimulus in the first sample of raw data

[dgw]

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

hth
d

On Wed, Aug 24, 2016 at 3:58 PM, Mainak Jas <mainakjas at gmail.com> wrote:
> 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
>
>
>
>
> On Thu, Aug 25, 2016 at 12:43 AM, Leonardo Barbosa <lsbarbosa at gmail.com>
> wrote:
>>
>> 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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