[Mne_analysis] missing event when importing .edf data

Alan leggitta3 at gmail.com
Fri Feb 14 17:20:59 EST 2014
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Hi Teon,

I haven't really been using mne.find_events on RawEDF objects, since I've
been using the command line tools and exporting text files of the events. I
just ran find_events on a sample RawEDF object and compared the output to
one of the text files. Below is a table, giving the number of each event
types across both methods. In this experiment, the events I'm interested in
are 4, 8, and 128, so both methods produce the same 'relevant' output, but
running find_events on the RawEDF produced more events. I'll have to double
check to see which event ids are actually programmed into biosemi.

ID        find_events    command line
    0:    0                    1
    1:    13                  13
    2:    22                  22
    3:    3                    0
    4:    556                556
    6:    9                    0
    7:    107                0
    8:    58                  58
  12:    530                23
  14:    213                0
  15:    51                  0
128:    100                100
129:    7                    0
Total:    1669            773




On Fri, Feb 14, 2014 at 12:15 PM, Teon Brooks <teon at nyu.edu> wrote:

> Hi Alan,
>
> Are you currently experiencing any of the masking problems with
> mne-python? If so, I could take a look at it and see if that could be
> resolved.
>
>
>
> Best,
> --
> teon
>
>
>
> On Fri, Feb 14, 2014 at 1:49 PM, Alan <leggitta3 at gmail.com> wrote:
>
>> Hi Alexandra,
>>
>> I've also run into mis-labelled events in the past, and I've found that
>> some errors could be resolved by using a "mask" in the mne command line
>> tools. Do you have access to the command line tools? If so I would read
>> this section of the manual<http://martinos.org/mne/stable/manual/convert.html#behiaadg>,
>> on importing from other systems. It sounds like you might need to apply a
>> mask of 255, (0xff).
>>
>> Hope this helps.
>>
>> -Alan
>>
>>
>> On Thu, Feb 13, 2014 at 12:35 PM, Alexandre Gramfort <
>> alexandre.gramfort at telecom-paristech.fr> wrote:
>>
>>> hi Alexandra,
>>>
>>> I have no experience with what you're trying to do.
>>>
>>> When you say "epocked" you mean multiple trials ? evoked / averaged data?
>>>
>>> read_raw_edf is meant to read continuous raw data.
>>>
>>> Also you can have a look at the channel STI014 to see what can go wrong.
>>>
>>> You may want to paste the commands you typed to do what you want. It
>>> might clarify things on my side.
>>>
>>> Best,
>>> Alex
>>>
>>>
>>>
>>> On Thu, Feb 13, 2014 at 5:28 PM,  <acorneyllie at olfac.univ-lyon1.fr>
>>> wrote:
>>> > Hi mne users!
>>> >
>>> > I'm trying to import epocked data from EEGlab (already preprocessed) in
>>> > mne. I used the .edf conversion and the read_raw_edf function to
>>> transform
>>> > .set and .fdt to fif format.
>>> > The data import appears good to me (channels  and samples are all here
>>> and
>>> > correct) and events are syncronised.
>>> > However, the number of event readen from raw data is less than the real
>>> > number.
>>> > (using events = mne.find_events(raw, stim_channel='STI 014'))
>>> >
>>> > Moreover, the event id are '255' instead of 1,2,3 and 4 (the triggers
>>> > values). I changed it manualy and then realized that all the event
>>> > associated to trigger '1' are missing.
>>> >
>>> > I tried on EEGlab to re-import the .edf file to look if all of the
>>> > triggers are well exported, and it seems to be good.
>>> >
>>> > So, what could be the source of this miss of event readen from raw
>>> data ?
>>> > Is there a way to add the missing events properly ?
>>> > And finaly, is there a simplier process to import epocked data from
>>> EEGlab ?
>>> >
>>> > Thanks for your help,
>>> > Best.
>>> > --
>>> > Alexandra Corneyllie
>>> > Equipe Cognition Auditive et Psychoacoustique
>>> > CNRS UMR 5292 - INSERM U1028
>>> > Centre de Recherche en Neurosciences de Lyon
>>> > Fixe : + 33 04.37.28.74.90
>>> > Mobile : + 33 06.32.55.78.62
>>> > e-mail : acorneyllie at olfac.univ-lyon1.fr
>>> >
>>> >
>>> >
>>> >
>>> >
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