[Mne_analysis] Automatic artifact rejection on continuous data

[Nico Adelhöfer]

Thanks again!

Is there a way to adjust the info of my raw data? The "reject"  
parameter expects certain channel types.
I created an info instance:

     inf =  
mne.create_info(12,512,['misc','misc','eog','eog','eeg','eeg','eeg','eeg','eeg','eeg'])

but I don't know how to change this information in my raw variable. I  
tried to change it manually like

     raw.info['chs'][2]['kind'] == 'eog'

but then I get an error that this type is not found. Changing the  
pick.py file (e.g. line 46 to: elif kind == FIFF.FIFFV_EOG_CH or  
'eog') before the command above did not change the kind and is also  
not the preferable way I guess. I could not find a solution on the MNE  
website.




Quoting Mainak Jas <mainakjas at gmail.com>:

> On Wed, Jul 27, 2016 at 5:36 PM, Nico Adelhöfer <nico.adelhoefer at st.ovgu.de>
> wrote:
>
>> Thanks! I got it working so far with Jean-Remi's method, but using
>> _segment_raw gives me an attribute error.
>>
>> I read that the "reject" parameter is the peak-to-peak amplitude. Are
>> there other rejection parameters such as maximum allowed voltage step,
>> or lowest allowed activity?
>>
>
> You also have the parameter "flat" which will remove epochs with too low a
> peak-to-peak amplitude.
>
> Mainak
>
>
>> Quoting Mikołaj Magnuski <mmagnuski at swps.edu.pl>:
>>
>> > Just adding to Jean-Remi’s answer:
>> > you can quickly and easily chop data into segments using _segment_raw:
>> >
>> > from mne.epochs import _segment_raw
>> > epochs = _segment_raw(raw_eeg, segment_length=2.)
>> >
>> > This chops the data into 2-s long segments.
>> > ​
>> >
>> > 2016-07-25 15:30 GMT+02:00 JR KING <jeanremi.king at gmail.com>:
>> >
>> >> Hi Nico,
>> >>
>> >> To reject chunks of continuous data, the best is probably to first chop
>> it
>> >> into small segments, which comes down to creating adjacent events/epoch
>> and
>> >> apply your favorite rejection threshold, i.e.
>> >>
>> >> chunk_size = 1000
>> >> chunk_starts = np.arange(0, raw.n_times, chunk_size)
>> >> events = np.c_[chunk_starts, np.zeros((len(chunk_starts),
>> 2))].astype(int)
>> >> epochs = mne.Epochs(raw, events, dict(chunk=0), tmin=0, tmax=chunk_size
>> *
>> >> raw.info['sfreq'], reject=dict(mag=5e-12))
>> >>
>> >> Don't forget to check out the artefact correction tutorial:
>> >>
>> http://martinos.org/mne/stable/auto_tutorials/plot_artifacts_detection.htm
>> >>
>> >> the gallery examples:
>> >>
>> >>
>> http://martinos.org/mne/stable/auto_examples/preprocessing/plot_find_eog_artifacts.html
>> >>
>> >>
>> http://martinos.org/mne/stable/auto_examples/preprocessing/plot_find_ecg_artifacts.html
>> >>
>> >>
>> http://martinos.org/mne/stable/auto_examples/preprocessing/plot_interpolate_bad_channels.html
>> >>
>> >> as well as Jas' autoreject repository:
>> >> https://github.com/jasmainak/autoreject
>> >>
>> >> Hope that helps,
>> >>
>> >> Jean-Rémi
>> >>
>> >> On 25 July 2016 at 08:44, Nico Adelhöfer <nico.adelhoefer at st.ovgu.de>
>> >> wrote:
>> >>
>> >>> Is there a way to set parameters for automatic artifact rejection on
>> >>> continuous data in MNE? I'm especially interested in setting
>> >>> parameters such as "maximum allowed voltage step" or "maximum and
>> >>> minimum amplitude". Is there a command that achieves this on raw data?
>> >>>
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