[Mne_analysis] epochs

[Andrade Rey René]

        External Email - Use Caution        

yes I did (epochs.plot_drop_log) but I just see a lot of columns with 100%. From where I deduce all channels are dropped. I don’t know what explanation for that.

On 14 Apr 2020, at 15:38, Eric Larson <larson.eric.d at gmail.com<mailto:larson.eric.d at gmail.com>> wrote:


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You can look at `epochs.drop_log` or `epochs.plot_drop_log` to see why. See for example:

https://mne.tools/dev/auto_tutorials/epochs/plot_10_epochs_overview.html#creating-epoched-data-from-a-raw-object

Eric


On Tue, Apr 14, 2020 at 9:37 AM Andrade Rey René <rene.andrade at edu.uah.es<mailto:rene.andrade at edu.uah.es>> wrote:

        External Email - Use Caution

Dear experts:
I am trying to run a tutorial recommended. https://mne.tools/dev/auto_tutorials/preprocessing/plot_20_rejecting_bad_data.html#rejecting-epochs-based-on-channel-amplitude

My data is not from the tutorial. It is an EEG. Also I have this outputs. Of course I use evoked = epochs.average() and it says less than one epoch. What can I do?

>>> print(events)
[[     0      0  65536]
 [ 18275      0    128]
 [ 19387      0      2]
 [ 20422      0      2]
 [ 32156      0    128]
 [ 46029      0    128]
 [ 46873      0      2]
 [ 47522      0      4]
 [ 72924      0    128]
 [ 73666      0      2]
 [ 74230      0      2]
 [ 92717      0    128]
 [ 94025      0      2]
 [ 94590      0      2]
 [108211      0    128]
 [109532      0      2]
 [110110      0      4]
 [130866      0    128]
 [131605      0      4]
 [132900      0      2]
 [156301      0    128]
 [157153      0      2]
 [157843      0      4]
 [176353      0    128]
 [177182      0      2]
 [177821      0      2]
 [191436      0    128]
 [192495      0      4]
 [193129      0      2]
 [233638      0    128]
 [234323      0      4]
 [234936      0      4]
 [248375      0    128]
 [249218      0      2]
 [249817      0      2]
 [255773      0    128]
 [256493      0      2]
 [257060      0      4]
 [286302      0    128]
 [287009      0      4]
 [287601      0      2]
 [320684      0    128]
 [321413      0      4]
 [340579      0    128]
 [341369      0      4]
 [342650      0      2]
 [383286      0    128]
 [384166      0      2]
 [384810      0      2]
 [406476      0    128]
 [407297      0      2]
 [407956      0      4]
 [409017      0      2]
 [444348      0    128]
 [445107      0      2]
 [445683      0      4]
 [446969      0      2]
 [482043      0    128]
 [482761      0      2]
 [483421      0      2]
 [521536      0    128]
 [522365      0      2]
 [523009      0      2]
 [535415      0    128]
 [536091      0      4]
 [536691      0      2]
 [573609      0    128]
 [574341      0      4]
 [574916      0      2]
 [588192      0    128]
 [588973      0      4]
 [589524      0      2]
 [611318      0    128]
 [612110      0      2]
 [612703      0      4]
 [613416      0      2]
 [634153      0    128]
 [635029      0      2]
 [635592      0      2]
 [636193      0      2]
 [662819      0    128]
 [663674      0      2]
 [664355      0      4]
 [665003      0      2]
 [677292      0    128]
 [678266      0      2]
 [678926      0      2]
 [679619      0      2]]



>>> epochs = mne.Epochs(raw, events, event_id=dict(aud=65536, vis=2, aud2=4, vis2=128), tmin=-0.2, tmax=0.5,reject=dict(eeg=100e-6), flat=dict(eeg=1e-6),preload=True)

88 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 88 events and 180 original time points ...


…


88 bad epochs dropped



Sincerely,
Andrade.
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