[Mne_analysis] Evoked error

[Dan McCloy]

        External Email - Use Caution        

The answer is exactly the same as [the last time you asked](https://mail.nmr.mgh.harvard.edu/pipermail//mne_analysis/2020-April/006681.html): read [this tutorial section](https://mne.tools/dev/auto_tutorials/preprocessing/plot_20_rejecting_bad_data.html#rejecting-epochs-based-on-channel-amplitude). In particular, I call your attention to this sentence: "The values that are appropriate are dataset- and hardware-dependent, so some trial-and-error may be necessary to find the correct balance between data quality and loss of power due to too many dropped epochs."

-- dan
Daniel McCloy
https://dan.mccloy.info
Research Scientist
Institute for Learning and Brain Sciences
University of Washington

‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Tuesday, April 14, 2020 5:37 AM, Renew Andrade <andraderenew at yahoo.com> wrote:

> Sorry I didn’t answer I had all emails in spam but I received some normally so I didn’t check for spam. Can you tell me how to know if parameters are too strict or not? How can I know how to set proper parameters? Is there any tutorial to know proper criteria?
>
> Sincerely,
> Andrade.
>
> On 3 Apr 2020, at 18:36, Dan McCloy <dan at mccloy.info> wrote:
>
>>         External Email - Use Caution
>>
>> When you create your epochs, include the parameter preload=True, then you will see in the output which epochs are getting dropped based on your reject criteria.  Probably your criteria are too stringent, that's why it says <Epochs | 0 events ...>.  Here "0 events" means all epochs were rejected.  See also epochs.plot_drop_log().
>>
>> -- dan
>> Daniel McCloy
>> https://dan.mccloy.info
>> Research Scientist
>> Institute for Learning and Brain Sciences
>> University of Washington
>>
>> ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
>> On Friday, April 3, 2020 8:57 AM, Andrade Rey René <rene.andrade at edu.uah.es> wrote:
>>
>>>         External Email - Use Caution
>>>
>>> Dear experts:
>>>
>>> I am processing egg data. I need to do epochs.average() but it says like epochs is empty and it makes no sense to do average. When I do mne.Epochs I find that not all the channels were dropped. Depending on the event_id= dict(aud=id1).
>>>
>>> Sincerely,
>>> Andrade.
>>>
>>>>>> epochs
>>> <Epochs  |   0 events (all good), -0.199219 - 0.5 sec, baseline [None, 0], ~161 kB, data loaded,
>>>  'read': 0>
>>>
>>>>>> events
>>> array([[ 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]])
>>
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