[Eeglablist] Pre-processing pipeline questions

[Makoto Miyakoshi]

Dear Mina,

> 1) Removing bad channels *before* epoching: During examination of the
continuous signal, if the parts that consist the epochs of interest are
quite ok and only need to reject less than 5-10% of the epochs for bad
channels, would it be ok to avoid interpolation, reject bad epochs, and
*then* re-reference the good epochs that are left?

re-referencing to average after removing bad channels. Epoch rejection does
not matter (unless you have huge correlated noise that can be reduced by
average referencing, which maybe very rare anyways)

> 2) Re-referencing to the common average: The data acquisition system was
referencing to the common average online for some (unfortunate I guess)
reason. Supposing I can extract enough epochs without bad electrode
contamination, should I re-reference and if yes to what formula?

No. And if you want, you can reference to average for infinite amount of
times because from the second time your channel average is 0.

> 3) Regarding the baseline subtraction: I am oriented towards single trial
brain connectivity analysis. Should I do baseline subtraction?

Yes why not, but it may depend on your experimental design.

> The experiment has around 2 sec fixation cross and then the stimulus
appears for 1 minute during which the subjects produce responses in
self-paced manner. Whenever they come up with a response the press the
button and say it. The epochs I will analyse are -1250 to -250 ms relative
to the button press. If I should do baseline correction should I use the
fixation cross interval or some resting eyes open intervals collected at
the beginning of the experiment.

Epoch -1 to 2 seconds for the later ERSP/ITC purpose. Allow overlap of
epochs, it's ok.

> 4) ICA: I did some tests and ended up with AMICA for EOG artifact
removal, and then subsequently I used AAR to remove EMG artifact. Do you
think this a proper way to deal with the artifacts?

I don't know what AAR is. We always recommend that you analyze ICs and not
channel signals.

Makoto

On Fri, Sep 11, 2015 at 4:32 AM, Mina Marmpena <mina.marmpena at gmail.com>
wrote:

> Dear EEGLAB list,
>
> I have some EEG data from a cognitive experiment which I will analyze for
> my master thesis. I am not experienced with preprocessing and after I read
> Makoto's guidelines and other information in the internet I would like to
> ask for your help in some dilemmas I face.
>
> 1) Removing bad channels *before* epoching: During examination of the
> continuous signal, if the parts that consist the epochs of interest are
> quite ok and only need to reject less than 5-10% of the epochs for bad
> channels, would it be ok to avoid interpolation, reject bad epochs, and
> *then* re-reference the good epochs that are left?
>
> 2) Re-referencing to the common average: The data acquisition system was
> referencing to the common average online for some (unfortunate I guess)
> reason. Supposing I can extract enough epochs without bad electrode
> contamination, should I re-reference and if yes to what formula?
>
> 3) Regarding the baseline subtraction: I am oriented towards single trial
> brain connectivity analysis. Should I do baseline subtraction? The
> experiment has around 2 sec fixation cross and then the stimulus appears
> for 1 minute during which the subjects produce responses in self-paced
> manner. Whenever they come up with a response the press the button and say
> it. The epochs I will analyse are -1250 to -250 ms relative to the button
> press. If I should do baseline correction should I use the fixation cross
> interval or some resting eyes open intervals collected at the beginning of
> the experiment.
>
> 4) ICA: I did some tests and ended up with AMICA for EOG artifact removal,
> and then subsequently I used AAR to remove EMG artifact. Do you think this
> a proper way to deal with the artifacts?
>
> Please take into consideration it's my first effort in the field and
> forgive my ignorance.
>
> Thank you very much,
> Mina
>
>
>
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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