[Eeglablist] Epoch Length, and Normalization

[Makoto Miyakoshi]

Dear Joseph,

> 1. What epoch lengths do I use for both blocks? Are -500ms to 3000ms, and
-500 to 5000ms okay for block 1 and block 2 respectively?

No, make them equal lengths.

> 2. For time-frequency transforms, do I need to subtract the baseline from
the data before I do the transformation?

Not necessary if your data is high-pass filtered properly. ERP baseline
subtraction is different from ERSP baseline subtraction too.

> 3. As a follow up from question 2, do I perform ICA before the
subtraction?

This does not matter, but after ICA sounds better. If you do, make sure
that you use long baseline (0.5-1 sec; if short baseline as pre-ICA
process, it'll compromise ICA performance by Groppe et al).

> 4. What is the effect of normalizing the data before computing the power
in each channel?

Do not normalize data for spectrum analysis. You'll lose information.

Makoto



On Tue, Jun 28, 2016 at 12:09 PM, Joseph Nuamah <jknuamah at aggies.ncat.edu>
wrote:

> Dear Everyone,
>
> I have a data set from an experiment that had two blocks of trials.
>
> Each block had 30 trials. Whereas each trial in block 1 had a duration of
> 6000ms, each trial in block 2 had a duration of 60 000 ms. The ISI for both
> blocks was 3000 ms.
>
> The sampling frequency was 256Hz. A trial ended when a participant clicked
> the mouse button. I found out that for both cases, participants clicked the
> mouse before elapse of the stipulated trial duration.
>
> My questions are:
>
> 1. What epoch lengths do I use for both blocks? Are -500ms to 3000ms, and
> -500 to 5000ms okay for block 1 and block 2 respectively?
>
> 2. For time-frequency transforms, do I need to subtract the baseline from
> the data before I do the transformation?
>
> 3. As a follow up from question 2, do I perform ICA before the subtraction?
>
> 4. What is the effect of normalizing the data before computing the power
> in each channel?
>
> Thanks.
>
> Joseph.
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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