[Eeglablist] Apparent inconsistencies in study power spectrum computations

[Makoto Miyakoshi]

Dear Raza,

This sounds somehow familiar to me. Isn't it something like the timing of
log transform? Could you think of any possibility about it? Sorry I'm not
exactly giving you the answer.

Makoto

On Sat, Apr 11, 2015 at 2:04 AM, Raza Ward <etcwar_d at hotmail.com> wrote:

> Dear Eeglablist,
>
> I'm presently running a study in eeglablist (independent variables: three
> task/epoch types within each dataset, two conditions per subject with a
> dataset each), and trying to extract the power spectrum (pre)computations
> eeglab made for my study design as numerical tables. Opening the .specdat
> files in matlab appears achieve this; however, I'm running into a worrisome
> apparent inconsistency: when I precompute the spectral data at default
> settings ("specmode" "fft" "logtrials" "off")  using the "save single trial
> measures" function, for every channel/component it faithfully produces a
> row for every epoch with columns for every 1/3th of a Hz across the
> frequency spectrum; and when I don't use the "save single trial measures",
> it outputs a single row with supposedly combined values for every 1/3th of
> a Hz instead. The problem is this: the combined values are not in fact the
> average of the individual epoch values, and while the average of all epochs
> and the outputted combined value are reasonably predictive of each other,
> their proportionality varies by as much as 0.35. Moreover, when I change
> the study design to use paired rather than unpaired statistics and tag
> "save single trial measures", it outputs yet another, different, power
> value for every epoch, the averages of which are again correlated but not
> fully consistent with the previously described values.
>
> Where are these differences coming from, and what do I need to do to get
> my hands on the "true" average power for all epochs of a single type from
> within a single dataset?
>
> My gratitude and best regards,
>
> ~ Raza
>
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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