[Eeglablist] Seeking power and understanding (newtimef)

[Makoto Miyakoshi]

Thanks you Johanna!

John, there is related discussion going on on the list, which is about the
similar case of using wavelet.

Makoto


On Tue, Aug 9, 2016 at 1:32 PM, Johanna Wagner <joa.wagn at gmail.com> wrote:

> Dear John, dear Makoto,
>
> the FFT output from newtimef() is normalized by window length. timefreq()
> is a function within newtimef() - newtimef() is scaling the timefreq()
> output.
> You can get all timefreq() output values via newtimef(), so I would
> recommend using the latter.
>
> John, newtimef() is dividing by the baseline (by default that is the
> period before 0 in an epoch - if not specified otherwise) - as Makoto wrote.
> Thus the power values are normalized relative to baseline and no scaling
> is needed...
>
> You do not need to use rmbase() as it removes the baseline in the
> timedomain - newtimef() is removing average power at each frequency instead.
>
> Best, johanna
>
>
>
>
> 2016-08-09 11:25 GMT-07:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
>
>> Dear John,
>>
>> > I think it is percent of baseline?
>>
>> No. If I remember correctly, it is dB change relative to the baseline
>> value. It's usually negative latency, but if data are continuous (which may
>> be your case), it should be whole-epoch value.
>>
>> > What I would really like to have is just µV^2 so I can eventually
>> compare, say, C3 between conditions and see the difference. Is that
>> possible?
>>
>> We just confirmed today that output from timefreq() is NOT normalized by
>> length of data. Which means that if your wavelet window size is longer, it
>> returns larger values. I'm not sure if this is corrected in newtimef()
>> which calls timefreq() but I guess it is so anyways. We started working on
>> this issue, so you'd better wait for our update. Otherwise, if you don't
>> mind coding yourself, you can normalize the 'tf' output from timefreq() by
>> the length of wavelet which you can obtain by using dftfilt3().
>>
>> Makoto
>>
>>
>>
>> On Sat, Aug 6, 2016 at 10:56 AM, John Johnson <john at johnjohnson.info>
>> wrote:
>>
>>> I've combed the interwebs and still haven't found a good answer.
>>>
>>> My data has a -5s rest period followed by a 20s active period (task
>>> related experiment, not event related). My pipeline includes rmbase(),
>>> among other things.
>>> Now, when I run newtimef, I can create nice plots from the ersp obtained
>>> using:
>>>
>>>     [ersp, itc, powbase, times, freqs, erspboot, itcboot, tfdata] = ...
>>>
>>>         newtimef( EEG.data(channel,:,:),...
>>>
>>>             EEG.pnts, [EEG.xmin EEG.xmax]*1000,...
>>>
>>>             EEG.srate,...
>>>
>>>             cycles,...
>>>
>>>             'plotersp', 'off',...
>>>
>>>             'plotitc', 'off',...
>>>
>>>             'freqs', [2 25],...
>>>
>>>             'scale', 'abs');
>>>
>>>
>>> I just do a mean(ersp, 2) to average across time, then mean() across
>>> subjects. The x-axis of my plot is frequency, but I don't know what the
>>> units for the y-axis would be. I think it is percent of baseline? Now if
>>> the values I'm seeing are percent of baseline, that means they're measured
>>> relative to the baseline, which is redundant because I've already removed
>>> the baseline.
>>>
>>> If I add the 'baseline',[NaN] argument to newtimef, my plots then look
>>> like ski slopes (high at low frequencies, low at higher frequencies).
>>>
>>> What I would really like to have is just µV^2 so I can eventually
>>> compare, say, C3 between conditions and see the difference. Is that
>>> possible?
>>>
>>>
>>> I've seen this question several times on the 'net, but haven't found a
>>> good answer.
>>>
>>>
>>> Thanks,
>>>
>>> John
>>>
>>>
>>>
>>>
>>> --
>>> Sent from a MacBook Pro
>>>
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>>
>>
>>
>> --
>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
>>
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>
>
>
> --
>
> Johanna Wagner, PhD
> Department of Psychology,
> University of California San Diego
> http://aronlab.org/people/
> http://scholar.google.at/citations?user=vSJYGtcAAAAJ&hl=en
>
> <http://sccn.ucsd.edu/>
>



-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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