[Eeglablist] How can I get the power of each frequency band?
#SHAN XIN#
SHAN0055 at e.ntu.edu.sg
Thu Apr 23 09:17:05 PDT 2015
Dear Makoto,
Thank you so much for the link!! I still have two questions:
(1) Since dB is the log ratio of two same quantities, so in the spectopo plot 10log10(uV^2/Hz) , does EEGlab implicitly assume that the reference level is 1uV^2 ?
(2) The script line "deltaPower = mean(10.^(spectra(deltaIdx)/10)); " gives uV^2/Hz, which is the mean value of densities. If I hope to get the total power within alpha band, what script should I write? Intuitively it seems something like: total power=sum(10.^(spectra(alphaIdx)/10)*(length of each discrete frequency)). Is this concept correct? If it is correct, then what should be the value for "length of each discrete frequency" (say for 128Hz sampling rate)? If not, then is there actually any method to get the total power?
Best wishes,
SHAN Xin
________________________________
From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Sent: Wednesday, 22 April 2015 2:07 AM
To: #SHAN XIN#
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] How can I get the power of each frequency band?
Dear Shan,
> Why divided by Hz?
Here is (esoteric) discussion about the related topic.
http://sccn.ucsd.edu/pipermail/eeglablist/2014/008224.html
Makoto
On Mon, Apr 20, 2015 at 3:58 AM, #SHAN XIN# <SHAN0055 at e.ntu.edu.sg<mailto:SHAN0055 at e.ntu.edu.sg>> wrote:
Hi Makoto,
Oh I didn't realize the scale on the figure might already in dB...is it? I know dB is defined as log ratio, but uV^2/Hz does not seem to be a ratio since the units (volt square and hz) do not match... Also, do you therefore mean that the definition of absolute power is uV^2/Hz?
I've gone through the related topics in archives and still don't understand these... I also search on Google but still cannot find a clear explanation. Why divided by Hz?
Thank you so much!!
Shan Xin
#SHAN XIN# <SHAN0055 at e.ntu.edu.sg<mailto:SHAN0055 at e.ntu.edu.sg>> 於 Apr 20, 2015 17:29 寫道:
Dear Makoto,
I have a doubt for the power unit. I read the "help spectopo" in Matlab, for the variable "spectra":
Outputs:
spectra = (nchans,nfreqs) power spectra (mean power over epochs), in dB
freqs = frequencies of spectra (Hz)
If the unit of variable "spectra" is in dB unit already, then when computing the absolute power, do we still need to do the transformation as you suggested (i.e. 10.^(spectra(deltaIdx)/10))?
I know when plotting the figure, the spectrum is plotted in log scale. But if the unit of spectra is in dB, then there's no need to transform, right?
I don't know if this is correct... Could you look more into it? Thank you so much!!
Shan Xin
________________________________
From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu<mailto:mmiyakoshi at ucsd.edu>>
Sent: Thursday, 16 April 2015 11:11 AM
To: #SHAN XIN#
Cc: eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] How can I get the power of each frequency band?
Dear Shan,
> I'm not very proficient in Matlab programming...
This could be a concern but you can try!
%%%%%%%%%%%%%%%%%%%
% for your epoched data, channel 2
[spectra,freqs] = spectopo(EEG.data(2,:,:), 0, EEG.srate);
% delta=1-4, theta=4-8, alpha=8-13, beta=13-30, gamma=30-80
deltaIdx = find(freqs>1 & freqs<4);
thetaIdx = find(freqs>4 & freqs<8);
alphaIdx = find(freqs>8 & freqs<13);
betaIdx = find(freqs>13 & freqs<30);
gammaIdx = find(freqs>30 & freqs<80);
% compute absolute power
deltaPower = mean(10.^(spectra(deltaIdx)/10));
thetaPower = mean(10.^(spectra(thetaIdx)/10));
alphaPower = mean(10.^(spectra(alphaIdx)/10));
betaPower = mean(10.^(spectra(betaIdx)/10));
gammaPower = mean(10.^(spectra(gammaIdx)/10));
%%%%%%%%%%%%%%%%%
Makoto
On Tue, Apr 14, 2015 at 6:23 AM, #SHAN XIN# <SHAN0055 at e.ntu.edu.sg<mailto:SHAN0055 at e.ntu.edu.sg>> wrote:
Hi EEGLab team,
Does EEGlab has plugin or script so that I can get the power of individual band (e.g. power of alpha band or beta band) from your computation, instead of just a continuous line in the power spectrum figure? I'm not very proficient in Matlab programming...thank you so much!!
Best wishes,
SHAN Xin
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--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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