You are now following this Submission You will see updates in your activity feed You may receive emails, depending on your notification preferences. Spacey Spacey 5, 6 33 Reblogged this on Qingfang’s Blog and commented: Thank you for the thoughtful comments, and for the Lund reference. Instead of looping through all voxels to obtain residuals, this can be done very quickly by keeping everything in matrix form. I have noticed that the AR1 coefficient often resembles the default mode network, but not sure what it means. Yes, I think this is right, that would be equivalent to adding a constant to the entire magnitude spectrum. If it’s just about how to filter the low energy parts in the signal, could you use a lowpass filter?
Is 1 iteration a good approximation? Or a combination of approaches? Very cool, thanks for sharing! Which brings me to another question: Below are my comments: As far as ARMA modeling I have seen in seismology applications in which these models are used to simulate earthquake ground motions, however not for the purpose of making a geophone record white. Then you just need to loop over voxels to fit an AR model to each column of E. Discover Live Editor Create scripts with code, output, and formatted text in a single executable document.
It sounds the tmie to me; it might just be different nomenclature. Thank you for the thoughtful comments, and for the Lund reference. Averaging across subjects greatly improves the smoothness of the coefficient maps, which is presumably indicative of better estimation. Yes, I think this is right, that would be equivalent to adding a constant to the entire magnitude spectrum. I know that for a signal which is discrete and finite, that this division by its magnitude spectrum doesn’t make sense – although I cannot justify the real reasons why it doesn’t make sense perhaps you may help me in understanding this too.
I matlag hoping you can cross-check if my code should do what I want it to do. Once you have this covariance matrix you can compute a whitening transform in the form of a matrix to multiply the data in order to get the whitened version.
Yes you could also have a flat spectrum due to random values white noise in the time domain. Learn About Live Editor. Deries how do we fix this?
Ideally this should be done several times, pre-multiplying again by the new W at each iteration. This function applies whitening to a given signal within jatlab defined frequency band. This will in turn broaden the band of noise signal in the cross-correlation and combat degradation caused by monochromatic persistent sources.
matlab | prewhitening a signal in matlab
If you make the magnitude perfectly flat, then after inverse FFT you would have an infinite impulse Dirac Delta function in the time lrewhitening. How to ‘whiten’ a time domain signal? Encorporated in this problem of prewhitening, shouldnt I be dividing by the smoothed spectrum, then multiplying the magnitude with a Blackman and then ifft?
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prewhitening a signal in matlab
Interestingly, the way autocorrelation is estimated has a large influence on the results, at least in single-subject studies. However, most researchers never do this substitution of the spectrum by a constant, but instead divide it by a smoothed magnitude prewhitneing I have tried to do in my code.
Moreover, the recorded time series often has peaks at specific frequencies which overwhelm the rest of the frequencies. Or perhaps they dont do it because of another reason again I suspect?
I chose to do this by looping through voxels and computing W for each voxel.
How to efficiently prewhiten fMRI timeseries the “right” way – Mandy Mejia
Choose a web site to get translated content where available and see local events and offers. I suspect they dont substitute by a constant because that would matlah changing the data too much, ie one would loose too much information by changing completely the spectrum instead of quieting the undesired overshoots in a frequency spectrum.
For example, what is the length of the power transfer function of X, if x[n] is of length N? The cross-correlation can look like a triangle, or some other godforsaken shape.
The context here is the following: Robotbugs Robotbugs 2. Due to this, it becomes hard to find the peak of the cross-correlation signal. This article from Karjalaien et.
Seires process is simple as Fourier transforming the signal after applying Hann window, then normalizing its magnitude, and then inverse Fourier transforming it.
SPM uses a global model, which assumes that that everywhere in the brain autocorrelation is the same.