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SPM for programmers » Manual high-pass filtering
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Tips Martin Pyka on 25 Aug 2011 10:48 am

Manual high-pass filtering

The high-pass filter can have a great effect on the subsequent parameter estimation. To explore, how a certain setting (default is 128) influences the time course of the regressor you can use the spm_filter function. First, we build a HRF-regressor:

reg = [zeros(10,1); ones(10,1); zeros(30,1); ones(10,1); zeros(30,1); ones(10,1); zeros(30,1)];

bf = spm_get_bf %TR is 2.2 in our case
U.u = reg;
U.name = {'reg'};
% create the regressor convolved with the HRF
convreg = spm_Volterra(U, bf.bf);

In your SPM.mat you can find this kind of regressors under SPM.xX.X.

spm_filter is a function that either creates the low frequencies to be removed or removes those frequencies from the time course. The input variable K defines all necessary parameters:

K.RT = 2.2;
K.row = 1:130; % 130 corresponds to the length of convreg
K.HParam = 128; % cut-off period in seconds

Here, I defined K.row as it also happens internally. But I guess, ones(1,130) should also work, as it seems that K.row just serves as a mask. Now, we compute the low frequencies that should be removed from convreg.

nK = spm_filter(K);

nK.X0 contains the frequencies that can be removed. A second call of spm_filter removes these frequencies.

Y = spm_filter(nK, convreg);

In SPM.mat these regressors are stored in SPM.xX.nKX and are depicted in the design matrix. As you can see, the high-pass filter cut-off period can greatly affect the regressors that are used for the parameter estimation:

K.HParam = 256;
figure; plot(spm_filter(spm_filter(K), convreg));

K.HParam = 64;
figure; plot(spm_filter(spm_filter(K), convreg));

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