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We confirm that using a causal filter for tractography performs much better than independent alternatives.
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The first method was frequency-dependent correction prediction, in which we used a digital causal filter to correct the site amplification for the observed waveform in the time domain.
We overcome this limitation by designing a causal filter.
This implies that a causal filter matrix must be lower triangular.
A so-called causal filter would have (n_{text{R}}).
This appears to be an artefact of the use of a causal filter.
For parametric spectral methods, the data can be modeled as the output of a discrete and causal filter whose input is white noise.
Bontempi and Meyer proposed a causal filter selection method, called min-Interaction Max-Relevance (mIMR) [ 17].
It happens that the analysis for noncausal filters is pretty much the same as that for causal filters, so we can easily relax this restriction.
However, Hoshiba (2013b) proposed a real-time convolution and deconvolution technique employing digital causal filters in the time domain, which we adopted for this study.
Correction of site amplification remains important in this scheme, and Hoshiba (2013b) proposed the use of digital causal filters that can be applied in a real-time setting for the correction.
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