As you can read in my markdown, I want to add a feature in Neurokit package. This feature would address the fMRI noise picked up by ECG electrodes. A recent approach for gradient artifacts reduction uses higher order statistics ( central moment of distribution) to cancel their expression on the ECG signal.
I want to design a filter that normalizes the fat kurtosis (or 4th order central moment) of a data points distribution in a time window between two adjacent gradient, i.e. 100ms of data points
My questions to you are : (1) Do you know tools in scipy package that would facilitate the design of my filter ?, and (2) could you help me translate the computation of this statistic to the design of a transfer function ?
Hey instructors @PeerHerholz @bpinsard @illdopejake
As you can read in my markdown, I want to add a feature in Neurokit package. This feature would address the fMRI noise picked up by ECG electrodes. A recent approach for gradient artifacts reduction uses higher order statistics ( central moment of distribution) to cancel their expression on the ECG signal.
I want to design a filter that normalizes the fat kurtosis (or 4th order central moment) of a data points distribution in a time window between two adjacent gradient, i.e. 100ms of data points
My questions to you are : (1) Do you know tools in scipy package that would facilitate the design of my filter ?, and (2) could you help me translate the computation of this statistic to the design of a transfer function ?
Thanks you very much