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https://github.com/sccn/arfitstudio


https://github.com/sccn/arfitstudio

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README 

          
What is ARfitStudio?

-------------------



First of all, ARfit is a collection of Matlab modules developed by Tapio

Schneider and Arnold Neumaier for estimating parameters of multivariate

autoregressive (AR) models, diagnostic checking of fitted AR models, and

analyzing eigenmodes of fitted AR models. This plugin, ARfitStudio, uses

it so that one can interactively clean TMS-induced short-burst (5-10 ms)

artifacts and so on. The nature of the artifact does not matter (could

be spikes by artifact, could be zeros, NaNs, etc), as long as the

artifact duration is short and they are correctly marked. It has

strength on the following points.



-   To perform quick (e.g. smart epoching & integration to continuous

    data using mcolon(); before and after correction comparison with one

    click, etc) and intuitive (e.g. training, correction, and blending

    windows overlaid on grand-median ERPs) correction of spiky

    artifacts.

-   Immediately usable after importing the continuous data; it is the

    very first stage of the preprocess pipeline.



This plugin is dependent on ARfit and mcolon. ARfit can be installed by

installing SIFT from EEGLAB plugin manager. To use mcolon(), users

should compile the mex file.



Why ARfitStudio?

---------------



If there are spike artifacts with high amplitude, we cannot filter the

data (because spikes spread in the time domain). In certain situation,

it is more important to make them harmless for the sake of data process,

rather than recovering the underlying signals (the latter is often

impossible). This plugin provides a solution for it.



How it works

------------



It learns autoregressive model from the training period (green), which

is the past of the spike, to make a prediction to replace problematic

spike data points (red). For smooth connection, one can also set

optional blending period (light magenta) during which predicted signal

and original signals are gradually mixed with a linear slope. Thus,

*this plugin replaces the bad data points using the past information*

(therefore temporal interpolation). It does NOT recover the signal under

the noise. One can select multiple markers for correction. In the main

plot, grand-median ERPs for all the channels are shown. To save the

result, just save the EEG dataset (do not check 'restore the last data'

for this!)



Screenshots

-----------



![300px\|Figure 1. interpolateSpike:

GUI](images/Screenshot3_interpolatespike.png)



![600px\|Figure 2. interpolateSpike: Before

correction](images/Screenshot2_interpolatespike.png)



![600px\|Figure 3. interpolateSpike: After

correction](images/Screenshot1_interpolatespike.png)



Data by courtesy of Michael Borich



If you want to use ICA for the final analysis

---------------------------------------------



Because this ARfit-based interpolation is performed for the same time

points across channels, ICA cannot decompose this period. As a result,

you'll see strange spikes in IC activations (i.e., EEG.icaact) during

the correction windows. In the plots below, I show 1) ERP of IC

activation for 33ch with ARfitStudio applied on channels (left), 2) the

same but ARfitStudio applied on ICs (right). The noise was originally

present within -/+10 ms relative to latency zero.



![Onchannels.png](images/Onchannels.png)

![Onics.png](images/Onics.png)



Performing ARfit-interpolation for ICA properly is complicated.



1.  Import data.

2.  Perform ARfitStudio on channels.

3.  Preprocess the data (high-pass filter, CleanLine plugin,

    clean_rawdata plugin, average referencing, AMICA plugin, DIPFIT

    plugin, fitTwoDipoles plugin). Note that you need to exclude the

    interpolated data from ICA process (those have no multivariate

    property across channels, so not decomposable).

4.  Perform ARfitStudio on IC activations.

5.  Backproject interpolated IC activation to reconstruct channel data.



Note that the ARfitStudio is used twice, and the first application is

purposed only for various filters, not even for ICA. In this way, you

can obtain both clean channel signals and IC activations. For detail,

see 'batchDemoForICA.m' included in the download package. I recommend

you take a look because there are several cumbersome steps you have to

go through.



Note for batch users (03/11/2019 updated)

-----------------------------------------



If one wants to run the process as a batch,



1.  Use EEGLAB function pop_epoch() to crop out peri-event data points.

    Note all the negative latencies will be used as a learning period

    for ARfit.

2.  Use arfit2interpolate(). Note that the last input

    'last_n_pointsToBlend' does NOT specify the additional length to

    the data points to the main interpolation window (as the GUI

    operation indicates), but it actually specifies the last data points

    of the main interpolation window. For example, outputData =

    arfit2interpolate(EEG.data, \[80 95\], 5) means, \[80:90\] for 100%

    interpolation, and \[91:95\] is blended with \[83% 67% 50% 33% 17%\]

    interpolated data blended with \[17% 33% 50% 67% 83%\] of the

    original data, respectively.

3.  Use putBackEpoch2Continuous() to re-construct the corrected, epoched

    data to the continuous data.



Reference

---------



A. Neumaier and T. Schneider, 2001: Estimation of parameters and

eigenmodes of multivariate autoregressive models. ACM Trans. Math.

Softw., 27, 27-57.



T. Schneider and A. Neumaier, 2001: Algorithm 808: ARfit – A Matlab

package for the estimation of parameters and eigenmodes of multivariate

autoregressive models. ACM Trans. Math. Softw., 27, 58-65.



Authors: Makoto Miyakoshi and Tim Mullen. SCCN, INC, UCSD