[Eeglablist] Preprocessing suggestions

Gramann, Klaus klaus.gramann at tu-berlin.de
Wed May 19 22:32:10 PDT 2021


Dear Chiara,



I agree with Stefan that many factors come into play regarding your data quality as well as the subsequent quality of the ICA decomposition.

We are having a workshop on Mobile Brain/Body Imaging next week from May 25 to May 27 where we discuss preprocessing options for MoBI data and introduce a pipeline that we developed in the Berlin Mobile Brain/Body Imaging labs.

If you are interested you can join us next week. 

The workshop is free and will also be recorded for later.



You find more information here:

https://urldefense.com/v3/__https://blogs.tu-berlin.de/bpn_bemobil/mobi-workshop/__;!!Mih3wA!SXqWZUz2R9CJA79e8kIHtUraAT7bxp-ut9JBn-l3YVdvWD1BDqjSrD-wSW-Cl9V-bpawSQ$ 



Best,

Klaus



-----Original message-----
From: Stefan Debener via eeglablist <eeglablist at sccn.ucsd.edu>
Sent: Thursday May 20th, 2021 0:50
To: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] Preprocessing suggestions


Hi Chiara,

It is difficult to give precise recommendations. How "noisy" EEG data
are during physical activity such as cycling will depend on the
recording conditions, the hardware used, and the participants, of
course. Additional information like cadence event markers, power output,
IMUs, or video footage during data acquisition would help guiding the
analysis. Moreover, it matters whether you have stationary cycling or
real cycling data, whether wireless EEG with a head-mounted amplifier
was used (hopefully), what kind of cap and electrodes, etc. In any case,
ICA benefits from detrending (approx. 1 Hz high-pass) and pruning (try
to avoid manual pruning) before decomposing the data. Moreover, it does
not make too much sense to me to determine the number of components to
be removed beforehand - but it makes sense to use semi-automatic tools
for component selection (depending on your experience). to get started,
I suggest you check out Makotos pre-processing pipeline...

In order to minimize the risk of circular analysis
(https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/19396166/__;!!Mih3wA!UWFLW_EAAfrgahU2qSH_ZvoiHLvG4cpP1yIyj_XhMGAr88OuypST0Q2F2wd2pipeOMMS6A$ ) you may want to take a few
pilot datasets, develop your optimal processing pipeline on these few
datasets only and then apply this pipeline to all other datasets.

Hope this helps.

Best,

Stefan

On 18.05.21 16:27, Chiara Gattoni via eeglablist wrote:
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>
> Hi all,
>
> I am in the process of starting the preprocessing of my EEG data which
> have been collected during a cycling task of 40 minutes duration under
> different conditions. Participants were cycling at a constant intensity
> (moderate intensity domain).
>
> This is my first time analysing EEG during physical activity and one of the
> first times analysing EEG data in general (I come from sport and exercise
> sciences).
>
> I would like to do frequency and time-frequency analyses and focus on
> alpha, beta and theta bands. No events have been measured and I was
> thinking of dividing the 40-min EEG data collected into 5-minute time
> intervals.
> Have you got any suggestions to give me regarding the preprocessing part?
> Data collected are going to be more noisy than resting EEG for sure, so if
> you have any specific tips it would be helpful.
>
> These are the steps I was thinking to follow:
>
> 1) Baseline removal;
> 2) Bandpass filter (0.5 - 30 Hz);
> 3) Downsampling (128 Hz; sampling rate was 500 Hz);
> 4) Removing artifacts manually;
> 5) ICA;
> 6) Using SASICA for removing bad components (2 maximum);
> 7) Epoching (4 seconds overlap).
>
> Would you please let me know if I am going in the right direction?
>
> Many thanks in advance,
>
> Chiara
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--
Prof. Dr. Stefan Debener
Department of Psychology
Neuropsychology	Lab
University of Oldenburg
D-26111 Oldenburg
Germany

Office: A7 0-038
Phone: +49-441-798-4271
Fax:   +49-441-798-5522
Email: stefan.debener at uni-oldenburg.de

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