[Eeglablist] Premature infants preprocessing pipeline with EEGLab tools
Efthymios Papatzikis
efp331 at mail.harvard.edu
Thu Aug 8 07:27:47 PDT 2024
Dear Cedric,
Thank you for this. I will study this approach too with your code and see if that works in this context.
By "accounting for" flat lines, I mean that instead of removing them, the preprocessing pipeline should recognize and integrate these periods as an important feature of the EEG signal. Flat lines, or periods of low voltage activity (called interburst intervals or IBIs in this context), are a natural part of the EEG patterns in premature infants. These IBIs, along with bursts of higher voltage activity, provide crucial information about the discontinuous nature of neonatal brain activity. The ratio and statistical significance of these patterns, as defined by specific criteria based on the infant's postmenstrual age according to the American Clinical Neurophysiology Society Guidelines (2013; 2024), highlight the pronounced discontinuity in preterm infants, which evolves as they mature. By integrating these 'flat lines', the pre-processing pipeline can better reflect the natural EEG development and help in identifying normal vs. pathological patterns in premature infants as well as developmental activity on a stimulus. On the other hand, removing them might inadvertently strip away critical information necessary for understanding the infant’s neurodevelopmental state. Therefore, I needed a tool that can account for these (statistically study them) in the dataset (ideally before performing ICA and ASR) and decide if these impact the ICA and ASR or not - shown as artifacts; which are not.
I hope this explanation helps for the specific context.
Best,
Efthymios
> On 7 Aug 2024, at 22:03, Cedric Cannard via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
>
> Hi,
>
> What do you mean by "accounting for" flat lines if you are not removing them?
>
>
> Here is a function adapted from the original clean_channels() code of clean_rawdata to detect and remove flat segments. You can adjust to keep them or do whatever you want with them instead of rejecting. The only difficulty is finding the right threshold for your EEG system/signal, it varies depending on active/passive electrodes, amplification, highpass filtering, etc.
>
> Hope this helps.
>
> Cedric
>
> CODE:
>
> %% Detect flat lines in EEG data, excludes very small segments (< 5 samples),
> % plots them in red (superimposed on raw EEG data), and print portion of data
> % that were removed (seconds and ratio).
> %
> % Inputs:
> % EEG - EEGLAB dataset
> % flatThresh - threshold to detect flat lines (e.g., 20*eps, 0.05, 0.001)
> % vis - visualize what has been removed (true) or not (false)
> %
> % Copyright (C) - Cedric Cannard, 2023
>
> function EEG = rm_flatSegments(EEG,flatThresh,vis)
>
> % Thresholds
> if ~exist('flatThresh','var') || isempty(flatThresh)
> % flatThresh = 20*eps;
> flatThresh = 0.05;
> end
>
> % Visualization
> if ~exist('vis','var') || isempty(vis)
> vis = true;
> end
>
> % copy of original dataset for visualization
> if vis
> oriEEG = EEG;
> end
>
> nChan = EEG.nbchan;
> mask = nan(nChan,EEG.pnts+1);
> for iChan = 1:nChan
> mask(iChan,:) = [false abs(diff(EEG.data(iChan,:)))<flatThresh false]; % false 1st and last sample to preserve original size
> % mask(iChan,:) = [false isoutlier(abs(diff(EEG.data(iChan,:))),'mean') false]; % this can detect EEG artifacts
> end
> if nChan == 4
> mask_front = or(mask(2,:),mask(3,:));
> mask_post = or(mask(1,:),mask(4,:));
> mask = or(mask_front, mask_post);
> elseif nChan == 3
> flat_mask_tmp = or(mask(1,:),mask(2,:));
> mask = or(mask(3,:),flat_mask_tmp);
> elseif nChan == 2
> mask = or(mask(1,:),mask(2,:));
> end
> mask(end) = [];
>
> % Remove them if present
> if sum(mask) > 0
> flatSeg = reshape(find(diff([false mask false])),2,[])';
> flatSeg(:,2) = flatSeg(:,2)-1;
>
> % Exclude flat segments shorter than 5 samples
> smallIntervals = diff(flatSeg,[],2) <= 5;
> flatSeg(smallIntervals,:) = [];
>
> % Expand segments because edges that are progressively flat are missed
> if ~isempty(flatSeg)
> flatSeg(:,1) = flatSeg(:,1) - floor(EEG.srate/4);
> flatSeg(:,2) = flatSeg(:,2) + floor(EEG.srate/4);
> if flatSeg(1,1) < 0
> flatSeg(1,1) = 1;
> end
> end
>
> % Update flat mask for vis_artifacts
> mask = false(1,EEG.pnts);
> for iSeg = 1:size(flatSeg,1)
> lowBound = flatSeg(iSeg,1);
> highBound = flatSeg(iSeg,2);
> mask(lowBound:highBound) = true;
> end
>
> % Calculate total length and ratio removed (before removing them)
> flatSamples = sum(diff(flatSeg,[],2));
> flatSegRatio = round(flatSamples/EEG.pnts*100,1);
> % flatSegSec = round(flatSamples/EEG.srate,1);
>
> % Remove from data
> EEG = pop_select(EEG,'nopoint',flatSeg);
>
> % Visualize
> if vis
> EEG.etc.clean_sample_mask = ~mask;
> vis_artifacts(EEG,oriEEG);
> end
>
> else
> flatSegRatio = 0;
> % flatSegSec = 0;
> end
>
> % Clear mask to avoid interference with ASR later
> if isfield(EEG.etc,'clean_sample_mask')
> EEG.etc = rmfield(EEG.etc,'clean_sample_mask');
> end
> EEG = eeg_checkset(EEG);
>
> % Print what was removed
> % fprintf("%g %% were flat segments that were removed (%g min). \n", flatSegRatio, flatSegSec/60)
> fprintf("%g %% were flat segments that were removed. \n", flatSegRatio)
>
>
>
>
>
>
> On Wednesday, August 7th, 2024 at 2:22 AM, Marco Buiatti via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
>
>> Hi Efthymios,
>>
>> I think it would be feasible to avoid the removal of flat lines in NEAR. I
>> put you in contact with Velu Kumaravel (cc), the developer of NEAR, maybe
>> he can help you.
>>
>> Best,
>>
>> Marco
>>
>> On Mon, 5 Aug 2024 at 11:09, Efthymios Papatzikis efp331 at mail.harvard.edu
>>
>> wrote:
>>
>>> Dear Marco,
>>>
>>> Thank you for this email. My inquiry still stands indeed, although I have
>>> managed since then to create an analysis and statistical preprocessing
>>> configuration of steps for my specific needs.
>>>
>>> Thank you for sharing your pipeline, though. Very informative and
>>> particularly the ASR configuration (TuneASR) part. I will definitely study
>>> this as it will provide me valuable input. However, I see your pipeline
>>> does exactly what I am trying to avoid; that is, reject flat lines instead
>>> of accounting for them in the preprocessing pipeline. This is a main
>>> characteristic (i.e., continuity/discontinuity ratio) of premature
>>> infants that I am working with in my project, and all along, from the very
>>> beginning, I was trying to find a pipeline pre-configured for that. I
>>> couldn’t, so I tried to create mine. Still needs some fine-tuning of
>>> course, but now it seems to do what is meant for.
>>>
>>> Happy to discuss more if you are interested.
>>>
>>> Best,
>>> Efthymios
>>>
>>> On 5 Aug 2024, at 10:47, Marco Buiatti marco.buiatti at unitn.it wrote:
>>>
>>> Dear Efthymios,
>>>
>>> If your request is still ongoing, I suggest you to also consider NEAR, our
>>> pipeline for artifact removal in newborn and infant EEG data, available as
>>> an EEGLAB plugin:
>>> https://urldefense.com/v3/__https://github.com/vpKumaravel/NEAR__;!!Mih3wA!D57UmdQYinCHOCnNcTXyb5ed0ro9PGfYVitKTCNZMOxsrMzHF4nV8z_xZrPuVwvGXsXm0AarWKpzM6vc4U4rkhRaFT3hO6A$
>>>
>>> Best,
>>>
>>> Marco
>>>
>>> On Tue, 19 Mar 2024 at 22:41, Dr. Efthymios Papatzikis via eeglablist <
>>> eeglablist at sccn.ucsd.edu> wrote:
>>>
>>>> Dear List,
>>>>
>>>> Is there anyone who has experience with EEGLab on data from premature
>>>> infants, and can suggest a preprocessing pipeline or relevant published
>>>> articles where EEGLab tools have been used to preprocess the data?
>>>>
>>>> Any suggestions more than welcome.
>>>>
>>>> Dr. Efthymios Papatzikis
>>>> Infant Brain Development
>>>> Oslo Metropolitan University, Norway
>>>>
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>>>
>>> --
>>> Marco Buiatti
>>>
>>> Baby Lab & Neonatal Neuroimaging Unit
>>> Center for Mind/Brain Sciences
>>> University of Trento,
>>> Piazza della Manifattura 1, 38068 Rovereto (TN), Italy
>>> E-mail: marco.buiatti at unitn.it
>>> Phone: +39 0464-808178
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>>> ***********************************************
>>
>>
>> --
>> Marco Buiatti
>>
>> Baby Lab & Neonatal Neuroimaging Unit
>> Center for Mind/Brain Sciences
>> University of Trento,
>> Piazza della Manifattura 1, 38068 Rovereto (TN), Italy
>> E-mail: marco.buiatti at unitn.it
>> Phone: +39 0464-808178
>> https://urldefense.com/v3/__https://sites.google.com/a/unitn.it/marcobuiatti/__;!!Mih3wA!D57UmdQYinCHOCnNcTXyb5ed0ro9PGfYVitKTCNZMOxsrMzHF4nV8z_xZrPuVwvGXsXm0AarWKpzM6vc4U4rkhRau70QE1E$
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>> ***********************************************
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