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In some Confluence pages, you can find panel notes in which some ideas of App improvements are listed.

Define an EEG datatype

All Apps use the meg/fif datatype when they directly preprocess the file containing the signals. But some app can preprocess both EEG and MEG data (app-notch-filtering, app-resampling, app-temporal-filtering, app-get-events, and app-make-epochs).

Files in fif format can contain both MEG and EEG data: to select EEG, you have to specify it with the parameter param_picks_by_channel_types_or_names (except for the app-get-events, a MNE function pick() may be added to that App).

But for now no EEG datatype we can use exists, so a discussion about EEG datatype is to needed. The apps app-notch-filtering, app-resampling, app-temporal-filtering, app-get-events, and app-make-epochs run correctly with EEG data stored in a fif file, so maybe it would be interesting to create a eeg/fif datatype. However fif is a format that is not usually used for EEG…

In helper.py a new function dealing with optional files from eeg/fif datatype should be created and used if param_picks_by_channel_types_or_names = 'eeg' instead of the one called when the meg/fif datatype is used (see Create a helper.py file).

HTML reports

The structure of the report needs to be discussed. It’s important for the App user to be able to visualize easily the signals.

Ideas of improvements:

  • Let the user decide the parameters of the plots (the scaling especially) and the plots to display?

  • Add other plots like topoplots

  • For app-bad-channels, plot in red the channels that are detected as bad?

  • Create HTLM reports for other exsiting Apps (app-resampling for instance?)

Assess the quality of the correction

We looked for a value to quantify the efficacy of the correction. A SNR computation was found but has to be improved (see Assess the quality of artifact correction/rejection). The code to compute this SNR is available in several Apps (app-bad-channels, app-maxwell-filter, app-notch-filter, app-temporal-filtering) but it is commented.

The result of this assessment must appear in the HTML report.

Improve existing Apps

  • To see the improvements to make on each app, please check the Confluence page relative to each App (Description of existing Apps).

  • For each App, make sure that error of info messages are clear and that the description of the parameters (in the docstring of the Python file, in the README.md, and on Brainlife) are consistent between each other and understandable.

  • Make sure that the default values of the Apps' parameters correspond to the values of MNE Python 0.23.

  • Try to create a pipeline rule with these Apps to preprocess several subjects in a raw.

  • Add Brainlife tag to output data (such as “epoched”, “filtered”).

  • In the Python code, parts that are no be tested are commented # to be tested.

  • See if the descriptions of the App parameters are consistent in README.md, the doctrings, and in BL

Next Apps to create

  • An app to detect bad channels automatically but not using Maxwell filter, so that it can be applied on .ctf and EEG files

  • An app to re-reference EEG channels

  • An app to automatically detect bad epochs

  • An App to apply ICA

  • Duplicate some of existing apps (apps that are not Elekta specific) so they can be applied to ctf files for example?

MNE Python was not designed at first to process EEG data, so their ICA can present some flaws. Maybe these Apps can used other toolboxes like FieldTrip for instance.

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