mne-tools / mne-tools/mne-python

Some limitations of EpochsTFRs: creating via raws, concatenating

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Description

Describe the new feature or enhancement

My pipeline involves cropping raws into blocks of trials, time-frequency transforming these cropped raws, then epoching them into trials. Transforming raws rather than epochs is desirable as it avoids creating edge artifacts around every trial. Under this workflow, it would be convenient if EpochsTFRArray could accept RawTFR objects and epoch them, as Epochs can do with Raws.

Another limitation of working with EpochsTFRs is concatenation. I've constructed EpochsTFRs from multiple different blocks/sessions and would like to concatenate them. I had expected concatenate_epochs to accept EpochsTFR but unfortunately it does not. see also https://mne.discourse.group/t/concatenating-epochstfr-objects/8463

Describe your proposed implementation

Either modification of concatenate_epochs and EpochsTFRArray, or new functions/classes analogous to time-domain counterparts.

Describe possible alternatives

As a work-around, I am flattening the time-frequency data arrays, concatenating channels and frequencies, then working with them as Epochs objects (epoching, concatenating). But the Epochs class is ill suited for handling time-frequency data.

Additional context

No response

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First steps

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  3. Fork the repository and make your change on a branch.
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Research direction

Start by reading the existing EpochsTFRArray, RawTFR, Epochs, and concatenate_epochs APIs and their documentation. Compare how time-domain epoching and concatenation handle their inputs, then determine whether the requested RawTFR epoching and EpochsTFR concatenation should extend existing functions or use new counterparts. Done means both workflows are supported consistently with the relevant existing APIs.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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