Utility for testing EEG data-cleaning pipelines?
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- Dominant language
- Python
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Description
Hi there,
Just stumbled upon this project: looks super-useful for reproducible science! I'm currently a collaborator on PyPREP (an MNE-Python reimplementation of the MATLAB PREP pipeline), and am wondering how useful moabb would be for evaluating generalized (i.e. BCI-unrelated) EEG preprocessing pipelines?
It would be highly useful for us to have a tool that benchmarks how well a given filtering method or noisy channel detection method improves a dataset's SNR, and it seems like comparing the effects of our preprocessing on BCI classification accuracy would be a good way of doing that. Is this kind of workflow something moabb is designed to support?
Thanks in advance!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
No file, test, or entry point is named. First clarify whether moabb should support benchmarking generalized EEG preprocessing, including filtering and noisy-channel methods evaluated through SNR or BCI classification; done means an agreed workflow and scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100