From Binary classification performance to BCI Application level
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Description
OT: Sorry for the flood of issues, I am just cleaning out my personal todo / issue log to the official one on Github :)
Currently moabb only allows the evaluation of binary classification performance, which is an important consideration for practical BCI usage, however there are application level metrics that could be interesting as well. E.g. for an ERP/P300 speller, you are mostly interested in correctly spelled letters.
However, enabling this requires more annotations of the epochs / ground truths etc. I have the necessary information for the Huebner datasets, and potentially Lee_ERP, but implementing this would be a larger task imo, so this issue is mostly a note for future work... :)
P.S.: Having sub-class information available (i.e. which letters were flashed for each epoch) could enable unsupervised learning / evaluations.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file, test, or entry point is identified. Start by reviewing how MOABB currently represents epoch annotations and binary performance for the Huebner datasets and potentially Lee_ERP. Done would include application-level metrics such as correctly spelled letters and the needed subclass ground truths.
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
- 25/100