BNCI2015003 Subject 1 and 2 imbalanced class ratio
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dataset
moabb
- Dominant language
- Python
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- Merged PRs (30d)
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Description
In the following plots, the title gives information on how many Target (T) and Nontarget (NT) Epochs are available per subject.
Subject 1
Subject 2
This is how it SHOULD be (as described in the paper / documentation). Note the 1:5 Target/NonTarget ratio
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 or test is named. Start by locating the BNCI2015003 dataset-loading entry point and compare the epoch counts for Subjects 1 and 2 with the paper or documentation; done means the Target/Nontarget counts match the documented 1:5 ratio.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100