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NeuroTechX
Mother of All BCI Benchmarks
Currently, the online documentation is created from the `develop` branch only. We should be able to switch between the different versions plus the `develop` branch (like with brain decode)
Implementing a paradigm that allows doing a "cropped" epoching (in the sense of braindecode: https://braindecode.org/stable/auto_examples/plot_bcic_iv_2a_moabb_cropped.html) would be relatively simple with the new modular paradigms. We would just need to implement...
In the multi-classes evaluations, it would be nice to also get the confusion matrices in the results.
After discussions at the braindecode code sprint and following up on #460, I think we should break down the evaluations into something like that: ```python class BaseEvaluation: def __init__( self,...
Hello, At the moment, split of the data into train and test sets is performed via the StratifiedKFold cross-validation, so that the distribution of the classes within the test and...
This PR proposes train validation split via StratifiedKFold to ensure balanced distribution of labels into training and validation subsets. See issue #474 .
Closes #419 Instead of waiting for the evaluation of a whole dataset to finish before saving the results, we could save them as soon as they are computed. Beneficial in...
Since PR #408, we theoretically have the possibility to return non-loaded (i.e. `preload=False`) epochs and raws from the cache. However, because the paradigms' `get_data` concatenates epochs, they are loaded anyway...