Advanced examples

These examples shows various advanced topics:

  • using scikit-learn pipeline with MNE inputs

  • selecting electrodes or resampling signal

  • using filterbank approach in motor imagery

  • apply statistics for meta-analysis

External examples

You need to install external dependencies to run these examples. These consist mostly of various classifier implementations. When using poetry, you can use

poetry install --extras external

Evaluation with learning curve

These examples demonstrate how to make evaluations using only a subset of available example. For example, if you consider a dataset with 100 trials for each class, you could evaluate several pipelines by using only a fraction of these trials. To ensure the robustness of the results, you need to specify the number of permutations. If you use 10 trials per class and 20 permutations, each pipeline will be evaluated on a subset of 10 trials chosen randomly, that will be repeated 20 times with different trial subsets.

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