Examples#
These examples demonstrate how to use MOABB, and its main concepts the
dataset, paradigm and evaluation. Those examples are using
only a small number of subjects, and a small number of sessions, to
keep the execution time short. In practice, you should use all the
subjects and sessions available in the dataset.
sphx_glr_auto_examples_noplot_vr_pc_p300_different_epoch_size.py
Examples of how to use MOABB to benchmark pipelines.
Benchmarking on MOABB with Tensorflow deep net architectures
Benchmarking on MOABB with Braindecode (PyTorch) deep net architectures
Cross-session motor imagery with deep learning EEGNet v4 model
Advanced examples#
These examples show 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
using a gridsearch in within-subject decoding
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.
