Tutorial 0: Getting Started#

This tutorial takes you through a basic working example of how to use this codebase, including all the different components, up to the results generation. If you’d like to know about the statistics and plotting, see the next tutorial.

# Authors: Vinay Jayaram <vinayjayaram13@gmail.com>
#
# License: BSD (3-clause)

Introduction#

To use the codebase you need an evaluation and a paradigm, some algorithms, and a list of datasets to run it all on. You can find those in the following submodules; detailed tutorials are given for each of them.

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC

If you would like to specify the logging level when it is running, you can use the standard python logging commands through the top-level moabb module

import moabb
from moabb.datasets import BNCI2014_001, utils
from moabb.evaluations import CrossSessionEvaluation
from moabb.paradigms import LeftRightImagery
from moabb.pipelines.features import LogVariance

In order to create pipelines within a script, you will likely need at least the make_pipeline function. They can also be specified via a .yml file. Here we will make a couple pipelines just for convenience

Create pipelines#

We create two pipelines: channel-wise log variance followed by LDA, and channel-wise log variance followed by a cross-validated SVM (note that a cross-validation via scikit-learn cannot be described in a .yml file). For later in the process, the pipelines need to be in a dictionary where the key is the name of the pipeline and the value is the Pipeline object

pipelines = {}
pipelines["AM+LDA"] = make_pipeline(LogVariance(), LDA())
parameters = {"C": np.logspace(-2, 2, 10)}
clf = GridSearchCV(SVC(kernel="linear"), parameters)
pipe = make_pipeline(LogVariance(), clf)

pipelines["AM+SVM"] = pipe

Datasets#

Datasets can be specified in many ways: Each paradigm has a property ‘datasets’ which returns the datasets that are appropriate for that paradigm

[<moabb.datasets.bnci.BNCI2014_001 object at 0x7f58e97b5f40>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f5963f02820>, <moabb.datasets.gigadb.Cho2017 object at 0x7f58e45d5250>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f58dde47b50>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f58e0b85760>, <moabb.datasets.liu2024.Liu2024 object at 0x7f58e0b85340>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f58e0b2f760>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f58de2dda00>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f58e5a9b700>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f58e0b2f970>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f58ddb746d0>, <moabb.datasets.Zhou2016.Zhou2016 object at 0x7f58e81e7eb0>]

Or you can run a search through the available datasets:

print(utils.dataset_search(paradigm="imagery", min_subjects=6))
[<moabb.datasets.alex_mi.AlexMI object at 0x7f58e041ba90>, <moabb.datasets.bnci.BNCI2014_001 object at 0x7f58e30c4970>, <moabb.datasets.bnci.BNCI2014_002 object at 0x7f58e03fbb50>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f58e40b02b0>, <moabb.datasets.bnci.BNCI2015_001 object at 0x7f58e46ae9d0>, <moabb.datasets.bnci.BNCI2015_004 object at 0x7f58e797be50>, <moabb.datasets.gigadb.Cho2017 object at 0x7f58e7c89760>, <moabb.datasets.fake.FakeDataset object at 0x7f58e797b490>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f58e0b68280>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f58df45b550>, <moabb.datasets.liu2024.Liu2024 object at 0x7f58df45b340>, <moabb.datasets.upper_limb.Ofner2017 object at 0x7f58f1964340>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f58dba5c310>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f58f0dcca60>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f58ecf5be50>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f58ef0127c0>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f58e0d19580>]

Or you can simply make your own list (which we do here due to computational constraints)

Paradigm#

Paradigms define the events, epoch time, bandpass, and other preprocessing parameters. They have defaults that you can read in the documentation, or you can simply set them as we do here. A single paradigm defines a method for going from continuous data to trial data of a fixed size. To learn more look at the tutorial Exploring Paradigms

Evaluation#

An evaluation defines how the training and test sets are chosen. This could be cross-validated within a single recording, or across days, or sessions, or subjects. This also is the correct place to specify multiple threads.

evaluation = CrossSessionEvaluation(
    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=False
)
results = evaluation.process(pipelines)
BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00<?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")

BNCI2014-001-CrossSession:  50%|#####     | 1/2 [00:03<00:03,  3.50s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:279: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")

BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:06<00:00,  3.45s/it]
BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:06<00:00,  3.45s/it]

Results are returned as a pandas DataFrame, and from here you can do as you want with them

print(results.head())
      score      time  samples  ... n_sessions       dataset  pipeline
0  0.797068  0.147747    144.0  ...          2  BNCI2014-001    AM+SVM
1  0.773920  0.145328    144.0  ...          2  BNCI2014-001    AM+SVM
2  0.550733  0.249766    144.0  ...          2  BNCI2014-001    AM+SVM
3  0.471451  0.175482    144.0  ...          2  BNCI2014-001    AM+SVM
4  0.786458  0.021679    144.0  ...          2  BNCI2014-001    AM+LDA

[5 rows x 9 columns]

Total running time of the script: ( 0 minutes 17.578 seconds)

Estimated memory usage: 289 MB

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