Note
Click here to download the full example code
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
moabb.set_log_level("info")
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
print(LeftRightImagery().datasets)
[<moabb.datasets.bnci.BNCI2014_001 object at 0x7fa2837da220>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7fa2837dae80>, <moabb.datasets.gigadb.Cho2017 object at 0x7fa282ea2ee0>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7fa26e2dec10>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7fa26c6640d0>, <moabb.datasets.liu2024.Liu2024 object at 0x7fa28e0aaee0>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7fa28e214af0>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7fa26e0a9ee0>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7fa282ea2610>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7fa28e214070>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7fa2bcae2340>, <moabb.datasets.Zhou2016.Zhou2016 object at 0x7fa2776712e0>]
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 0x7fa2770deb20>, <moabb.datasets.bnci.BNCI2014_001 object at 0x7fa26e1c4910>, <moabb.datasets.bnci.BNCI2014_002 object at 0x7fa280aa9fd0>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7fa2835679a0>, <moabb.datasets.bnci.BNCI2015_001 object at 0x7fa26c7a8a00>, <moabb.datasets.bnci.BNCI2015_004 object at 0x7fa2bcae2250>, <moabb.datasets.gigadb.Cho2017 object at 0x7fa2bcae2460>, <moabb.datasets.fake.FakeDataset object at 0x7fa2bcae2340>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7fa28e27a9a0>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7fa26f513a90>, <moabb.datasets.liu2024.Liu2024 object at 0x7fa2837dac70>, <moabb.datasets.upper_limb.Ofner2017 object at 0x7fa28dc9f820>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7fa2837da220>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7fa282ea2610>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7fa2837dae80>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7fa282ea2ee0>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7fa2997a3e80>]
Or you can simply make your own list (which we do here due to computational constraints)
dataset = BNCI2014_001()
dataset.subject_list = dataset.subject_list[:2]
datasets = [dataset]
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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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 [00:05<00:05, 5.41s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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:278: 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 [00:10<00:00, 5.36s/it]
BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:10<00:00, 5.37s/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 0.147117 144.0 ... 2 BNCI2014-001 AM+SVM
1 0.773920 0.149183 144.0 ... 2 BNCI2014-001 AM+SVM
2 0.550733 0.249927 144.0 ... 2 BNCI2014-001 AM+SVM
3 0.471451 0.168082 144.0 ... 2 BNCI2014-001 AM+SVM
4 0.786458 0.027950 144.0 ... 2 BNCI2014-001 AM+LDA
[5 rows x 9 columns]
Total running time of the script: ( 0 minutes 21.013 seconds)
Estimated memory usage: 384 MB