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 0x7f5ee8197c70>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f5ee81974c0>, <moabb.datasets.gigadb.Cho2017 object at 0x7f5ee8197d90>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f5ef1084fd0>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f5ee8197520>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f5ee81970a0>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f5ee8197790>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f5ee81973a0>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f5ee8197d60>, <moabb.datasets.Zhou2016.Zhou2016 object at 0x7f5ee8197fa0>]
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 0x7f5ee8197670>, <moabb.datasets.bnci.BNCI2014_001 object at 0x7f5ee8197c70>, <moabb.datasets.bnci.BNCI2014_002 object at 0x7f5ee8197d90>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f5ee81971f0>, <moabb.datasets.bnci.BNCI2015_001 object at 0x7f5ee8197cd0>, <moabb.datasets.bnci.BNCI2015_004 object at 0x7f5ee8197850>, <moabb.datasets.gigadb.Cho2017 object at 0x7f5ee8197370>, <moabb.datasets.fake.FakeDataset object at 0x7f5ee8197d00>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f5ee8197dc0>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f5ee8197a30>, <moabb.datasets.upper_limb.Ofner2017 object at 0x7f5ee8197250>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f5ee8197280>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f5ee81979d0>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f5ee8197580>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f5ee8197d30>]
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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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, 3.44s/it]/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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:273: 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, 3.36s/it]
BNCI2014-001-CrossSession: 100%|##########| 2/2 [00:06<00:00, 3.37s/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 0.148544 144.0 ... 2 BNCI2014-001 AM+SVM
1 0.773920 0.140720 144.0 ... 2 BNCI2014-001 AM+SVM
2 0.550733 0.249333 144.0 ... 2 BNCI2014-001 AM+SVM
3 0.471451 0.181071 144.0 ... 2 BNCI2014-001 AM+SVM
4 0.786458 0.025422 144.0 ... 2 BNCI2014-001 AM+LDA
[5 rows x 9 columns]
Total running time of the script: ( 0 minutes 10.253 seconds)
Estimated memory usage: 181 MB