Note
Go to the end 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)
/home/runner/work/moabb/moabb/moabb/datasets/fake.py:93: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_FAKEDATASET-IMAGERY-10-2--60-60--120-120--FAKE1-FAKE2-FAKE3--C3-CZ-C4_PATH"
set_config(key, temp_dir)
/home/runner/work/moabb/moabb/moabb/datasets/fake.py:93: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_FAKEVIRTUALREALITYDATASET-P300-21-1--60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60--120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120--TARGET-NONTARGET--C3-CZ-C4_PATH"
set_config(key, temp_dir)
[<moabb.datasets.bnci.BNCI2014_001 object at 0x7f8e5a6f8a90>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f8e5a6f9fc0>, <moabb.datasets.beetl.Beetl2021_A object at 0x7f8e5a6fb070>, <moabb.datasets.beetl.Beetl2021_B object at 0x7f8e5a6f8760>, <moabb.datasets.gigadb.Cho2017 object at 0x7f8e5a6f9e40>, <moabb.datasets.dreyer2023.Dreyer2023 object at 0x7f8e5a6f8ca0>, <moabb.datasets.dreyer2023.Dreyer2023A object at 0x7f8e5a6f87c0>, <moabb.datasets.dreyer2023.Dreyer2023B object at 0x7f8e5a6f92d0>, <moabb.datasets.dreyer2023.Dreyer2023C object at 0x7f8e5a6f8340>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f8e5a6f8280>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f8e5a6fa920>, <moabb.datasets.liu2024.Liu2024 object at 0x7f8e5a6f8fd0>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f8e5a6f86a0>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f8e5a7a42b0>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f8e5a6faaa0>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f8e5a7a5300>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f8e5a7a5390>, <moabb.datasets.Zhou2016.Zhou2016 object at 0x7f8e5a7a5e40>]
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 0x7f8e5a6faf80>, <moabb.datasets.bnci.BNCI2014_001 object at 0x7f8e5a6f81c0>, <moabb.datasets.bnci.BNCI2014_002 object at 0x7f8e649af460>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f8e649aed40>, <moabb.datasets.bnci.BNCI2015_001 object at 0x7f8e649af4c0>, <moabb.datasets.bnci.BNCI2015_004 object at 0x7f8e649af4f0>, <moabb.datasets.gigadb.Cho2017 object at 0x7f8e649af430>, <moabb.datasets.dreyer2023.Dreyer2023 object at 0x7f8e649af850>, <moabb.datasets.dreyer2023.Dreyer2023A object at 0x7f8e649af490>, <moabb.datasets.dreyer2023.Dreyer2023B object at 0x7f8e649af0a0>, <moabb.datasets.dreyer2023.Dreyer2023C object at 0x7f8e649af610>, <moabb.datasets.fake.FakeDataset object at 0x7f8e5a7a6e60>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f8e5a7a6a10>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f8e5a7a5e40>, <moabb.datasets.liu2024.Liu2024 object at 0x7f8e5a7a5300>, <moabb.datasets.upper_limb.Ofner2017 object at 0x7f8e5a7a5450>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f8e5a7a5390>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f8e5a7a4760>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f8e5a7a42b0>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f8e5a7a49a0>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f8e5a7a45e0>]
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)
/home/runner/work/moabb/moabb/moabb/analysis/results.py:93: RuntimeWarning: Setting non-standard config type: "MOABB_RESULTS"
set_config("MOABB_RESULTS", osp.join(osp.expanduser("~"), "mne_data"))
2025-03-28 10:23:34,079 INFO MainThread moabb.evaluations.base Processing dataset: BNCI2014-001
BNCI2014-001-CrossSession: 0%| | 0/2 [00:00<?, ?it/s]MNE_DATA is not already configured. It will be set to default location in the home directory - /home/runner/mne_data
All datasets will be downloaded to this location, if anything is already downloaded, please move manually to this location
/home/runner/work/moabb/moabb/moabb/datasets/download.py:57: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_BNCI_PATH"
set_config(key, get_config("MNE_DATA"))
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:278: UserWarning: warnEpochs <Epochs | 24 events (all good), 2 – 6 s (baseline off), ~4.1 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, data loaded,
'left_hand': 12
'right_hand': 12>
warn(f"warnEpochs {epochs}")
2025-03-28 10:23:38,432 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 1 | 0train: Score 0.786
2025-03-28 10:23:38,585 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 1 | 1test: Score 0.802
2025-03-28 10:23:38,861 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 1 | 0train: Score 0.797
2025-03-28 10:23:39,145 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 1 | 1test: Score 0.774
BNCI2014-001-CrossSession: 50%|█████ | 1/2 [00:05<00:05, 5.21s/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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, 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 MiB, data loaded,
'left_hand': 12
'right_hand': 12>
warn(f"warnEpochs {epochs}")
2025-03-28 10:23:43,540 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 2 | 0train: Score 0.577
2025-03-28 10:23:43,690 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 2 | 1test: Score 0.499
2025-03-28 10:23:44,075 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 2 | 0train: Score 0.551
2025-03-28 10:23:44,387 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 2 | 1test: Score 0.471
BNCI2014-001-CrossSession: 100%|██████████| 2/2 [00:10<00:00, 5.23s/it]
BNCI2014-001-CrossSession: 100%|██████████| 2/2 [00:10<00:00, 5.22s/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 0.786458 0.026386 144.0 ... 2 BNCI2014-001 AM+LDA
1 0.801698 0.024031 144.0 ... 2 BNCI2014-001 AM+LDA
2 0.576582 0.021178 144.0 ... 2 BNCI2014-001 AM+LDA
3 0.498650 0.021181 144.0 ... 2 BNCI2014-001 AM+LDA
4 0.797068 0.144919 144.0 ... 2 BNCI2014-001 AM+SVM
[5 rows x 9 columns]
Total running time of the script: (0 minutes 11.747 seconds)
Estimated memory usage: 689 MB