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 0x7f3431996680>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f3431994040>, <moabb.datasets.beetl.Beetl2021_A object at 0x7f3431997d30>, <moabb.datasets.beetl.Beetl2021_B object at 0x7f34319964a0>, <moabb.datasets.gigadb.Cho2017 object at 0x7f3431997dc0>, <moabb.datasets.dreyer2023.Dreyer2023 object at 0x7f342ff935e0>, <moabb.datasets.dreyer2023.Dreyer2023A object at 0x7f3431997640>, <moabb.datasets.dreyer2023.Dreyer2023B object at 0x7f342ff907f0>, <moabb.datasets.dreyer2023.Dreyer2023C object at 0x7f342ff932e0>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f342fcba380>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f342fcb89a0>, <moabb.datasets.liu2024.Liu2024 object at 0x7f342ffdf400>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f342ffdc0a0>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f342ffdca90>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f342fcb9cc0>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f342ffdf6a0>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f342ffdcbb0>, <moabb.datasets.Zhou2016.Zhou2016 object at 0x7f342ff935b0>]

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 0x7f3430deba90>, <moabb.datasets.bnci.BNCI2014_001 object at 0x7f3430debf40>, <moabb.datasets.bnci.BNCI2014_002 object at 0x7f3430de9b40>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f3430de8040>, <moabb.datasets.bnci.BNCI2015_001 object at 0x7f3430de98a0>, <moabb.datasets.bnci.BNCI2015_004 object at 0x7f342ffdf400>, <moabb.datasets.gigadb.Cho2017 object at 0x7f342ffdc730>, <moabb.datasets.dreyer2023.Dreyer2023 object at 0x7f342ffdc820>, <moabb.datasets.dreyer2023.Dreyer2023A object at 0x7f342ffdca60>, <moabb.datasets.dreyer2023.Dreyer2023B object at 0x7f342ffdca90>, <moabb.datasets.dreyer2023.Dreyer2023C object at 0x7f342ffdcbb0>, <moabb.datasets.fake.FakeDataset object at 0x7f342ffdf6a0>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f342ffdc8b0>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f342ff935b0>, <moabb.datasets.liu2024.Liu2024 object at 0x7f342ff932e0>, <moabb.datasets.upper_limb.Ofner2017 object at 0x7f342ffdc0a0>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f342ff907f0>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f342ff93e20>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f342ff935e0>, <moabb.datasets.bbci_eeg_fnirs.Shin2017B object at 0x7f342ff93520>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f342ff91510>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f342ff91a20>]

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

fmin = 8
fmax = 35
paradigm = LeftRightImagery(fmin=fmin, fmax=fmax)

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"))
2025-04-18 13:58:26,596 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}")
2025-04-18 13:58:30,936 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 1 | 0train: Score 0.786
2025-04-18 13:58:31,085 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 1 | 1test: Score 0.802
2025-04-18 13:58:31,357 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 1 | 0train: Score 0.797
2025-04-18 13:58:31,639 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 1 | 1test: Score 0.774

BNCI2014-001-CrossSession:  50%|█████     | 1/2 [00:05<00:05,  5.18s/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}")
2025-04-18 13:58:36,000 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 2 | 0train: Score 0.577
2025-04-18 13:58:36,144 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 2 | 1test: Score 0.499
2025-04-18 13:58:36,515 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 2 | 0train: Score 0.551
2025-04-18 13:58:36,820 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 2 | 1test: Score 0.471

BNCI2014-001-CrossSession: 100%|██████████| 2/2 [00:10<00:00,  5.18s/it]
BNCI2014-001-CrossSession: 100%|██████████| 2/2 [00:10<00:00,  5.18s/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
0.786458  0.025549    144.0  ...          2  BNCI2014-001    AM+LDA
0.801698  0.025064    144.0  ...          2  BNCI2014-001    AM+LDA
0.576582  0.019451    144.0  ...          2  BNCI2014-001    AM+LDA
0.498650  0.018977    144.0  ...          2  BNCI2014-001    AM+LDA
0.797068  0.144297    144.0  ...          2  BNCI2014-001    AM+SVM

[5 rows x 9 columns]

Total running time of the script: (0 minutes 11.644 seconds)

Estimated memory usage: 690 MB

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